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14 pages, 1001 KiB  
Case Report
Decoding Chemotherapy Resistance of Undifferentiated Pleomorphic Sarcoma at the Single Cell Resolution: A Case Report
by Timur I. Fetisov, Maxim E. Menyailo, Alexander V. Ikonnikov, Anna A. Khozyainova, Anastasia A. Tararykova, Elena E. Kopantseva, Anastasia A. Korobeynikova, Maria A. Senchenko, Ustinia A. Bokova, Kirill I. Kirsanov, Marianna G. Yakubovskaya and Evgeny V. Denisov
J. Clin. Med. 2024, 13(23), 7176; https://doi.org/10.3390/jcm13237176 - 26 Nov 2024
Viewed by 273
Abstract
Background: Undifferentiated pleomorphic sarcoma (UPS) is a highly malignant mesenchymal tumor that ranks as one of the most common types of soft tissue sarcoma. Even though chemotherapy increases the 5-year survival rate in UPS, high tumor heterogeneity frequently leads to chemotherapy resistance and [...] Read more.
Background: Undifferentiated pleomorphic sarcoma (UPS) is a highly malignant mesenchymal tumor that ranks as one of the most common types of soft tissue sarcoma. Even though chemotherapy increases the 5-year survival rate in UPS, high tumor heterogeneity frequently leads to chemotherapy resistance and consequently to recurrences. In this study, we characterized the cell composition and the transcriptional profile of UPS with resistance to chemotherapy at the single cell resolution. Methods: A 58-year-old woman was diagnosed with a 13.6 × 9.3 × 6.0 cm multi-nodular tumor with heterogeneous cysto-solid structure at the level of the distal metadiaphysis of the left thigh during magnetic resonance tomography. Morphological and immunohistochemical analysis led to the diagnosis of high-grade (G3) UPS. Neoadjuvant chemotherapy, surgery (negative resection margins), and adjuvant chemotherapy were conducted, but tumor recurrence developed. The UPS sample was used to perform single-cell RNA sequencing by chromium-fixed RNA profiling. Results: Four subpopulations of tumor cells and seven subpopulations of tumor microenvironment (TME) have been identified in UPS. The expression of chemoresistance genes has been detected, including KLF4 (doxorubicin and ifosfamide), ULK1, LUM, GPNMB, and CAVIN1 (doxorubicin), and AHNAK2 (gemcitabine) in tumor cells and ETS1 (gemcitabine) in TME. Conclusions: This study provides the first description of the single-cell transcriptome of UPS with resistance to two lines of chemotherapy, showcasing the gene expression in subpopulations of tumor cells and TME, which may be potential markers for personalized cancer therapy. Full article
(This article belongs to the Section Oncology)
20 pages, 982 KiB  
Review
Correlation Between Antihypertensive Drugs and Survival Among Patients with Pancreatic Ductal Adenocarcinoma
by Natalia Kluz, Leszek Kraj, Paulina Chmiel, Adam M. Przybyłkowski, Lucjan Wyrwicz, Rafał Stec and Łukasz Szymański
Cancers 2024, 16(23), 3945; https://doi.org/10.3390/cancers16233945 - 25 Nov 2024
Viewed by 333
Abstract
There is a growing prevalence of pancreatic cancer, accompanied by accelerated disease progression and diminished survival rates. Radical resection with clear margins remains the sole viable option for achieving a long-term cure in patients. In cases of advanced, unresectable, and metastatic disease, chemotherapy [...] Read more.
There is a growing prevalence of pancreatic cancer, accompanied by accelerated disease progression and diminished survival rates. Radical resection with clear margins remains the sole viable option for achieving a long-term cure in patients. In cases of advanced, unresectable, and metastatic disease, chemotherapy based on leucovorin, 5-fluorouracil, irinotecan, oxaliplatin, gemcitabine, or nab-paclitaxel represents the cornerstone of the treatment. Considering the limited treatment options available following initial therapy, the strategy of repurposing commonly prescribed drugs such as antihypertensives into anti-cancer therapies in palliative treatment represents a promising avenue for enhancing survival in patients with pancreatic ductal adenocarcinoma. The repurposing of existing drugs is typically a more cost-effective and expedient strategy than the development of new ones. The potential for antihypertensive drugs to be employed as adjunctive therapies could facilitate a more comprehensive treatment approach by targeting multiple pathways involved in cancer progression and acquired resistance to treatment. Antihypertensive medications, particularly those belonging to the pharmacological classes of angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, and calcium channel blockers, are commonly prescribed and have well-established safety profiles, particularly among patients with pancreatic cancer who are affected by multiple comorbidities. Therefore, we emphasize the preclinical and clinical evidence supporting the use of antihypertensive agents in the treatment of pancreatic cancer, emphasizing their beneficial chemosensitizing effects. Full article
(This article belongs to the Special Issue Advanced Research in Pancreatic Ductal Adenocarcinoma)
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Figure 1
<p>Antihypertensive drugs in PDAC: antitumoral mechanisms. In this figure, we summarize potential mechanisms through which antihypertensive drugs may aid chemotherapy through different cellular effects. Ag II, angiotensin II; HA, hyaluronic acid; ACEI, angiotensin-converting enzyme inhibitors; ARB, angiotensin I receptor blocker; CCB, calcium channel blocker; β2AR, β2-adrenergic receptor; BBs, beta-blockers. Created in BioRender.com (accessed on 20 November 2024).</p>
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19 pages, 4763 KiB  
Article
Altered Mechanobiology of PDAC Cells with Acquired Chemoresistance to Gemcitabine and Paclitaxel
by Alessandro Gregori, Cecilia Bergonzini, Mjriam Capula, Rick Rodrigues de Mercado, Erik H. J. Danen, Elisa Giovannetti and Thomas Schmidt
Cancers 2024, 16(22), 3863; https://doi.org/10.3390/cancers16223863 - 18 Nov 2024
Viewed by 549
Abstract
Background: Pancreatic ductal adenocarcinoma acquired resistance to chemotherapy poses a major limitation to patient survival. Despite understanding some biological mechanisms of chemoresistance, much about those mechanisms remains to be uncovered. Mechanobiology, which studies the physical properties of cells, holds promise as a [...] Read more.
Background: Pancreatic ductal adenocarcinoma acquired resistance to chemotherapy poses a major limitation to patient survival. Despite understanding some biological mechanisms of chemoresistance, much about those mechanisms remains to be uncovered. Mechanobiology, which studies the physical properties of cells, holds promise as a potential target for addressing the challenges of chemoresistance in PDAC. Therefore, we, here in an initial step, assessed the altered mechanobiology of PDAC cells with acquired chemoresistance to gemcitabine and paclitaxel. Methods: Five PDAC cell lines and six stably resistant subclones were assessed for force generation on elastic micropillar arrays. Those measurements of mechanical phenotype were complemented by single-cell motility and invasion in 3D collagen-based matrix assays. Further, the nuclear translocation of Yes-associated protein (YAP), as a measure of active mechanical status, was compared, and biomarkers of the epithelial-to-mesenchymal transition (EMT) were evaluated using RT-qPCR. Results: The PDAC cells with acquired chemoresistance exert higher traction forces than their parental/wild-type (WT) cells. In 2D, single-cell motility was altered for all the chemoresistant cells, with a cell-type specific pattern. In 3D, the spheroids of the chemoresistant PDAC cells were able to invade the matrix and remodel collagen more than their WT clones. However, YAP nuclear translocation and EMT were not significantly altered in relation to changes in other physical parameters. Conclusions: This is the first study to investigate and report on the altered mechanobiological features of PDAC cells that have acquired chemoresistance. A better understanding of mechanical features could help in identifying future targets to overcome chemoresistance in PDAC. Full article
(This article belongs to the Section Cancer Metastasis)
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Figure 1

Figure 1
<p>Workflow of the study. (<b>A</b>) PDAC cells were exposed to gemcitabine (GEM) or paclitaxel (PTX) to generate chemoresistant clones. (<b>B</b>) PDAC cells, and their resistant subclones, seeded on elastic micropillar arrays of varying stiffness were assessed for force generation by measuring the pillar deflections. Traction forces were defined as the inward-pointing forces. (<b>C</b>) Single-cell motility was assessed in cells seeded on collagen- and fibronectin-coated substrates. (<b>D</b>) The 3D collagen-embedded spheroid invasion and spheroid-induced ECM remodeling were analyzed. (<b>E</b>) YAP nuclear translocation assessed by immunofluorescence for cells growing on soft pillars. (<b>F</b>) Biomarkers of epithelial-to-mesenchymal transition (EMT) were assessed by RT-qPCR. Part of the figure was adapted from images made by Servier Medical Art by Servier, licensed under a Creative Commons Attribution 4.0 Unported License, at <a href="https://smart.servier.com" target="_blank">https://smart.servier.com</a>.</p>
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<p>PDAC cell spreading area varies with stiffness. (<b>A</b>) Representative confocal microscopy images of BxPC-3 cells (green) growing on fibronectin-coated pillars (red) of different stiffness. Scale bar: 10 µm (<b>B</b>) Boxplots of cell spreading area (μm<sup>2</sup>) for BxPC-3, CAPAN-1, HPDE, SUIT-2.028, and SUIT-2.007 growing on fibronectin-coated pillars (25th and 75th percentiles marked, line at median).</p>
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<p>PDAC cell traction forces increase with substrate stiffness, but not with metastatic potential. (<b>A</b>) Representative linear regression of the total forces (nN) vs. spreading area (μm<sup>2</sup>) of SUIT-2.028. All the linear regression models of the other cell lines are found in <a href="#app1-cancers-16-03863" class="html-app">Supplemental Figure S1</a>. In all the measurements, the regression coefficient was R<sup>2</sup> &gt; 0.45. (<b>B</b>) Representative confocal microscopy images of the traction forces of the BxPC-3 cells (green) growing on fibronectin-coated pillars (red). White arrows indicate cellular traction forces on the pillars. (<b>C</b>) Boxplots of the PDAC cell traction forces expressed as mean force per pillar (nN) (25th and 75th percentiles marked, line at median). Statistical significance was calculated using the softest condition (11 kPa) as the reference group. (<b>D</b>) Mean force per pillar (nN) of the PDAC cell lines of different phenotypes. The results from other stiffness values (11, 29, and 142 kPa) are shown in <a href="#app1-cancers-16-03863" class="html-app">Supplemental Figure S3</a> and pillar‘s background forces are shown in <a href="#app1-cancers-16-03863" class="html-app">Supplemental Figure S2</a>. Each dot of plots in (<b>A</b>,<b>C</b>,<b>D</b>) represents the result from one cell. (<b>C</b>,<b>D</b>) Statistical significance was set at <span class="html-italic">p</span> &lt; 0.05 and is indicated by ****, <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Chemoresistant PDAC cells apply higher traction forces. (<b>A</b>) Representative confocal microscopy images of the traction forces of SUIT-2.028 WT, GR, and PR cells growing on fibronectin-coated soft (11 kPa) and stiff (47 kPa) pillars. White arrows indicate traction forces that the cells applied to deflect the pillars. Nucleus is indicated by cyan color (DAPI) and cytoskeleton by green color (AlexaFluor568 Phalloidin). (<b>B</b>) Boxplots of PDAC cell traction forces on the soft and stiff pillars on the left and right, respectively, expressed as mean force per pillar (nN) (25th and 75th percentiles marked, and line at median). Statistical significance was calculated using the parental cells (WT) as the reference group. Each dot represents one cell analyzed. Statistical significance was set at <span class="html-italic">p</span> &lt; 0.05 and is indicated by *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001; ****, <span class="html-italic">p</span> &lt; 0.0001, “ns” means not significant.</p>
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<p>PDAC resistant single-cell migration is different from parental cells. PDAC single-cell mean velocity (μm/min), growing on (<b>A</b>) collagen-coated or (<b>D</b>) fibronectin-coated wells. The directionality of PDAC cell migration trajectories growing on collagen-coated wells, expressed as (<b>B</b>) diffusive fraction = DF and (<b>C</b>) diffusion constant. The directionality of PDAC cell migration trajectories growing on fibronectin-coated wells, expressed as (<b>E</b>) DF fraction and (<b>F</b>) diffusion constant. All the conditions are represented as boxplots with the smallest and largest values marked, and line at median. The statistical significance was calculated using the parental cells (WT) as the reference group. Each dot represents the population mean for one section of the well. (<b>A</b>–<b>F</b>) Statistical significance was set at <span class="html-italic">p</span> &lt; 0.05 and is indicated by *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001; ****, <span class="html-italic">p</span> &lt; 0.0001, “ns” means not significant.</p>
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<p>PDAC chemoresistant cell ability for 3D ECM remodeling and invasion. (<b>A</b>) Representative collagen fibers alignment (left columns), obtained by reflection microscopy, and actin (right columns) of PDAC chemoresistant spheroids. Scale bar: 200 µm (<b>B</b>) Polar plots representing the percentage of the frequency of the distribution of collagen fibers angles from 0° to 90°. Each bar represents the % of fibers in a sector of 5 degrees, expressed as the mean of 2 biological replicates, with at least 4 technical replicates. Lines of the darker shade of the bars represent the upper and lower bounds of SD of the % of collagen fibers in each sector. Orange lines represent the mean % of the frequency of the respective WT for each cell line to facilitate the comparison. (<b>C</b>) Relative area covered by spheroids after 2 days. Dots represent the value of individual spheroids. Data are expressed as mean ± SD. (<b>D</b>) Percentage of aligned fibers, defined as fibers comprised between the angles of 72.5 and 90, which means that those fibers are perpendicular to the closest point of the spheroid. Dots represent the value of individual spheroids. (<b>C</b>,<b>D</b>) Statistical significance was set at <span class="html-italic">p</span> &lt; 0.05 and is indicated by ****, <span class="html-italic">p</span> &lt; 0.0001. “ns” means not significant.</p>
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<p>PDAC chemoresistant cell differential mechanobiology does not rely on YAP nuclear translocation nor on EMT switch. (<b>A</b>) Relative gene expression of <span class="html-italic">E-cadherin</span>, <span class="html-italic">N-cadherin</span>, and <span class="html-italic">Vimentin</span> as assessed by RT-qPCR. Data are expressed as the mean ± SD of three independent experiments. (<b>B</b>) Representative confocal microscopy images of SUIT-2.028 cells growing on soft (11 kPa) pillars and stained with YAP. Scale bar: 10 µm (<b>C</b>) YAP nuclear translocation, expressed as % of nuclear YAP over total YAP in SUIT-2.028 and SUIT-2.007. (<b>D</b>) Linear regression model between the mean force per pillar vs. % of nuclear YAP in SUIT-2.007 (left panel) and SUIT-2.028 (right panel). Each dot in (<b>C</b>,<b>D</b>) represents one cell. *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001; ****, <span class="html-italic">p</span> &lt; 0.0001, “ns” means not significant.</p>
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<p>PDAC resistance leads to changes in the mechanobiological signatures of cells, the detailed characteristics of which yet depend on cell type and ECM dimensionality. Part of the figure was adapted from images made by Servier Medical Art by Servier, licensed under a Creative Commons Attribution 4.0 Unported License, at <a href="https://smart.servier.com" target="_blank">https://smart.servier.com</a>.</p>
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15 pages, 3020 KiB  
Article
Tumor-Colonizing E. coli Expressing Both Collagenase and Hyaluronidase Enhances Therapeutic Efficacy of Gemcitabine in Pancreatic Cancer Models
by Lara C. Avsharian, Suvithanandhini Loganathan, Nancy D. Ebelt, Azadeh F. Shalamzari, Itzel Rodarte Muñoz and Edwin R. Manuel
Biomolecules 2024, 14(11), 1458; https://doi.org/10.3390/biom14111458 - 17 Nov 2024
Viewed by 755
Abstract
Desmoplasia is a hallmark feature of pancreatic ductal adenocarcinoma (PDAC) that contributes significantly to treatment resistance. Approaches to enhance drug delivery into fibrotic PDAC tumors continue to be an important unmet need. In this study, we have engineered a tumor-colonizing E. coli-based [...] Read more.
Desmoplasia is a hallmark feature of pancreatic ductal adenocarcinoma (PDAC) that contributes significantly to treatment resistance. Approaches to enhance drug delivery into fibrotic PDAC tumors continue to be an important unmet need. In this study, we have engineered a tumor-colonizing E. coli-based agent that expresses both collagenase and hyaluronidase as a strategy to reduce desmoplasia and enhance the intratumoral perfusion of anticancer agents. Overall, we observed that the tandem expression of both these enzymes by tumor-colonizing E. coli resulted in the reduced presence of intratumoral collagen and hyaluronan, which likely contributed to the enhanced chemotherapeutic efficacy observed when used in combination. These results highlight the importance of combination treatments involving the depletion of desmoplastic components in PDAC before or during treatment. Full article
(This article belongs to the Special Issue Immune-Related Biomarkers: 2nd Edition)
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Figure 1
<p>Tandem construct design and expression analysis in BL21 <span class="html-italic">E. coli</span> transformants. (<b>A</b>) The synthesized sequence encoding for both bacterial collagenase (ColH) and hyaluronidase (HylB) was engineered with independent 5′ ribosomal binding sites (RBSs) and fused to a myc- or his-tag, respectively. The sequence was cloned downstream of the inducible pBAD promoter in the pBAD/HIS A plasmid and then transformed into BL21 to generate BL21-TAN. (<b>B</b>) BL21-TAN cultures were grown to an exponential phase (shaking at 37 °C) and then left alone (uninduced, U) or induced (I) at a final concentration of 0.02% L-arabinose for 4 h. Bacterial pellets and culture media (CM) were then subjected to western blot analysis to detect the expression of ColH (anti-Myc) and HylB (anti-His). Western blot original images can be found in <a href="#app1-biomolecules-14-01458" class="html-app">Supplementary Materials</a>. (<b>C</b>) BL21 transformed with a control pBAD-eGFP plasmid (BL21-eGFP), and BL21-TAN were cultured to an optical density (OD<sub>600</sub>) of ~1. Cultures were either left uninduced (BL21-eGFP, BL21-TAN) or induced at 0.02% L-arabinose (BL21-TAN), and OD<sub>600</sub> was measured over time. (<b>D</b>) Uninduced or induced BL21-eGFP (0.02% L-arabinose, 4 h) were fixed in 4% paraformaldehyde and then stained simultaneously with anti-myc and anti-his to detect ColH and HylB expression, respectively. A representative single bacterium for each condition is shown. <span class="html-italic">n.s</span>. = no significance. Error bars display standard error of the mean.</p>
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<p>In vitro degradation of collagens and hyaluronic acid by BL21-TAN. Hydrolysis reactions were performed using uninduced (U) or induced (I) BL21-TAN co-incubated with FITC-conjugated pig skin gelatin (<b>A</b>), bovine skin collagen type I (<b>B</b>), human placenta collagen type IV (<b>C</b>), or purified hyaluronic acid (HA) (<b>D</b>) in 50 mM Tris-HCl (pH 8.0) containing 10 mM CaCl<sub>2</sub> at 37 °C. The negative control includes the culture media (LB). Increases in fluorescence intensity signify the degradation of the FITC-conjugated target. Enzyme activity was measured by monitoring fluorescence (FITC) (ex: 495 nm, em: 519 nm). Data are expressed as mean ± error of the mean of three independent experiments. **** <span class="html-italic">p</span> &lt; 0.0001, <span class="html-italic">t</span>-test.</p>
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<p>BL21-TAN depletes PDAC-derived collagen and HA in serial tumor sections. Serial sections of KPC, BxPC3, and de-identified patient (UPN) PDAC tumors were treated overnight with BL21-TAN under uninduced or induced conditions at 37 °C. Sections were then stained by trichrome to detect collagen (blue). (<b>A</b>) or biotin-labeled hyaluronic acid binding protein (HABP) followed by streptavidin-FITC (<b>B</b>). Trichrome images were deconvoluted using ImageJ to quantify collagen content (blue staining) in randomly selected fields (10) of each tumor section (<b>C</b>). Fluorescence intensity was used to quantify HA content (FITC/488 channel) and was measured using ImageJ and normalized to uninduced treatment (<b>D</b>). Data are expressed as mean values ± error of the mean. ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001, <span class="html-italic">t</span>-test. Scale bar = 20 µm.</p>
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<p>Intravenously administered BL21-TAN colonizes PDAC tumors and expresses both ColH and HylB. Mice bearing subcutaneous KPC or BxPC3 tumors (6–8 mm diameter) were intravenously injected with 5 × 10<sup>7</sup> colony-forming units (CFUs) of BL21-TAN for three consecutive days. Twenty-four hours following the final injection, the mice were either administered PBS (uninduced, U) or 40 mg L-arabinose (induced, I) intraperitoneally. Tumors were collected 48 h post induction and sections were evaluated for BL21-TAN colonization and enzyme expression by immunofluorescence using antibodies specific to BL21 <span class="html-italic">E. coli</span>, Myc-tag (ColH) and His-tag (HylB). Objective: 100× oil. Scale bars = 10 µm.</p>
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<p>In vivo collagen and HA depletion by BL21-TAN. NSG mice with subcutaneous BxPC3 xenografts (6–8 mm diameter) were intravenously injected with 5 × 10<sup>7</sup> colony-forming units (CFUs) of the BL21-eGFP control or BL21-TAN for three consecutive days by the intravenous route. Twenty-four hours following the final injection, the mice given BL21-TAN were administered 40 mg L-arabinose intraperitoneally. Tumors (<span class="html-italic">n</span> = 8) were collected 48 h post induction and serial sections were evaluated for collagen (blue) using trichrome staining (<b>A</b>) and HA using HABP staining (<b>B</b>). Regions (box) from each image were magnified for greater resolution of collagen and HA (inset). Random fields (<span class="html-italic">n</span> = 15, 10× objective) from each treatment group were used for deconvolution analysis to quantify collagen and HA content in multiple tumors (<b>C</b>). Data are expressed as mean ± error of the mean. **** <span class="html-italic">p</span> &lt; 0.0001, <span class="html-italic">t</span>-test. Scale bars = 1 mm.</p>
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<p>BL21-TAN pre-treatment of PDAC tumors enhances the efficacy of gemcitabine. (<b>A</b>) NSG mice with subcutaneous BxPC3 xenografts (6–8 mm diameter) were injected with 5 × 10<sup>7</sup> colony-forming units (CFUs) of the BL21-eGFP control or BL21-TAN for three consecutive days by the intravenous route. After 24 h of the final injection, mice were administered 40 mg L-arabinose (induction) or PBS control intraperitoneally. Gemcitabine (GEM) or vehicle (PBS) was injected intraperitoneally 48 h after induction and maintenance doses were given every three days thereafter. Tumor growth (<b>B</b>) and mouse weights (<b>C</b>) were measured over time until control groups required euthanization. Data are expressed as mean values ± error of the mean. *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001, 2-way ANOVA.</p>
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20 pages, 4531 KiB  
Article
The Role of Dicer Phosphorylation in Gemcitabine Resistance of Pancreatic Cancer
by Ching-Feng Chiu, Hui-Ru Lin, Yen-Hao Su, Hsin-An Chen, Shao-Wen Hung and Shih-Yi Huang
Int. J. Mol. Sci. 2024, 25(21), 11797; https://doi.org/10.3390/ijms252111797 - 2 Nov 2024
Viewed by 558
Abstract
Dicer, a cytoplasmic type III RNase, is essential for the maturation of microRNAs (miRNAs) and is implicated in cancer progression and chemoresistance. Our previous research demonstrated that phosphorylation of Dicer at S1016 alters miRNA maturation and glutamine metabolism, contributing to gemcitabine (GEM) resistance [...] Read more.
Dicer, a cytoplasmic type III RNase, is essential for the maturation of microRNAs (miRNAs) and is implicated in cancer progression and chemoresistance. Our previous research demonstrated that phosphorylation of Dicer at S1016 alters miRNA maturation and glutamine metabolism, contributing to gemcitabine (GEM) resistance in pancreatic ductal adenocarcinoma (PDAC). In this study, we focused on the role of Dicer phosphorylation at S1728/S1852 in GEM-resistant PDAC cells. Using shRNA to knock down Dicer in GEM-resistant PANC-1 (PANC-1 GR) cells, we examined cell viability through MTT and clonogenic assays. We also expressed phosphomimetic Dicer 2E (S1728E/S1852E) and phosphomutant Dicer 2A (S1728A/S1852A) to evaluate their effects on GEM resistance and metabolism. Our results show that phosphorylation at S1728/S1852 promotes GEM resistance by reprogramming glutamine metabolism. Specifically, phosphomimetic Dicer 2E increased intracellular glutamine, driving pyrimidine synthesis and raising dCTP levels, which compete with gemcitabine’s metabolites. This metabolic shift enhanced drug resistance. In contrast, phosphomutant Dicer 2A reduced GEM resistance. These findings highlight the importance of Dicer phosphorylation in regulating metabolism and drug sensitivity, offering insights into potential therapeutic strategies for overcoming GEM resistance in pancreatic cancer. Full article
(This article belongs to the Topic Cancer Cell Metabolism (2nd Edition))
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Figure 1
<p>The expression level of <span class="html-italic">Dicer1</span> in PDAC tumor tissues and its impact on survival rates. (<b>A</b>) Analysis of the difference in <span class="html-italic">Dicer1</span> gene expression between PDAC tumor tissues (PAAD, n = 179) and normal tissues (n = 171) using the online database GEPIA; (<b>B</b>) survival analysis of pancreatic cancer patients based on <span class="html-italic">Dicer</span> expression levels (red line: high expression; green line: low expression) using the PROGgeneV2 Prognostic Database.</p>
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<p>The expression level of Dicer in the pancreatic cancer cell line PANC-1 GR is positively correlated with gemcitabine resistance and cell proliferation. (<b>A</b>) The clonogenicity assay was used to test colony formation ability under different concentrations of gemcitabine treatment for 7 days. (<b>B</b>) The MTT assay was used to test cell viability after treatment with different concentrations of gemcitabine for 72 h. Data are presented as mean ± SEM and analyzed using Student’s <span class="html-italic">t</span>-test. (<b>C</b>) The mRNA expression level of Dicer in PANC-1 and PANC-1 GR cells was tested using qRT-PCR. The qRT-PCR data were normalized to the GAPGH level in each sample, and a bar plot presents fold changes in the expression of PANC-1 cells. (<b>D</b>) The protein expression level of Dicer in PANC-1 and PANC-1 GR cells was tested using Western Blot, with Vinculin as the control. Data are presented as mean ± SEM and analyzed using Student’s <span class="html-italic">t</span>-test. Significant differences are indicated when <span class="html-italic">p</span> &lt; 0.05 (represented as * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Silencing Dicer expression in the pancreatic cancer cell line PANC-1 GR increases sensitivity to gemcitabine and reduces cell growth rate. (<b>A</b>) The mRNA expression level of Dicer in PANC-1 GR/shCtrl #1 and PANC-1 GR/shDicer#1 cells was tested using qRT-PCR. The qRT-PCR data were normalized to the GAPGH level in each sample, and a bar plot presents fold changes in the expression of PANC-1_GR_shCtrl#1 cells. (<b>B</b>) The protein expression level of Dicer in PANC-1 GR/shCtrl #1 and PANC-1 GR/shDicer#1 cells was tested using Western Blot, with Vinculin as the control. (<b>C</b>) The MTT assay was used to test cell viability at 24, 48, and 72 h in PANC-1 GR/shCtrl #1 and PANC-1 GR/shDicer#1 cells. (<b>D</b>) The clonogenicity assay was used to test colony formation ability under different concentrations of gemcitabine treatment for 7 days. Data are presented as mean ± SEM and analyzed using Student’s <span class="html-italic">t</span>-test and Two-way ANOVA. Significant differences are indicated when <span class="html-italic">p</span> &lt; 0.05 (represented as * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Simulating phosphorylation of Dicer at S1728E/S1852E in PANC-1 GR/shDicer cells restores cell growth rate. (<b>A</b>) The protein expression level of Dicer wild type (WT), phosphomutant Dicer S1728A/S1852A, and phosphomimetic Dicer S1728E/S1852E was tested using Western Blot, with Vinculin as the control. (<b>B</b>) The MTT assay was used to test cell viability at 24, 48, and 72 h in Ctrl, Dicer WT, Dicer 2A, and Dicer 2E cells. (<b>C</b>) The clonogenicity assay was used to test colony formation ability under different concentrations of gemcitabine treatment for 7 days in Ctrl, Dicer WT, Dicer 2A, and Dicer 2E cells. Data are presented as mean ± SEM and analyzed using One-way ANOVA and Two-way ANOVA. Significant differences are indicated when <span class="html-italic">p</span> &lt; 0.05 (represented as **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Simulating phosphorylation of Dicer at S1728E/S1852E in PANC-1 GR/shDicer cells restores resistance to gemcitabine. (<b>A</b>) The MTT assay was used to test cell viability after treatment with different concentrations of gemcitabine in Ctrl, Dicer WT, Dicer 2A, and Dicer 2E cells after 72 h. (<b>B</b>) The clonogenicity assay was used to test colony formation ability under different concentrations of gemcitabine treatment in Ctrl, Dicer WT, Dicer 2A, and Dicer 2E cells after 7 days. Data are presented as mean ± SEM and analyzed using One-way ANOVA. Significant differences are indicated when <span class="html-italic">p</span> &lt; 0.05 (represented as and **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Impact of simulating phosphorylation of Dicer S1728E/S1852E on glutamine metabolism in PANC-1 GR/shDicer cells. (<b>A</b>–<b>E</b>) The mRNA expression levels of GLS, GLUL, SLC38A1, SLC1A5, and SLC1A5_var in Ctrl, Dicer WT, Dicer 2A, and Dicer 2E cells were tested using qRT-PCR. The qRT-PCR data were normalized to the GAPGH level in each sample, and a bar plot presents fold changes in the expression of PANC-1_GR_shCtrl#1 cells. Data are presented as mean ± SEM and analyzed using One-way ANOVA. Significant differences are indicated when <span class="html-italic">p</span> &lt; 0.05 (represented as * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Simulating Dicer S1728E/S1852E phosphorylation in PANC-1 GR/shDicer cells impacts glutamine metabolism, further increasing intracellular glutamine concentration. (<b>A</b>–<b>D</b>) The intracellular concentrations of glutamine and glutamate and the concentrations in the culture medium were measured using the glutamine/glutamate-Glo assay after 72 h. Data are presented as mean ± SEM and analyzed using One-way ANOVA. Significant differences are indicated when <span class="html-italic">p</span> &lt; 0.05 (represented as ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Simulating Dicer S1728E/S1852E phosphorylation in PANC-1 GR/shDicer cells regulates specific miRNAs affecting glutamine metabolism. (<b>A</b>,<b>B</b>) The expression levels of miRNAs regulating GLUL in Ctrl, Dicer WT, Dicer 2A, and Dicer 2E cells were tested using qRT-PCR. The qRT-PCR data were normalized to the U47 level in each sample, and a bar plot presents fold changes in the expression of PANC-1_GR_shCtrl#1 cells. Data are presented as mean ± SEM and analyzed using One-way ANOVA. Significant differences are indicated when <span class="html-italic">p</span> &lt; 0.05 (represented as ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Inhibition of cell proliferation and increased sensitivity to gemcitabine through resolving glutamine metabolism abnormalities in pancreatic cancer drug-resistant cells. (<b>A</b>) The MTT assay was used to test cell viability after Ctrl, Dicer WT, Dicer 2A, and Dicer 2E cells were cultured in a glutamine-free medium after 72 h. (<b>B</b>) The MTT assay was used to test cell viability, followed by treatment with different concentrations of gemcitabine after 72 h, of Ctrl, Dicer WT, Dicer 2A, and Dicer 2E cells cultured in glutamine-free medium. (<b>C</b>) The MTT assay was used to test the cell viability of Ctrl, Dicer WT, Dicer 2A, and Dicer 2E cells treated with methionine sulfoximine (MSO) after 72 h. (<b>D</b>) The clonogenicity assay was used to test Ctrl, Dicer WT, Dicer 2A, and Dicer 2E cells’ colony formation ability after being treated with methionine sulfoximine (MSO) after 7 days. (<b>E</b>) Three thousand cells/well were seeded in a 96-well plate and treated with methionine sulfoximine (MSO), followed by treatment with different concentrations of gemcitabine, and MTT assay to test the cell viability of Ctrl, Dicer WT, Dicer 2A, and Dicer 2E cells after 72 h. (<b>F</b>) Five hundred cells/well were seeded in a 6-well plate and treated with methionine sulfoximine (MSO), followed by clonogenicity assay to test the colony formation ability of Ctrl, Dicer WT, Dicer 2A, and Dicer 2E cells under different concentrations of gemcitabine treatment after 7 days. Data are presented as mean ± SEM and analyzed using One-way and Two-way ANOVAs. Significant differences are indicated when <span class="html-italic">p</span> &lt; 0.05 (represented as * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001, and ns: Non significance.).</p>
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<p>Simulating phosphorylation of Dicer at S1728E/S1852E increases the expression of enzymes CAD and CTPS1 and 2 required for pyrimidine synthesis, leading to competitive inhibition by gemcitabine’s end metabolites and resulting in gemcitabine resistance. (<b>A</b>) The mRNA expression levels of CAD and CTPS1 and 2 in Ctrl, Dicer WT, Dicer 2A, and Dicer 2E cells were tested using qRT-PCR. The qRT-PCR data were normalized to the GAPGH level in each sample, and a bar plot presents fold changes in the expression of PANC-1_GR_shCtrl#1 cells. (<b>B</b>) The protein expression levels of CAD and CTPS1 and 2 in Ctrl, Dicer WT, Dicer 2A, and Dicer 2E cells were tested using Western Blot, with Vinculin as the control. Data are presented as mean ± SEM and analyzed using One-way ANOVA. Significant differences are indicated when <span class="html-italic">p</span> &lt; 0.05 (represented as * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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17 pages, 2450 KiB  
Article
TGF-β Signaling Loop in Pancreatic Ductal Adenocarcinoma Activates Fibroblasts and Increases Tumor Cell Aggressiveness
by Noemi di Miceli, Chiara Baioni, Linda Barbieri, Davide Danielli, Emiliano Sala, Lucia Salvioni, Stefania Garbujo, Miriam Colombo, Davide Prosperi, Metello Innocenti and Luisa Fiandra
Cancers 2024, 16(21), 3705; https://doi.org/10.3390/cancers16213705 - 1 Nov 2024
Viewed by 876
Abstract
Background: The interaction between cancer cells and cancer-associated fibroblasts (CAFs) is a key determinant of the rapid progression, high invasiveness, and chemoresistance of aggressive desmoplastic cancers such as pancreatic ductal adenocarcinoma (PDAC). Tumor cells are known to reprogram fibroblasts into CAFs by secreting [...] Read more.
Background: The interaction between cancer cells and cancer-associated fibroblasts (CAFs) is a key determinant of the rapid progression, high invasiveness, and chemoresistance of aggressive desmoplastic cancers such as pancreatic ductal adenocarcinoma (PDAC). Tumor cells are known to reprogram fibroblasts into CAFs by secreting transforming growth factor beta (TGF-β), amongst other cytokines. In turn, CAFs produce soluble factors that promote tumor-cell invasiveness and chemoresistance, including TGF-β itself, which has a major role in myofibroblastic CAFs. Such a high level of complexity has hampered progress toward a clear view of the TGFβ signaling loop between stromal fibroblasts and PDAC cells. Methods: Here, we tackled this issue by using co-culture settings that allow paracrine signaling alone (transwell systems) or paracrine and contact-mediated signaling (3D spheroids). Results: We found that TGF-β is critically involved in the activation of normal human fibroblasts into alpha-smooth muscle actin (α-SMA)-positive CAFs. The TGF-β released by CAFs accounted for the enhanced proliferation and resistance to gemcitabine of PDAC cells. This was accompanied by a partial epithelial-to-mesenchymal transition in PDAC cells, with no increase in their migratory abilities. Nevertheless, 3D heterospheroids comprising PDAC cells and fibroblasts allowed monitoring the pro-invasive effects of CAFs on cancer cells, possibly due to combined paracrine and physical contact-mediated signals. Conclusions: We conclude that TGF-β is only one of the players that mediates the communication between PDAC cells and fibroblasts and controls the acquisition of aggressive phenotypes. Hence, these advanced in vitro models may be exploited to further investigate these events and to design innovative anti-PDAC therapies. Full article
(This article belongs to the Special Issue Targeting the Tumor Microenvironment (Volume II))
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Figure 1
<p>TGF-β released by tumor cells promotes fibroblasts’ activation. (<b>A</b>) Expression of α-SMA in MRC-5 fibroblasts, cultured on the upper side of the transwell, at the end of 5 days exposure to PANC-1 cells, or TGF-β (10 ng/mL); representative cropped images of the Western blot of α-SMA and GAPDH (housekeeping protein) derived from the original blots in <a href="#app1-cancers-16-03705" class="html-app">Figure S1</a>. (<b>B</b>) The intensity of the SMA signal was quantified and normalized to that of GAPDH from the same lane (α-SMA expression in MRC-5 cells in the monoculture was set to 1. Mean values ± SE (n = 3 biological replicates) were compared by one-way ANOVA; * <span class="html-italic">p</span> &lt; 0.05. (<b>C</b>) MRC-5 fibroblasts at day 5 of the monoculture or treated with TGF-β (5 ng/mL), with or without 10 μM TGF-β receptor inhibitor (SB431542) or SMAD3 inhibitor (SIS3), or day 5 of the co-culture with PANC-1 cells, in the absence or presence of the same inhibitors. The number of MRC-5 cells in the monoculture was set as a reference at 100%. Data, represented as box-and-whisker plots, were compared by one-way ANOVA; **** <span class="html-italic">p</span> &lt; 0.0005, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01 (n = 3–13).</p>
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<p>(<b>A</b>) MRC-5 cells promote tumor-cell proliferation in transwell co-cultures. PANC-1 cells in the lower chamber of the transwell units were counted after 5 days of co-culture with MRC-5 cells or exposure to TGF-β (5 ng/mL), with or without 10 μM TGF-β receptor inhibitor (SB431542) or SMAD3 inhibitor (SIS3). The number of PANC-1 cells in the monoculture was set as a reference at 100%. (<b>B</b>) MRC-5 cells promote tumor-cell chemoresistance to gemcitabine (Gem). The viability of PANC-1 cultured for 5 days with or without MRC-5 fibroblasts in the transwell, or with or without TGF-β (5 ng/mL), and then treated with 50 µM Gem for 48 h, was assessed by MTT. The viability of the untreated monoculture was set to 100%. Data, represented as box-and-whisker plots, were compared by one-way ANOVA. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0005 ((<b>A</b>): n = 4–10; (<b>B</b>): n = 4).</p>
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<p>Migration of PANC-1 cells upon culture with activated fibroblasts or TGF-β. (<b>A</b>) Wound-healing assays performed with PANC-1 cells at the end of 5 days of culture in the presence of 5 ng/mL TGF-β, or co-cultured with MRC-5 cells, to detect their migration at 4, 8, and 24 h; starting cell-free space: 500 μm. Bar: 100 µm. (<b>B</b>) Migration-free space (μm) over time (0–24 h). Points represent mean values ± SE (n = 3) and were compared by one-way ANOVA (** <span class="html-italic">p</span> &lt; 0.01 vs. control PANC-1). (<b>C</b>) Migration ability of PANC-1 cells induced by 5 ng/mL TGF-β or co-cultured with MRC-5 cells in transwell units. The migration of PANC-1 cells alone was taken as a reference (1) to normalize the data. Mean ± SE (n = 3) are displayed. ** <span class="html-italic">p</span> &lt; 0.01 (one-way ANOVA).</p>
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<p>Role of TGF-β in PANC-1/MRC-5 heterospheroid proliferation. (<b>A</b>) Representative images of heterospheroids consisting of PANC-1 and MRC-5 cells pre-labeled with Vybrant<sup>®</sup> CFDA SE and CellTracker™ Deep Red dye. Scale bars: 200 µm. (<b>B</b>) Images of MRC-5/PANC-1 heterospheroids treated with 10 μM TGF-βR1 inhibitor (SB431542) or SMAD3 inhibitor (SIS3) for 7 days. (<b>C</b>) The size of the spheroids was determined using the acquired images (average of 4 different diameters for each condition). Data, represented as box-and-whisker plots, were compared by one-way ANOVA. * <span class="html-italic">p</span> &lt; 0.05 (n = 10–12).</p>
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<p>Chemoresistance and invasiveness of PANC-1 cells in heterospheroids, and role of TGF-β. (<b>A</b>) Cell death of 7-day-old PANC-1 monospheroids, pre-incubated with or without TGF-β (5 ng/mL), or PANC-1/MRC-5 heterospheroids after 48 h incubation with 50 μM gemcitabine (Gem), determined as LDH production (% variation toward the respective untreated ones). Means ± SE (n = 3–7), **** <span class="html-italic">p</span> &lt; 0.0005 vs. PANC-1 monospheroids (one-way ANOVA). (<b>B</b>) Invasiveness of PANC-1 cells labeled with Vybrant<sup>®</sup> CFDA SE from 7-day-old PANC-1 monospheroids, pre-incubated with or without TGF-β, or PANC-1/MRC-5 heterospheroids into the surrounding semi-solid matrix. Spheroids were observed with an Operetta CLS Imaging System (5×), and brightfield and fluorescence images represent a single inner z-stack. Arrows indicate PANC-1 cells spreading from the spheroid to the surrounding area.</p>
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20 pages, 3785 KiB  
Article
Impact of Nordihydroguaiaretic Acid on Proliferation, Energy Metabolism, and Chemosensitization in Non-Small-Cell Lung Cancer (NSCLC) Cell Lines
by Carina Chipón, Paula Riffo, Loreto Ojeda, Mónica Salas, Rafael A. Burgos, Pamela Ehrenfeld, Rodrigo López-Muñoz and Angara Zambrano
Int. J. Mol. Sci. 2024, 25(21), 11601; https://doi.org/10.3390/ijms252111601 - 29 Oct 2024
Viewed by 550
Abstract
Lung cancer (LC) is the leading cause of cancer death worldwide. LC can be classified into small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC), with the last subtype accounting for approximately 85% of all diagnosed lung cancer cases. Despite the existence of [...] Read more.
Lung cancer (LC) is the leading cause of cancer death worldwide. LC can be classified into small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC), with the last subtype accounting for approximately 85% of all diagnosed lung cancer cases. Despite the existence of different types of treatment for this disease, the development of resistance to therapies and tumor recurrence in patients have maintained the need to find new therapeutic options to combat this pathology, where natural products stand out as an attractive source for this search. Nordihydroguaiaretic acid (NDGA) is the main metabolite extracted from the Larrea tridentata plant and has been shown to have different biological activities, including anticancer activity. In this study, H1975, H1299, and A549 cell lines were treated with NDGA, and its effect on cell viability, proliferation, and metabolism was evaluated using a resazurin reduction assay, incorporation of BrdU, and ki-67 gene expression and glucose uptake measurement, respectively. In addition, the combination of NDGA with clinical chemotherapeutics was investigated using an MTT assay and Combenefit software (version 2.02). The results showed that NDGA decreases the viability and proliferation of NSCLC cells and differentially modulates the expression of genes associated with different metabolic pathways. For example, the LDH gene expression decreased in all cell lines analyzed. However, GLUT3 gene expression increased after 24 h of treatment. The expression of the HIF-1 gene decreased early in the H1299 and A549 cell lines. In addition, the combination of NDGA with three chemotherapeutics (carboplatin, gemcitabine, and taxol) shows a synergic pattern in the decrease of cell viability on the H1299 cell line. In summary, this research provides new evidence about the role of NDGA in lung cancer. Interestingly, using NDGA to enhance the anticancer activity of antitumoral drugs could be an improved therapeutic resource against lung cancer. Full article
(This article belongs to the Section Molecular Oncology)
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<p><b>NDGA decreases cell survival of non-small-cell lung cancer cell lines.</b> H1975, H1299, A549, H358, Calu-1, SK-LU-1, and H2228 cell lines were treated with different concentrations of NDGA for 72 h (<b>A</b>), and the effect on cell viability was measured using a resazurin reduction assay. (<b>B</b>) Table with IC50 values obtained from A. Graph data represent mean ± SEM of three independent experiments.</p>
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<p><b>NDGA induces cell death in NSCLC cells.</b> (<b>A</b>) H1975, H1299, and A549 cells were treated with different concentrations of NDGA for 24 h, and the effect on cell viability was measured by propidium iodide staining. Graphs represent the mean of three experiments performed in triplicate. (<b>B</b>–<b>D</b>) H1975, H1299, and A549 cells were treated with different concentrations of NDGA (0, IC50, 100, and 250 µM) for 24 h, and the percentage of annexin V+/− or PI+/− cells was evaluated by flow cytometry and graphed. (<b>E</b>–<b>G</b>) After 24 h of treatment with NDGA, cells were collected by centrifugation, and caspase-3 activity was analyzed according to the kit manufacturer’s instructions. The inhibitor was included in the kit. (<b>H</b>–<b>J</b>) Cells were treated with the corresponding IC50 values for each cell line for 3 h, 6 h, or 24 h, and then RNA was extracted. The gene expression of Bcl-2 and Bax was evaluated using RT-qPCR assays. Actin was used as a housekeeping gene, and culture media was used as a vehicle. Data are presented as mean ± SEM of 3 independent experiments. Statistical analysis was performed using two-way ANOVA for flow cytometry assays. <span class="html-italic">p</span>-values are * <span class="html-italic">p</span> &lt; 0.05 and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p><b>Role of NDGA on pyroptosis cell death.</b> (<b>A</b>–<b>C</b>) Cells were treated with NDGA values corresponding to the IC50 for each cell line during 3 h, 6 h, and 24 h, and then RNA was extracted. The gene expression of IL-1b was evaluated using RT-qPCR assays. (<b>D</b>–<b>F</b>) Cells were treated with corresponding IC50 values of NDGA for each cell line. The gene expression of IL-18 was evaluated using RT-qPCR assays. Actin was used as a housekeeping gene, and media was used as a vehicle. Data are presented as means ± SD. (<b>G</b>). Western blot analysis of total proteins from cells treated with 25 or 100 µM NDGA during 24 h, using the specific antibodies anti-GSDME and anti-GSDMD. An antibody against vinculin was used as a loading control. The cells were treated with a vehicle (media) as a control. Statistical analysis by one-way ANOVA. <span class="html-italic">p</span>-values are * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p><b>NDGA inhibits cell proliferation of NSCLC cells.</b> (<b>A</b>) H1975, H1299, and A549 cells were treated with different concentrations of NDGA for 24 h. After 10 days, the colony-forming assay was performed. (<b>B</b>–<b>D</b>) The cell lines were treated with 25, 50, or 100 µM NDGA for 24 h, and cell proliferation was measured by the relative incorporation of BrdU. (<b>E</b>–<b>G</b>) Cells were treated for 3 h, 6 h, and 24 h, and then the effect on ki-67 gene expression was evaluated with RT-qPCR assays. Actin was used as a housekeeping gene, and media was used as a vehicle. Data are presented as means ± SD. (<b>H</b>–<b>J</b>) The cells were treated for 24 h with IC50 and 100 µM NDGA, collected, and stained with PI for cell cycle analysis by flow cytometry. <span class="html-italic">p</span>-values are determined by one-way ANOVA compared to the control * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p><b>NDGA impacts glucose uptake and metabolic enzymes.</b> (<b>A</b>–<b>C</b>) H1975, H1299, and A549 cells were treated with IC50 values and 100 µM of NDGA for 6 h and 24 h. The intracellular accumulation of 2-deoxy-glucose-6-phosphate was measured by luminescence. Cytochalasin B (50 µM) was used as a glucose transport inhibitor control. (<b>D</b>–<b>R</b>) H1975, H1299, and A549 cells were treated during 3, 6, and 24 h with IC50 values of NDGA, and then RNA was extracted and used to evaluate the levels of GLUT1 (<b>D</b>–<b>F</b>), GLUT3 (<b>G</b>–<b>I</b>), PKM2 (<b>J</b>–<b>L</b>), HIF-1 (<b>M</b>–<b>O</b>), and LDH (<b>P</b>–<b>R</b>). Actin was used as a housekeeping gene, and DMSO (or media) was used as a vehicle. Data are presented as means ± SD. <span class="html-italic">p</span>-values were determined by one-way ANOVA compared to the control * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p><b>In combination with carboplatin, NDGA synergically decreases the cell viability of H1975, H1299, and A549 cells.</b> NSCLC cells were treated for 72 h with different concentrations of NDGA and carboplatin, gemcitabine, or taxol. Cell viability was measured using an MTT assay. (<b>A</b>–<b>C</b>) Effect matrices are plotted using Combenefit software and the Loewe model of drug combinations.</p>
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8 pages, 231 KiB  
Case Report
Multiple Sclerosis and Subcutaneous Panniculitis-like T Cell Lymphoma with Hemophagocytic Syndrome: The Role of Treatment Sequencing in the Pathogenetic Mechanism
by Assunta Trinchillo, Antonio Carotenuto, Antonio Luca Spiezia, Daniele Caliendo, Alessandro Severino, Cristina Di Monaco, Carmine Iacovazzo, Giuseppe Servillo, Vincenzo Brescia Morra and Roberta Lanzillo
Sclerosis 2024, 2(4), 314-321; https://doi.org/10.3390/sclerosis2040020 - 28 Oct 2024
Viewed by 537
Abstract
Introduction: Although panniculitis-like T cell lymphoma (SPTCL) and hemophagocytic syndrome (HSP) have been described as complications following immunosuppressive treatments, there are no reported cases of concomitant SPTCL/HSP and multiple sclerosis (MS). Materials and Methods: We describe the case of a patient affected by [...] Read more.
Introduction: Although panniculitis-like T cell lymphoma (SPTCL) and hemophagocytic syndrome (HSP) have been described as complications following immunosuppressive treatments, there are no reported cases of concomitant SPTCL/HSP and multiple sclerosis (MS). Materials and Methods: We describe the case of a patient affected by an aggressive phenotype of relapsing remitting MS, characterized by consecutive severe relapses with no complete remission. He developed panniculitis-like T cell lymphoma (SPTCL) and hemophagocytic syndrome (HSP) after receiving multiple immunosuppressive treatments in sequence. Despite the aggressive nature of these complications, the patient responded well to a combination of Gemcitabine and Cisplatin. Discussion and Conclusions: With this case, we suggest that physicians always consider blood diseases as possible MS therapy complications, especially in the sequencing setting, and also consider uncommon treatments in those with autoimmune predispositions. Full article
15 pages, 3934 KiB  
Article
A Tumor Homing Peptide-Linked Arsenic Compound Inhibits Pancreatic Cancer Growth and Enhances the Inhibitory Effect of Gemcitabine
by Hong He, Chelsea Dumesny, Judith A. Carrall, Carolyn T. Dillon, Katja I. de Roo, Mal Eutick, Li Dong, Graham S. Baldwin and Mehrdad Nikfarjam
Int. J. Mol. Sci. 2024, 25(21), 11366; https://doi.org/10.3390/ijms252111366 - 22 Oct 2024
Viewed by 924
Abstract
Arsenic trioxide (ATO) has been shown to inhibit pancreatic cancer (PC) cell growth in vitro and to promote the inhibitory effects of gemcitabine (Gem) on PC in vivo. However, the high toxicity of ATO associated with the required high doses and indiscriminate [...] Read more.
Arsenic trioxide (ATO) has been shown to inhibit pancreatic cancer (PC) cell growth in vitro and to promote the inhibitory effects of gemcitabine (Gem) on PC in vivo. However, the high toxicity of ATO associated with the required high doses and indiscriminate targeting has limited its clinical application. This study aimed to determine whether coupling arsenic to a tumor homing peptide would increase the inhibitory potency against PC cells. The effects of this peptide-linked arsenic compound (PhAs-LHP), the analogous non-targeting arsenic compound (phenylarsine oxide, PAO), and marketed ATO on PC growth were tested in vitro and in a mouse model. The data demonstrated that PhAs-LHP inhibited PC cell growth in vitro more potently, with IC50 values 10 times lower than ATO. Like ATO, PhAs-LHP induced cell death and cell cycle arrest. This cytotoxic effect of PhAs-LHP was mediated via a macropinocytosis-linked uptake pathway as amiloride (a macropinocytosis inhibitor) reduced the inhibitory effect of PhAs-LHP. More importantly, PhAs-LHP inhibited PC growth in mice and enhanced the inhibitory effect of Gem on PC growth at 2 times lower molar concentration than PAO. These results indicate that PhAs-LHP inhibited PC more potently than ATO/PAO and suggest a potential clinical use for the combination of Gem with the peptide-linked arsenic compound for the treatment of pancreatic cancer. Full article
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Figure 1
<p><b>ATO inhibited pancreatic cancer cell growth.</b> Human (MiaPaCa-2 (<b>A</b>) and PANC-1 (<b>B</b>)) and murine (KPCWT942 (<b>C</b>) and TB33117 (<b>D</b>)) pancreatic cancer (PC) cells were incubated with ATO for 24, 48, and 72 h. Cell proliferation was measured by the MTT assay as described in the Materials and Methods section. The values of control cells without treatment with ATO were defined as 100%. The results were representatives from three independent experiments.</p>
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<p><b>The inhibitory effect of ATO on pancreatic cancer cell proliferation was enhanced by pre-treatment with gemcitabine or PF-3758309.</b> Human (MiaPaCa-2 (<b>A</b>) and PANC-1 (<b>B</b>)) and murine (TB33117 (<b>C</b>) and KPCWT942 (<b>D</b>)) pancreatic cancer (PC) cells were pre-treated with gemcitabine (Gem) and PF-3758309 (PF) at concentrations indicated in the figures for 24 h, followed by 48 h treatment with arsenic trioxide (ATO). Cell proliferation was measured by the MTT assay. The values of control cells without any treatment were defined as 100%. The results were summarized from three independent experiments. *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01, compared to the non-treated control; #: <span class="html-italic">p</span> &lt; 0.05, compared to Gem-pre-treatment only; <span>$</span>: <span class="html-italic">p</span> &lt; 0.05, compared to PF pre-treatment only.</p>
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<p><b>PhAs-LHP inhibited pancreatic cancer cell growth more potently.</b> Human (MiaPaCa-2 (<b>A</b>) and PANC-1 (<b>B</b>)) and murine TB33117 (<b>C</b>) pancreatic cancer (PC) cells were incubated with ATO, PhAs-LHP, or the peptide LHP for 48 h. Cell proliferation was measured by the MTT assay. The values of control cells without any treatment were defined as 100%. The results were summarized from at least three independent experiments.</p>
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<p><b>PhAs-LHP induced cell death and cell cycle arrest more potently than ATO.</b> MiaPaCa-2 cells were treated with ATO (0.5 μM) or PhAs-LHP (0.05 μM) for 48 h (<b>A</b>). PANC-1 cells were treated with ATO (2 μM) or PhAs-LHP (0.2 μM) for 24 h (<b>B</b>). Both MiaPaCa-2 and PANC-1 cells were then stained with propidium iodide (PI) and subjected to flow cytometry analysis. Numbers indicate the percent of PI-negative cells of a total of 10,000 cells analyzed per condition (<b>A</b>).</p>
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<p><b>PhAs-LHP inhibited MiaPaCa-2 proliferation via LHP-mediated macropinocytosis uptake.</b> Uptake of 5FAM-labeled LHP peptide into live MiaPaCa-2 human cells. (<b>a</b>) Typical confocal image of 5FAM-LHP (green) in live cells counterstained with Hoechst 33342 (blue) following incubation (1 h, 37 °C). Image taken using a Leica SP8 microscope at 63× magnification where scale bar = 50 µm. (<b>b</b>) Comparison of the uptake of 5FAM-LHP in live MiaPaCa-2 cells following 1 h incubation in the absence or presence of the macropinocytosis inhibitor amiloride (25 µM, 1 h co-treatment or 16 h pre-treatment). Uptake is presented as the mean total particle area/total cell area. Error bars represent the standard deviation from the mean (n = 8–15). (<b>c</b>) Comparison of the IC<sub>50</sub> values obtained from MTT assays of MiaPaCa-2 cells following 24 h treatment with ATO, PAO, or PhAs-LHP alone or in the presence of amiloride (25 µM) for 24 h. Error bars represent the standard deviation from the mean (n = 3). Statistical significance is indicated by **, <span class="html-italic">p</span> &lt; 0.01, or ****, <span class="html-italic">p</span> &lt; 0.0001, ns, not significant.</p>
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<p><b>PhAs-LHP suppressed pancreatic cancer growth in Scid mice.</b> MiaPaCa-2 human pancreatic cancer cells (5 × 10<sup>6</sup> cells) were subcutaneously injected into the flank of a Scid mouse. When the tumor size reached &gt;50 mm<sup>3</sup>, PAO (50 μg/kg) or PhAs-LHP (200 μg/kg) was given by intraperitoneal (i.p.) injection every other day for the time periods indicated in (<b>A</b>). The control (CT) mice were treated with saline. Tumor volume (<b>A</b>) and weight (<b>B</b>) were measured as described in the Materials and Methods section. There were 7, 8, and 8 mice in the control, PAO, and PhAs-LHP groups respectively, as indicated in the photo of tumors isolated from each mouse (<b>C</b>). The mouse body weight was monitored and shown in (<b>D</b>). *, <span class="html-italic">p</span> &lt; 0.05, compared to control.</p>
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<p><b>PhAs-LHP enhanced the inhibitory effect of gemcitabine on pancreatic cancer growth in Scid mice.</b> Human pancreatic cancer cells, MiaPaCa-2 (5 × 10<sup>6</sup> cells) were subcutaneously injected into the flank of a Scid mouse. When tumor size reached &gt;50 mm<sup>3</sup>, gemcitabine (Gem, 50 mg/kg), Gem plus PAO (50 μg/kg), or PhAs-LHP (200 μg/kg) was given by intraperitoneal (i.p.) injection for the time periods indicated in (<b>A</b>). Gem was given every 4 days. PAO and PhAs-LHP were given every other day. Tumor volume (<b>A</b>) and weight (<b>B</b>) were measured as described in the Materials and Methods section. There were 4, 4, 5, and 5 mice in the control, Gem, Gem + PAO, and Gem + PhAs-LHP groups, respectively, as indicated in (<b>C</b>). The mouse body weight was monitored and shown in (<b>D</b>). CT: control; *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01, compared to control; #, <span class="html-italic">p</span> &lt; 0.05, compared to Gem alone.</p>
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<p><b>Arsenic distribution of PAO and PhAs-LHP.</b> The brain, heart, liver, and kidney tissues from the mice of the control, Gem, PAO + Gem, and PhAs-LHP + Gem groups were analyzed by graphite furnace atomic absorption spectrophotometry (GFAAS) to determine the arsenic (As) concentrations (<b>c</b>). Heat maps showed the As distributions in these organs: (<b>a</b>) PAO + Gem; (<b>b</b>) PhAs-LHP + Gem. Colors represent the relative mean concentrations of As detected. Rel. Min. As, relative minimum amount of As detected, Rel. Max. As, relative maximum amount of As detected. Heat map figures (<b>c</b>) created in BioRender.com. (29 September 2024) The treatment groups are represented by: control group (black, n = 4), gemcitabine treatment (green, n = 4), PAO + gemcitabine (red, n = 5), and PhAs(LHP) + gemcitabine (blue, n = 5). Each data point represents the specified data from one mouse. Statistical significance with respect to the control group is indicated by: ns (not significant), †††† (<span class="html-italic">p</span> &lt; 0.0001). Data are represented as the mean with the interquartile range.</p>
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<p><b>The expression of Skp2 was reduced by PAO, PhAs-LHP, and gemcitabine.</b> Tumor tissues from the experiments shown in <a href="#ijms-25-11366-f005" class="html-fig">Figure 5</a> and <a href="#ijms-25-11366-f006" class="html-fig">Figure 6</a> were immunohistochemically stained by Skp2. The expression of Skp2 was decreased in the tumors of mice treated with PAO, PhAs-LHP, and gemcitabine. The combination of gemcitabine with PAO or PhAs-LHP did not further decrease the levels of Skp2. *, <span class="html-italic">p</span> &lt; 0.05 compared to the untreated control. CT, control; Gem, gemcitabine.</p>
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20 pages, 7024 KiB  
Article
Dual-Action Gemcitabine Delivery: Chitosan–Magnetite–Zeolite Capsules for Targeted Cancer Therapy and Antibacterial Defense
by Yuly Andrea Guarín-González, Gerardo Cabello-Guzmán, José Reyes-Gasga, Yanko Moreno-Navarro, Luis Vergara-González, Antonia Martin-Martín, Rodrigo López-Muñoz, Galo Cárdenas-Triviño and Luis F. Barraza
Gels 2024, 10(10), 672; https://doi.org/10.3390/gels10100672 - 21 Oct 2024
Viewed by 842
Abstract
Cancer and infectious diseases are two of the world’s major public health problems. Gemcitabine (GEM) is an effective chemotherapeutic agent against several types of cancer. In this study, we developed macrocapsules incorporating GEM into a chitosan matrix blended with magnetite and zeolite by [...] Read more.
Cancer and infectious diseases are two of the world’s major public health problems. Gemcitabine (GEM) is an effective chemotherapeutic agent against several types of cancer. In this study, we developed macrocapsules incorporating GEM into a chitosan matrix blended with magnetite and zeolite by ionic gelation. Physicochemical characterization was performed using HRTEM-ED, XRD, FESEM–EDS, FT-IR, TGA, encapsulation efficiency (%E.E.), and release profiles at pHs 7.4 and 5.0. Cell viability tests against A549 and H1299 cell lines, and microbiological properties against staphylococcal strains were performed. Our results revealed the successful production of hemispherical capsules with an average diameter of 1.22 mm, a rough surface, and characteristic FT-IR material interaction bands. The macrocapsules showed a high GEM encapsulation efficiency of over 86% and controlled release over 24 h. Cell viability assays revealed that similar cytotoxic effects to free GEM were achieved with a 45-fold lower GEM concentration, suggesting reduced dosing requirements and potentially fewer side effects. Additionally, the macrocapsules demonstrated potent antimicrobial activity, reducing Staphylococcus epidermidis growth by over 90%. These results highlight the macrocapsules dual role as a chemotherapeutic and antimicrobial agent, offering a promising strategy for treating lung cancer in patients at risk of infectious diseases or who are immunosuppressed. Full article
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<p>(<b>A</b>) FESEM micrograph with 20,000× magnification, (<b>B</b>) EDS and elemental composition, (<b>C</b>) TEM micrograph, and (<b>D</b>) diffraction pattern of zeolite clinoptilolite.</p>
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<p>(<b>A</b>) XRD planes; (<b>B</b>) FT-IR of zeolite clinoptilolite.</p>
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<p>(<b>A</b>) FESEM micrograph with 160,000× magnification, (<b>B</b>) EDS and elemental composition, (<b>C</b>) TEM micrograph, and (<b>D</b>) diffraction pattern of magnetite.</p>
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<p>(<b>A</b>) XRD planes; (<b>B</b>) FT-IR of magnetite.</p>
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<p>(<b>A</b>) Chitosan macrocapsules with clinoptilolite zeolite; (<b>B</b>) TEM micrograph of clinoptilolite within chitosan macrocapsules; (<b>C</b>) HRTEM micrograph of zeolite; (<b>D</b>) chitosan macrocapsules with magnetite; (<b>E</b>) TEM micrograph of magnetite within chitosan macrocapsules, and (<b>F</b>) HRTEM micrograph of magnetite.</p>
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<p>FESEM micrographs; (<b>A</b>) macrocapsules, group 2, with 50× magnification; (<b>B</b>) microcapsule, group 7, with 140× magnification; (<b>C</b>) microcapsule, group 13, with 140× magnification, and (<b>D</b>) split microcapsule, group 10, with 240× magnification.</p>
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<p>FT-IR spectrum macrocapsules, group 14.</p>
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<p>Release profile of free and encapsulated GEM under physiological temperature conditions (37 °C): (<b>A</b>) in PBS at pH 7.4 and (<b>B</b>) in acetate buffer at pH 5.</p>
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<p>(<b>A</b>) Cell viability in A549 and (<b>B</b>) in H1299 cell lines.</p>
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18 pages, 5253 KiB  
Article
Targeted PHA Microsphere-Loaded Triple-Drug System with Sustained Drug Release for Synergistic Chemotherapy and Gene Therapy
by Shuo Wang, Chao Zhang, Huandi Liu, Xueyu Fan, Shuangqing Fu, Wei Li and Honglei Zhang
Nanomaterials 2024, 14(20), 1657; https://doi.org/10.3390/nano14201657 - 16 Oct 2024
Viewed by 914
Abstract
The combination of paclitaxel (PTX) with other chemotherapy drugs (e.g., gemcitabine, GEM) or genetic drugs (e.g., siRNA) has been shown to enhance therapeutic efficacy against tumors, reduce individual drug dosages, and prevent drug resistance associated with single-drug treatments. However, the varying solubility of [...] Read more.
The combination of paclitaxel (PTX) with other chemotherapy drugs (e.g., gemcitabine, GEM) or genetic drugs (e.g., siRNA) has been shown to enhance therapeutic efficacy against tumors, reduce individual drug dosages, and prevent drug resistance associated with single-drug treatments. However, the varying solubility of chemotherapy drugs and genetic drugs presents a challenge in co-delivering these agents. In this study, nanoparticles loaded with PTX were prepared using the biodegradable polymer material poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHHx). These nanoparticles were surface-modified with target proteins (Affibody molecules) and RALA cationic peptides to create core-shell structured microspheres with targeted and cationic functionalization. A three-drug co-delivery system (PTX@PHBHHx-ARP/siRNAGEM) were developed by electrostatically adsorbing siRNA chains containing GEM onto the microsphere surface. The encapsulation efficiency of PTX in the nanodrug was found to be 81.02%, with a drug loading of 5.09%. The chemogene adsorption capacity of siRNAGEM was determined to be 97.3%. Morphological and size characterization of the nanodrug revealed that PTX@PHBHHx-ARP/siRNAGEM is a rough-surfaced microsphere with a particle size of approximately 150 nm. This nanodrug exhibited targeting capabilities toward BT474 cells with HER2 overexpression while showing limited targeting ability toward MCF-7 cells with low HER2 expression. Results from the MTT assay demonstrated that PTX@PHBHHx-ARP/siRNAGEM exhibits high cytotoxicity and excellent combination therapy efficacy compared to physically mixed PTX/GEM/siRNA. Additionally, Western blot analysis confirmed that siRNA-mediated reduction of Bcl-2 expression significantly enhanced cell apoptosis mediated by PTX or GEM in tumor cells, thereby increasing cell sensitivity to PTX and GEM. This study presents a novel targeted nanosystem for the co-delivery of chemotherapy drugs and genetic drugs. Full article
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<p>SEM (<b>a</b>), TEM (<b>b</b>), and FTIR (<b>c</b>) analysis.</p>
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<p>Release curves of PTX and siRNA<sub>GEM</sub> in PTX@PHBHHx-ARP/siRNA<sub>GEM</sub>. (<b>a</b>) The PTX drug release curve. (<b>b</b>) The release curve of siRNA<sub>GEM</sub>.</p>
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<p>Drug uptake by BT474 and MCF-7 cells. (<b>a</b>) CLSM analysis of BT474 cells treated with PTX@PHBHHx-ARP/siRNA<sub>GEM</sub> for different times. Panel (<b>b</b>) CLSM analysis of MCF-7 cells treated with PTX@PHBHHx-ARP/siRNA<sub>GEM</sub> for different times. Scale bar: 20 μm.</p>
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<p>Survival of BT474 and MCF-7 cells treated with PTX, GEM, physical mixture of PTX/GEM/siRNA and PTX@PHBHHx-ARP/siRNA<sub>GEM</sub>. (<b>a</b>) The survival rates of the two cell types after 48 h incubation with PTX monotherapy. (<b>b</b>) The survival rates of the two cell types after 48 h incubation with GEM monotherapy. (<b>c</b>) The survival rates of the two cell types after 48 h of incubation with the physical mixture of PTX/GEM/siRNA. (<b>d</b>) The survival rates of the two cell types after 48 h incubation with PTX@PHBHHx-ARP/siRNA<sub>GEM</sub>.</p>
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<p>Western blot of Bcl-2 in (<b>a</b>) BT474 cells and (<b>b</b>) MCF-7 cells treated with free PTX, GEM, PTX/GEM/siRNA, and PTX@PHBHHx-ARP/siRNA<sub>GEM</sub>, respectively. (<b>c</b>) Significance analysis of Bcl-2 in BT474 cells. (<b>d</b>) Significance analysis of Bcl-2 in MCF-7 cells. Statistical analysis: * <span class="html-italic">p</span> &lt; 0.05, **** <span class="html-italic">p</span> &lt; 0.0001 and ns (No significant difference).</p>
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<p>Scatter plots of apoptosis in (<b>a</b>) MCF-7 cells and (<b>b</b>) BT474 cells treated with free PTX, GEM, PTX/GEM/siRNA, and PTX@PHBHHx-ARP/siRNA<sub>GEM</sub>, respectively.</p>
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<p>The assembly process of PTX@PHBHHx-ARP/siRNA<sub>GEM</sub> and their synergistic cancer therapy.</p>
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22 pages, 19388 KiB  
Article
Network Pharmacology Approaches Used to Identify Therapeutic Molecules for Chronic Venous Disease Based on Potential miRNA Biomarkers
by Oscar Salvador Barrera-Vázquez, Juan Luis Escobar-Ramírez and Gil Alfonso Magos-Guerrero
J. Xenobiot. 2024, 14(4), 1519-1540; https://doi.org/10.3390/jox14040083 - 15 Oct 2024
Viewed by 1021
Abstract
Chronic venous disease (CVD) is a prevalent condition in adults, significantly affecting the global elderly population, with a higher incidence in women than in men. The modulation of gene expression through microRNA (miRNA) partly regulated the development of cardiovascular disease (CVD). Previous research [...] Read more.
Chronic venous disease (CVD) is a prevalent condition in adults, significantly affecting the global elderly population, with a higher incidence in women than in men. The modulation of gene expression through microRNA (miRNA) partly regulated the development of cardiovascular disease (CVD). Previous research identified a functional analysis of seven genes (CDS2, HDAC5, PPP6R2, PRRC2B, TBC1D22A, WNK1, and PABPC3) as targets of miRNAs related to CVD. In this context, miRNAs emerge as essential candidates for CVD diagnosis, representing novel molecular and biological knowledge. This work aims to identify, by network analysis, the miRNAs involved in CVD as potential biomarkers, either by interacting with small molecules such as toxins and pollutants or by searching for new drugs. Our study shows an updated landscape of the signaling pathways involving miRNAs in CVD pathology. This latest research includes data found through experimental tests and uses predictions to propose both miRNAs and genes as potential biomarkers to develop diagnostic and therapeutic methods for the early detection of CVD in the clinical setting. In addition, our pharmacological network analysis has, for the first time, shown how to use these potential biomarkers to find small molecules that may regulate them. Between the small molecules in this research, toxins, pollutants, and drugs showed outstanding interactions with these miRNAs. One of them, hesperidin, a widely prescribed drug for treating CVD and modulating the gene expression associated with CVD, was used as a reference for searching for new molecules that may interact with miRNAs involved in CVD. Among the drugs that exhibit the same miRNA expression profile as hesperidin, potential candidates include desoximetasone, curcumin, flurandrenolide, trifluridine, fludrocortisone, diflorasone, gemcitabine, floxuridine, and reversine. Further investigation of these drugs is essential to improve the treatment of cardiovascular disease. Additionally, supporting the clinical use of miRNAs as biomarkers for diagnosing and predicting CVD is crucial. Full article
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<p>Network analysis of CVD-associated miRNAs with their expression, sources, countries of origin, and detection methods. The network depicts the interconnected structure of miRNAs derived from CVD patients, organized by their expression, sources, countries of origin, and detection methods. In the network, miRNAs are denoted as blue (upregulated) or red (downregulated) nodes, green nodes represent countries, yellow nodes represent sources, and gray nodes represent detection methods. The connections between the nodes signify the frequency of independent study reports. The three most outstanding sources were the proximal part of the significant saphenous vein tissue, vein tissues, and peripheral blood mononuclear cells. China was the country with the most available finds from miRNAs. Microarrays and RT-PCR are the most effective methods for diagnosing CVD. At least two tissues are expected to contain five specific miRNAs: miR-34a, miR-34c, miR-202-3, miR-1202, and miR-130a. The network, constructed using Cytoscape software (v.3.10.2), comprises 78 nodes and 193 edges, with a diameter and a network density of 6 and 0.106, respectively. Please refer to the <a href="#app1-jox-14-00083" class="html-app">Supplementary Materials (Figure S1)</a> for a better image resolution.</p>
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<p>Network analysis of miRNAs associated with CVD and their predicted targets. (<b>A</b>) The bar plot visually presents the number of targetable genes in the miRNA curated dataset. In the plot, gray bars represent upregulated genes, blue bars denote downregulated genes, and orange bars indicate genes with undetermined expression (ND). (<b>B</b>) We constructed a structural network using reported and predicted interactions between miRNAs and their targeted genes. This network consists of 1882 nodes and 5267 edges, with a diameter and a network density of 12 and 0.001. The network was created using Cytoscape software (v.3.10.2). Please refer to the <a href="#app1-jox-14-00083" class="html-app">Supplementary Materials (Figure S2)</a> for a better image resolution.</p>
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<p>The structural network represents the ten most connected nodes, including miRNAs and targets. Nodes are color-coded from orange to yellow based on their degree of connection, representing the most connected genes and miRNAs in the network. The most relevant nodes in this network are WNK-1, hsa-miR-106b-3p, IL1NR, hsa-miR-92a-3p, PPP6R2, hsa-miR-454-3p, PRRC2B, hsa-miR-548ac, hsa-miR-128-3p, and ADIPOQ. The network consists of 921 nodes and 1256 edges, with a diameter and a network density of 7 and 0.003, respectively. This network was created using Cytoscape software (v.3.10.2). Please refer to the <a href="#app1-jox-14-00083" class="html-app">Supplementary Materials (Figure S3)</a> for a better image resolution.</p>
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<p>The structural network of small molecules, phlebotonic, and miRNAs. The structural network depicts the targeted miRNAs (green nodes) between small molecules (yellow nodes) and the phlebotonic hesperidin (pink node). It was observed that curcumin exhibited the highest connection of miRNAs with the reported phlebotonic hesperidin. The network involves 246 nodes and 1153 edges, with a diameter and a network density of 6 and 0.038, respectively. The network construction utilized Cytoscape software (v.3.10.2). Please refer to the <a href="#app1-jox-14-00083" class="html-app">Supplementary Materials (Figure S4)</a> for a better image resolution.</p>
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<p>Structural network of hesperidin-specific miRNAs that are also altered by small molecules. This structural network depicts the miRNA profiles shared by both miRNAs that are specifically upregulated or downregulated by the reference compound hesperidin and the small molecules studied in this work. These shared miRNA profiles between hesperidin and small molecules allow the selection of potential candidates for CVD treatment. Downregulated miRNAs are shown in red, upregulated in blue, and those shared with small molecules in orange. The network involves 93 nodes and 320 edges, with a diameter and a density of 4 and 0.075, respectively. This network was created using Cytoscape software (v.3.10.2). Please refer to the <a href="#app1-jox-14-00083" class="html-app">Supplementary Materials (Figure S5)</a> for a better image resolution.</p>
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<p>Flow diagram of the bibliographical screening performed for this research.</p>
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18 pages, 804 KiB  
Review
Ritonavir’s Evolving Role: A Journey from Antiretroviral Therapy to Broader Medical Applications
by Mariana Pereira and Nuno Vale
Curr. Oncol. 2024, 31(10), 6032-6049; https://doi.org/10.3390/curroncol31100450 - 8 Oct 2024
Viewed by 1088
Abstract
Ritonavir is a protease inhibitor initially developed for HIV treatment that is now used as a pharmacokinetic booster for other antiretrovirals due to it being a cytochrome P450 3A4 enzyme and P-glycoprotein inhibitor. Consequently, ritonavir is of special interest for repurposing in other [...] Read more.
Ritonavir is a protease inhibitor initially developed for HIV treatment that is now used as a pharmacokinetic booster for other antiretrovirals due to it being a cytochrome P450 3A4 enzyme and P-glycoprotein inhibitor. Consequently, ritonavir is of special interest for repurposing in other diseases. It had an important role in battling the COVID-19 pandemic as a part of the developed drug Paxlovid® in association with nirmatrelvir and has shown effects in hepatitis and other pathogenic diseases. Ritonavir has also shown promising results in overcoming drug resistance and enhancing the efficacy of existing chemotherapeutic agents in oncology. Evidence of cancer repurposing potential was demonstrated in cancers such as ovarian, prostate, lung, myeloma, breast, and bladder cancer, with several mechanisms of action presented. In vitro studies indicate that ritonavir alone can inhibit key pathways involved in cancer cell survival and proliferation, causing apoptosis, cell cycle arrest, endoplasmic reticulum stress, and metabolic stress due to the inhibition of molecules like heat shock protein 90 and cyclin-dependent kinases. Ritonavir also causes resistant cells to become sensitized to anticancer drugs like gemcitabine or docetaxel. These findings indicate that repurposing ritonavir, either on its own or in combination with other medications, could be a promising approach for treating various diseases. This is particularly relevant in cancer therapy, where ritonavir repurposing is the central focus of this review. Full article
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<p>Chemical structure of ritonavir. Developed with ChemBioDraw<sup>®</sup> Ultra version 13.0., a chemical drawing software. Available online: <a href="https://chemdrawdirect.perkinelmer.cloud/js/sample/index.html" target="_blank">https://chemdrawdirect.perkinelmer.cloud/js/sample/index.html</a> (accessed on 28 August 2024).</p>
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<p>Inhibition (red cross) and induction (green upvote arrow) of transporters and metabolization enzymes of co-administered drugs (yellow ball) by ritonavir.</p>
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3 pages, 1529 KiB  
Interesting Images
Transforming Lung Cancer Management: A Promising Case Study of Immune Checkpoint Inhibitor Success Following a Multidisciplinary Approach
by Tadashi Nishimura, Hajime Fujimoto, Takumi Fujiwara, Tomohito Okano, Taro Yasuma, Esteban C. Gabazza, Hidenori Ibata and Tetsu Kobayashi
Diagnostics 2024, 14(19), 2159; https://doi.org/10.3390/diagnostics14192159 - 28 Sep 2024
Viewed by 788
Abstract
A 54-year-old female patient diagnosed with Stage IIIb squamous cell carcinoma (cT2aN3M0) initially received chemoradiotherapy. Two years after initial treatment, cancer relapse led to the administration of nivolumab, which was halted due to the development of drug-induced pneumonitis. Subsequent management with prednisolone and [...] Read more.
A 54-year-old female patient diagnosed with Stage IIIb squamous cell carcinoma (cT2aN3M0) initially received chemoradiotherapy. Two years after initial treatment, cancer relapse led to the administration of nivolumab, which was halted due to the development of drug-induced pneumonitis. Subsequent management with prednisolone and eight different cytotoxic agents failed to prevent metastasis to the cervical lymph nodes. The tumor’s programmed death-ligand 1 (PD-L1) expression rate was recorded at 10%. Four years after her diagnosis, the patient received a ninth-line therapy combining cisplatin, gemcitabine, and necitumumab, followed by palliative neck radiation due to increasing lymph node size. Remarkable tumor regression occurred three months after introducing atezolizumab as the tenth-line treatment, suggesting that previous treatments, particularly radiotherapy and cisplatin, might have enhanced PD-L1 expression, aligning with the existing literature. This case highlights the urgent need for further research to elucidate the intricate interplay between treatment history and PD-L1 expression in squamous cell carcinoma, emphasizing the importance of accumulating case studies to inform therapeutic strategies. Full article
(This article belongs to the Section Medical Imaging and Theranostics)
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<p>Rapid and marked reduction in right cervical lymph node size post-atezolizumab therapy. Cancer treatment typically adheres to evidence-based protocols established in international clinical guidelines. Despite this, progression can occur even with strict adherence to recommended therapies. We present a challenging case of a 54-year-old female diagnosed with Stage IIIb squamous cell carcinoma (cT2aN3M0), initially managed with chemoradiotherapy ((<b>A</b>); 1st-line therapy). Following a relapse two years later, nivolumab was administered ((<b>A</b>); 2nd-line therapy). Unfortunately, the emergence of drug-induced pneumonitis necessitated the discontinuation of nivolumab and the initiation of prednisolone therapy. Despite subsequent treatment with eight different cytotoxic anticancer agents ((<b>A</b>); 3rd- to 7th-line therapy), metastasis to the cervical lymph nodes continued to progress ((<b>B</b>,<b>C</b>), red arrow indicates the tumor). Genomic profiling of cervical lymph node samples via next-generation sequencing (FoundationOne<sup>®</sup>, Foundation Medicine, Inc., Cambridge, MA, USA revealed amplifications of fibroblast growth factors (FGF)3, FGF4, FGF19, KIT, platelet-derived growth factor receptor alpha (PDGFRA), Cyclin D1 (CCND1), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), and SRY-box transcription factor 2 (SOX2), along with a tumor mutational burden (TMB) of 11.35 Muts/Mb. Programmed death-ligand 1 (PD-L1) expression, assessed using the 22C3 assay, was found to be 10%. Four years post-diagnosis, the eighth-line therapy (<b>A</b>) comprising cisplatin, gemcitabine, and necitumumab was initiated. However, due to the enlargement of the right cervical lymph node, palliative neck radiation therapy consisted of 2 Gy/day for 10 days; in total, 20 Gy was administered (<b>A</b>). Radiation was delivered with fractionation over 10 days, rather than 5, to avoid potential bleeding from repeated irradiation due to the overlap in the areas receiving 60 Gy in both 2015 and 2022 [<a href="#B1-diagnostics-14-02159" class="html-bibr">1</a>]. Remarkably, three months later, the administration of atezolizumab as the ninth-line therapy (<b>A</b>) resulted in a significant reduction in tumor size (<b>D</b>–<b>F</b>). Two significant observations emerged from the patient’s treatment trajectory. Initially, while nivolumab induced pneumonitis, atezolizumab did not precipitate this adverse event, suggesting a variance in the side effect profiles of anti-PD-L1 and anti-PD-1 therapies [<a href="#B2-diagnostics-14-02159" class="html-bibr">2</a>]. Secondly, despite modest PD-L1 expression and tumor mutational burden (TMB), the patient experienced long-term efficacy with the immune checkpoint inhibitor. Notably, she had undergone irradiation and cisplatin treatment just before the administration of immune checkpoint inhibitors. The existing literature suggests that both cisplatin and irradiation may enhance PD-L1 expression, potentially explaining the late-line success observed in this case, mirroring other documented instances [<a href="#B3-diagnostics-14-02159" class="html-bibr">3</a>,<a href="#B4-diagnostics-14-02159" class="html-bibr">4</a>]. Growing evidence supports the combination of radiotherapy with immune checkpoint inhibitor therapy [<a href="#B4-diagnostics-14-02159" class="html-bibr">4</a>,<a href="#B5-diagnostics-14-02159" class="html-bibr">5</a>,<a href="#B6-diagnostics-14-02159" class="html-bibr">6</a>], as indicated by similar case reports [<a href="#B7-diagnostics-14-02159" class="html-bibr">7</a>], although others suggest caution [<a href="#B8-diagnostics-14-02159" class="html-bibr">8</a>]. This case exemplifies the unpredictable and individual nature of cancer treatment outcomes and highlights the importance of personalized medicine. It also underlines the potential of revisiting previously failed therapies under altered physiological contexts, such as changes in PD-L1 expression induced by specific treatments. While conclusions drawn from a single case are inherently limited, this patient’s experience offers valuable insights into the complex dynamics between cancer biology, treatment history, and therapeutic response. It underscores the critical need for ongoing research and the accumulation of detailed case studies to enhance our understanding of how treatments can be optimized based on individual patient profiles, providing hope and potentially life-extending options for those facing advanced cancers.</p>
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Article
Comparative Efficacy of 21 Treatment Strategies for Resectable Pancreatic Cancer: A Network Meta-Analysis
by Fausto Petrelli, Roberto Rosenfeld, Antonio Ghidini, Andrea Celotti, Lorenzo Dottorini, Matteo Viti, Gianluca Baiocchi, Ornella Garrone, Gianluca Tomasello and Michele Ghidini
Cancers 2024, 16(18), 3203; https://doi.org/10.3390/cancers16183203 - 20 Sep 2024
Viewed by 790
Abstract
The primary treatment for operable pancreatic cancer (PC) involves surgery followed by adjuvant therapy. Nevertheless, perioperative or neoadjuvant chemotherapy (CT) may be used to mitigate the likelihood of recurrence and mortality. This network meta-analysis (NMA) assesses the comparative efficacy of various treatment approaches [...] Read more.
The primary treatment for operable pancreatic cancer (PC) involves surgery followed by adjuvant therapy. Nevertheless, perioperative or neoadjuvant chemotherapy (CT) may be used to mitigate the likelihood of recurrence and mortality. This network meta-analysis (NMA) assesses the comparative efficacy of various treatment approaches for resectable PC. A thorough search was carried out on January 31, 2023, encompassing PubMed/MEDLINE, Cochrane Library, and Embase databases. We incorporated randomized clinical trials (RCTs) that compared surgical interventions with or without (neo)adjuvant or perioperative therapies for operable PC. We conducted a fixed-effects Bayesian NMA. We presented the effect sizes in terms of hazard ratios (HRs) for overall survival (OS) along with 95% credible intervals (95% CrIs). The treatment was deemed statistically superior when the 95% credible interval (CrI) did not encompass a null value (hazard ratio < 1). Treatment rankings were established based on the surface under the cumulative ranking curve (SUCRA). A total of 24 studies were incorporated, comparing 21 treatments with surgery in isolation. Eleven treatments showed superior efficacy compared to surgery alone, with HRs ranging from 0.38 for perioperative treatments to 0.73 for adjuvant 5-fluorouracil. After the exclusion of studies conducted in Asia, it was found that the perioperative regimen of gemcitabine combined with nab-paclitaxel was the most effective regimen (SUCRA, p = 0.99). The findings endorse the utilization of perioperative CT, especially multi-agent CT, as the favored intervention for operable PC in Western nations. Full article
(This article belongs to the Special Issue Multimodal Treatment for Pancreatic Cancer)
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<p>Flow diagram of included studies.</p>
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<p>Network of direct treatment comparisons in operable pancreatic cancer. S, surgery; CT, chemotherapy; RT, radiotherapy; CDDP, cisplatin; GEM, gemcitabine; FFOX, FOLFIRINOX; CAPE, capecitabine; GNABP, gemcitabine + nab-paclitaxel; PEXG, cisplatin + epirubicin + capecitabine + gemcitabine; 5FU, 5-fluorouracil; IFN, interferon; ERLO, erlotinib; UFT, tegafur–uracil; &gt;, followed by.</p>
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<p>Forest plot of various treatment arms compared with surgery alone arm. S, surgery; CT, chemotherapy; RT, radiotherapy; CDDP, cisplatin; GEM, gemcitabine; FFOX, FOLFIRINOX; CAPE, capecitabine; GNABP, gemcitabine + nab-paclitaxel; PEXG, cisplatin + epirubicin + capecitabine + gemcitabine; 5FU, 5-fluorouracil; IFN, interferon; ERLO, erlotinib; UFT, tegafur–uracil; &gt;, followed by.</p>
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