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Ovarian Cancer: Advances on Pathophysiology and Therapies (2nd Edition)

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Oncology".

Deadline for manuscript submissions: 20 April 2025 | Viewed by 2810

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Special Issue Editor

Dr. Giovanni Tossetta

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Department of Experimental and Clinical Medicine, Polytechnic University of Marche, Via Tronto, 10/a, 60126 Ancona, Italy
Interests: pregnancy complications; preeclampsia; preterm birth; ovarian cancer; early marker of pregnancy complications; oxidative stress; chemoresistance
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Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit a manuscript to the Special Issue “Ovarian Cancer: Advances on Pathophysiology and Therapies 2.0”. The incidence of ovarian cancer has increased significantly over the past 50 years. Despite advances in medical tumor therapy, the occurrence of chemoresistance and metastatic disease is a common cause of death in patients with ovarian cancer. Thus, it is necessary to develop new therapeutic approaches that can improve diagnosis and treatment outcomes. To this aim, we need a better understanding of the molecular changes occurring in ovarian cancer and the development of molecular biomarkers able to predict tumor behavior and risk of disease recurrence and chemoresistance.

Topics will include (but are not limited to):

Pathogenesis of ovarian cancer
Diagnostic and prognostic molecular markers
Molecular mechanism of cancer onset and progression
Novel treatments of ovarian cancer
Ovarian cancer prevention.

This Special Issue of IJMS, therefore, welcomes original research articles and reviews related to Ovarian Cancer. Communications, mini-reviews, systematic reviews and meta-analyses are also welcome.

We look forward to receiving your contributions.

Dr. Giovanni Tossetta
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

ovarian cancer
chemoresistance
therapy
pathogenesis
marker
diagnosis

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Ovarian Cancer: Advances on Pathophysiology and Therapies in International Journal of Molecular Sciences (10 articles)

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19 pages, 3653 KiB

Open AccessArticle

Metformin Impairs Linsitinib Anti-Tumor Effect on Ovarian Cancer Cell Lines

by Diana Luísa Almeida-Nunes, João P. N. Silva, Mariana Nunes, Patrícia M. A. Silva, Ricardo Silvestre, Ricardo Jorge Dinis-Oliveira, Hassan Bousbaa and Sara Ricardo

Int. J. Mol. Sci. 2024, 25(22), 11935; https://doi.org/10.3390/ijms252211935 - 6 Nov 2024

Viewed by 558

Abstract

Ovarian cancer (OC) remains one of the leading causes of cancer-related mortality among women. Targeting the insulin-like growth factor 1 (IGF-1) signaling pathway has emerged as a promising therapeutic strategy. Linsitinib, an IGF-1 receptor (IGF-1R) inhibitor, has shown potential in disrupting this pathway. Additionally, metformin, commonly used in the treatment of type 2 diabetes, has been studied for its anti-cancer properties due to its ability to inhibit metabolic pathways that intersect with IGF-1 signaling, making it a candidate for combination therapy in cancer treatments. This study explores the anti-cancer effects of linsitinib and metformin on OVCAR3 cells by the suppression of the IGF-1 signaling pathway by siRNA-mediated IGF-1 gene silencing. The goal is to evaluate their efficacy as therapeutic agents and to emphasize the critical role of this pathway in OC cell proliferation. Cellular viability was evaluated by resazurin-based assay, and apoptosis was assessed by flux cytometry. The results of this study indicate that the combination of linsitinib and metformin exhibits an antagonistic effect (obtained by SynergyFinder 2.0 Software), reducing their anti-neoplastic efficacy in OC cell lines. Statistical analyses were performed using ordinary one-way or two-way ANOVA, followed by Tukey’s or Šídák’s multiple comparison tests. While linsitinib shows promise as a therapeutic option for OC, further research is needed to identify agents that could synergize with it to enhance its therapeutic efficacy, like the combination with standard chemotherapy in OC (carboplatin and paclitaxel). Full article

(This article belongs to the Special Issue Ovarian Cancer: Advances on Pathophysiology and Therapies (2nd Edition))

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Figure 1

Figure 1
The insulin-like growth factor 1 signaling pathway. Insulin-like growth factor 1 (IGF-1) activates both phosphatidylinositol 3-kinase/Akt and Ras/mitogen-activated protein kinase pathways, resulting in cell proliferation, increased protein synthesis, and cell growth. Phosphatidylinositol 3-kinase/Akt activates nuclear factor-κB and MDM2 for cell survival and inhibits apoptosis through inhibition of BAD and FKHR. Akt—Ak strain transforming; BAD—BCL2-associated agonist of cell death; Erk—extracellular-signal-regulated kinase; FKHR—Forkhead transcription factor FOXO1; IGF-I—insulin-like growth factor 1; IGF-IR—insulin-like growth factor 1 receptor; IGFBP—insulin-like growth factor binding protein; IRSs—insulin receptor substrate proteins; MDM2—mouse double minute 2; MEK—mitogen-activated protein kinase; mTOR—mammalian target of rapamycin; NFκB—nuclear factor immunoglobulin κ chain enhancer-B cell; P—phosphate; PI3K—phosphatidylinositol 3-kinase; PIP2—phosphatidylinositol 3, 4 phosphates; PIP3—phosphatidylinositol 3, 4, 5 phosphates; Raf—rapidly accelerated fibrosarcoma; Ras—rat sarcoma; SHC—Src homology/collagen. Figure created in <a href="http://BioRender.com" target="_blank">BioRender.com</a>. Full article ">Figure 2
IGF-1 gene expression in ovarian cell lines. Bar chart showing relative IGF-1 mRNA expression levels in HOSE6.3, OVCAR3, OVCAR8, and OVCAR8 PTX R P cell lines determined by qRT-PCR with β-Actin and GAPDH used as housekeeping genes. The assays were carried out in triplicate in at least three independent experiments. Data are expressed as mean ± standard error of mean deviation (SEM) and plotted using GraphPad Prism Software Inc., San Diego, CA, USA v9. Statistical analysis was performed using ordinary one-way ANOVA followed by Šídák’s multiple comparison test, and values of **** < 0.0001 were considered statistically significant. Full article ">Figure 3
Silencing of IGF-1 gene in OVCAR3 cell line. (a) Bar chart showing relative IGF-1 mRNA expression levels in OVCAR3, OVCAR3 transfected with siRNA control (OVCAR3 siNEG), and OVCAR3 transfected with siRNA of IGF-1 (OVCAR3 siIGF-1) determined by qRT-PCR. β-Actin and GAPDH were used as housekeeping genes. (b) Representative Western blot showing IGF-1 protein expression in HOSE6.3, OVCAR3, OVCAR3 siNEG, and OVCAR3 siIGF-1 cell lines. α-tubulin was used as a loading control. (c) Bar chart showing relative IGF-1 protein expression levels in HOSE6.3, OVCAR3, OVCAR3 siNEG, and OVCAR3 siIGF-1 determined by ImageJ 1.4v software. α-tubulin intensity levels were used as a control. The assays were carried out in triplicate in at least three independent experiments. Data are expressed as mean ± standard error of mean deviation (SEM) and plotted using GraphPad Prism Software Inc. v9. Statistical analysis was performed using ordinary one-way ANOVA followed by Šídák’s multiple comparison test and values of * < 0.05 and ** < 0.001 were considered statistically significant. Full article ">Figure 4
Dose–response curves for HOSE6.3 and OVCAR3 of drugs linsitinib and metformin. (a) Dose–response curves for HOSE6.3 and OVCAR3 cells were obtained by Presto Blue assay after exposure to increasing concentrations of linsitinib (780 to 100,000 nM) for 48 h. (b) Dose–response curves for HOSE6.3 and OVCAR3 cells were obtained by Presto Blue assay after exposure to increasing concentrations of metformin (80 to 10,000 μM) for 48 h. IC50 values are represented by a dotted line in each dose–response curve and are mentioned below. The assays were carried out in triplicate in at least three independent experiments. Data are expressed as mean ± standard error of mean deviation (SEM) and plotted using GraphPad Prism Software Inc. v9. Full article ">Figure 5
Linsitinib demonstrates high efficacy in reducing the cellular viability of OVCAR3 and OVCAR3 siIGF-1. (a) Bar charts showing cell viability of OVCAR3 cells obtained by Presto Blue assay after exposure to a fixed-dose ratio of linsitinib combined with metformin. (b) Bar charts showing cell viability of OVCAR3 siIGF-1 cells obtained by Presto Blue assay after exposure to a fixed-dose ratio of linsitinib combined with metformin. All assays were performed in triplicate in at least three independent experiments. Data are expressed as mean ± standard deviation and plotted using GraphPad Prism Software Inc. v9. Statistical analysis was performed using ordinary two-way ANOVA followed by Šidák’s multiple comparison test, and values of * < 0.05, ** < 0.001, *** < 0.005, and **** < 0.0001 were considered statistically significant. Full article ">Figure 6
Stain with Annexin V/PI and analyzed by flow cytometry to confirm the cellular viability using the drugs linsitinib and metformin in the OVCAR3 and OVCAR3 siIGF-1 cells. (a) Representative flow cytometry histogram of propidium iodide (PI) versus annexin V (FITC-A) intensity in OVCAR3 and OVCAR3 siIGF-1 before (control–DMSO) and after exposure to metformin (500 µM), linsitinib (35 µM), and the combination of both drugs, during 48 h. DMSO was used as a control. The quadrants Q were defined as Q1 = live cells (Annexin V-negative/PI-negative), Q1-LR = early stage of apoptosis (Annexin V-positive/PI-negative), Q1-UL = late stage of apoptosis (Annexin V-positive/PI-positive), and Q1-UL = necrosis (Annexin V-negative/PI-positive). (b) Bar charts showing the percentage of Annexin V-positive cells (early and late stage of apoptosis) to the different conditions of OVCAR3 and OVCAR3 siIGF-1. The assays were carried out in triplicate in at least three independent experiments. Data are expressed as mean ± standard error of mean deviation (SEM) and plotted using GraphPad Prism Software Inc. v9. Statistical analysis was performed using ordinary one-way ANOVA followed by Šídák’s multiple comparison test and values of ** < 0.001, *** < 0.005, and **** < 0.0001 were considered statistically significant. Full article ">Figure 6 Cont.
Stain with Annexin V/PI and analyzed by flow cytometry to confirm the cellular viability using the drugs linsitinib and metformin in the OVCAR3 and OVCAR3 siIGF-1 cells. (a) Representative flow cytometry histogram of propidium iodide (PI) versus annexin V (FITC-A) intensity in OVCAR3 and OVCAR3 siIGF-1 before (control–DMSO) and after exposure to metformin (500 µM), linsitinib (35 µM), and the combination of both drugs, during 48 h. DMSO was used as a control. The quadrants Q were defined as Q1 = live cells (Annexin V-negative/PI-negative), Q1-LR = early stage of apoptosis (Annexin V-positive/PI-negative), Q1-UL = late stage of apoptosis (Annexin V-positive/PI-positive), and Q1-UL = necrosis (Annexin V-negative/PI-positive). (b) Bar charts showing the percentage of Annexin V-positive cells (early and late stage of apoptosis) to the different conditions of OVCAR3 and OVCAR3 siIGF-1. The assays were carried out in triplicate in at least three independent experiments. Data are expressed as mean ± standard error of mean deviation (SEM) and plotted using GraphPad Prism Software Inc. v9. Statistical analysis was performed using ordinary one-way ANOVA followed by Šídák’s multiple comparison test and values of ** < 0.001, *** < 0.005, and **** < 0.0001 were considered statistically significant. Full article ">Figure 7
Combining linsitinib with metformin has an antagonist effect on OVCAR3 and OVCAR3 siIGF-1 cells. (a) ZIP, Bliss Independence, Loewe, and High Single Agent (HSA) synergy 2D and 3D plots showing drug antagonism of OVCAR3 cells after exposure to a fixed-dose ratio of linsitinib and metformin for 48 h. (b) ZIP, Bliss Independence, Loewe, and HSA synergy 2D and 3D plots showing drug antagonism of OVCAR3 siIGF-1 cells after exposure to a fixed-dose ratio of linsitinib and metformin for 48 h. The combined treatment was co-administered at the same time. All assays were performed in triplicate in at least three independent experiments. Synergy score: <10 (antagonism, green), =1 (additivity, white), and >10 (synergism, red). Full article ">Figure 8
Schematic representation of metformin’s possible interaction with linsitinib. The most common pathway involves the activation of AMPK, which regulates energy metabolism by modulating complex 1 of the respiratory chain in mitochondria by changes in the AMP/ATP ratio, which inhibits Akt and mTOR. Metformin binds with IGF-1 and modulates pathways involved in tumor progression. Upon binding, metformin inhibits the PI3K/Akt/mTOR and Ras/Raf/ERK pathways, leading to reductions in cell proliferation, thereby causing tumor cell death. Metformin, through AMPK activation and mTOR inhibition, could increase glucose uptake and glycolysis and have better efficiency in low-glucose media. The arrows ↑ ↓ indicate upregulation and downregulation, respectively. The drug linsitinib blocks IGF-1R (represented by the red *), which helps to block the IGF-1 signaling pathway. ADP—adenosine diphosphate; Akt—Ak strain transforming; AMP—adenosine monophosphate; AMPK—adenosine monophosphate-activated protein kinase; ATP—adenosine triphosphate; Erk—extracellular-signal-regulated kinase; IGF-I—insulin-like growth factor 1; IGF-IR—insulin-like growth factor 1 receptor; MEK—mitogen-activated protein kinase; mTOR—mammalian target of rapamycin; PI3K—phosphatidylinositol 3-kinase; Raf—rapidly accelerated fibrosarcoma; Ras—rat sarcoma. Figure created in <a href="http://BioRender.com" target="_blank">BioRender.com</a>. Full article ">

12 pages, 5657 KiB

Open AccessArticle

Myo-Inositol and D-Chiro-Inositol Reduce DHT-Stimulated Changes in the Steroidogenic Activity of Adult Granulosa Cell Tumors

by Anna Maria Wojciechowska, Paulina Zając, Justyna Gogola-Mruk, Magdalena Karolina Kowalik and Anna Ptak

Int. J. Mol. Sci. 2024, 25(20), 10974; https://doi.org/10.3390/ijms252010974 - 12 Oct 2024

Viewed by 931

Abstract

Considering the properties of myo-inositol (MI) and D-chiro-inositol (DCI), which are well known in polycystic ovary syndrome therapy, and the limitations of adult granulosa cell tumor (AGCT) treatment, especially for androgen-secreting tumors, we studied the role of MI and DCI in the androgen-rich environment of AGCTs. For this purpose, we analyzed the mRNA expression of steroidogenic genes and the secretion of progesterone (P4) and 17β-estradiol (E2) in an unstimulated and/or dihydrotestosterone (DHT)-stimulated environment under MI and DCI influence. Thus, we used the HGrC1 and KGN cell lines as in vitro models of healthy and cancerous granulosa cells. We found that DHT, the most potent androgen, increased E2 secretion and steroidogenic acute regulatory protein (StAR) and cytochrome P450 side-chain cleavage gene (CYP11A1) mRNA expression without affecting 450 aromatase (CYP19A1) in AGCTs. However, after the MI and DCI treatment of KGN cells, both compounds strongly reduced StAR and CYP11A1 expression. Interestingly, in DHT-stimulated KGN cells, only DCI alone and its cotreatment with MI reduced both CYP11A1 mRNA and E2 secretion. These findings suggest that CYP11A1 is responsible for the antiestrogenic effect of DCI in the androgen-rich environment of AGCTs. Therefore, MI and DCI could be used as effective agents in the adjuvant treatment of AGCT, but further detailed studies are needed. Full article

(This article belongs to the Special Issue Ovarian Cancer: Advances on Pathophysiology and Therapies (2nd Edition))

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Figure 1

Figure 1
Basal mRNA (A) and protein (B) expression of AR and mRNA expression of SRD5A1 (C) in HGrC1 and KGN cells. The relative expression (RQ) of HGrC1 was set to 1. The data represent the mean ± SEM of three independent experiments. ** p < 0.01, *** p < 0.0001. Full article ">Figure 2
Effect of DHT at concentrations of 1, 50, 100, 150, and 200 ng/mL on the viability of HGrC1 (A) and KGN (B) cells after 48 h of incubation. The data represent the mean ± SEM of three independent experiments. Ctrl, control. * p < 0.05. Full article ">Figure 3
Lipid content in HGrC1 and KGN cells stained with Nile Red dye, scale bar = 100 µm (A) and basal mRNA expression of StAR (B), CYP11A1 (C), 3β-HSD (D), and CYP19A1 (E) in both cell lines. The relative expression (RQ) of HGrC1 was set to 1. The data represent the mean ± SEM of three independent experiments. ** p < 0.01, **** p < 0.0001. Full article ">Figure 4
Effects of DHT (500 nM) on StAR (A), CYP11A1 (B), and CYP19A1 (C) mRNA expression as well as P4 (D) and E2 (E) secretion and lipid content determined via Nile Red staining, scale bar = 50 µm (F) after incubation of KGN cells for 24 h. The relative expression (RQ) of the Ctrl was set to 1. The data represent the mean ± SEM of three or four independent experiments. Ctrl, control. * p < 0.05, *** p < 0.001. Full article ">Figure 5
Effects of MI (0.01, 0.1, 1, 2.5, 5, and 10 mM) and DCI (0.2, 2, 20, 200, and 2000 nM) on the viability of HGrC1 (A,B) and KGN (C,D) cells after 48 h of incubation. The data represent the mean ± SEM of three independent experiments. Ctrl, control. Full article ">Figure 6
Effects of MI (1 mM) and DCI (20 nM), added alone or together, on StAR (A), CYP11A1 (B), and CYP19A1 (C) mRNA expression after incubation of KGN for 24 h. The relative expression (RQ) of the Ctrl was set to 1. The data represent the mean ± SEM of three independent experiments. Ctrl, control. * p < 0.05, ** p < 0.01. Full article ">Figure 7
Effects of MI (1 mM) and DCI (20 nM), added alone or together, on StAR (A), CYP11A1 (B), and CYP19A1 (C) mRNA expression; P4 (D) and E2 secretion (E); and cell viability (F) in DHT-stimulated KGN cells after incubation for 24 h. The relative expression (RQ) of DHT was set to 1. The data represent the mean ± SEM of three or four independent experiments. DHT, dihydrotestosterone used as a control. * p < 0.05, ** p < 0.01. Full article ">

18 pages, 1814 KiB

Open AccessArticle

Analysis of ATP7A Expression and Ceruloplasmin Levels as Biomarkers in Patients Undergoing Neoadjuvant Chemotherapy for Advanced High-Grade Serous Ovarian Carcinoma

by David Lukanović, Sara Polajžer, Miha Matjašič, Borut Kobal and Katarina Černe

Int. J. Mol. Sci. 2024, 25(18), 10195; https://doi.org/10.3390/ijms251810195 - 23 Sep 2024

Viewed by 867

Abstract

Ovarian cancer (OC), particularly high-grade serous carcinoma (HGSC), is a leading cause of gynecological cancer mortality due to late diagnosis and chemoresistance. While studies on OC cell lines have shown that overexpression of the ATP7A membrane transporter correlates with resistance to platinum-based drugs (PtBMs) and cross-resistance to copper (Cu), clinical evidence is lacking. The functionality of ceruloplasmin (CP), the main Cu-transporting protein in the blood, is dependent on, among other things, ATP7A activity. This study investigated ATP7A expression and CP levels as potential biomarkers for predicting responses to PtBMs. We included 28 HGSC patients who underwent neoadjuvant chemotherapy (NACT). ATP7A expression in ovarian and peritoneal tissues before NACT and in peritoneal and omental tissues after NACT was analyzed via qPCR, and CP levels in ascites and plasma were measured via ELISA before and after NACT. In total, 54% of patients exhibited ATP7A expression in pretreatment tissue (ovary and/or peritoneum), while 43% of patients exhibited ATP7A expression in tissue after treatment (peritoneum and/or omentum). A significant association was found between higher ATP7A expression in the peritoneum before NACT and an unfavorable CA-125 elimination rate constant k (KELIM) score. Patients with omental ATP7A expression had significantly higher plasma mean CP levels before NACT. Plasma CP levels decreased significantly after NACT, and higher CP levels after NACT were associated with a shorter platinum-free interval (PFI). These findings suggest that the ATP7A transporter and CP have the potential to serve as predictive markers of chemoresistance, but further research is needed to validate their clinical utility. Full article

(This article belongs to the Special Issue Ovarian Cancer: Advances on Pathophysiology and Therapies (2nd Edition))

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