CN118086505A - Mesothelioma prognosis prediction method, molecular marker and kit and application thereof - Google Patents
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Abstract
The invention provides a mesothelioma prognosis prediction method, a molecular marker, a kit and application thereof, wherein the method comprises the following steps: (1) Extracting RNA from the sample, and converting the RNA into cDNA by using reverse transcriptase; (2) Amplifying and quantifying the cDNA obtained in the step (1) by using a primer and SYBR Green; (3) Detecting the expression levels of CARS1, CDKN1A, TFRC, FANCD2, FDFT1, HSPB1, SLC1A5, SLC7A11 and DPP4 through the step (2), and obtaining corresponding expression signals; (4) The mesothelioma prognosis prediction risk score is taken as a mesothelioma prognosis prediction result and is marked as Y, and the relation between Y and the expression signal in the step (3) is ：Y=0.6976×CARS1+0.3167×CDKN1A+0.0887×TFRC+0.8286×FANCD2+0.1581×FDFT1+0.4534×HSPB1+0.2378×SLC1A5+（-0.0554）×SLC7A11+（-0.0214）×DPP4.. The mesothelioma prognosis prediction method, the molecular marker and the kit have good universality, high accuracy and sensitivity, and the AUC of 1 year, 3 years and 5 years of prediction prognosis is more than 0.7.
Description
Technical Field
The invention relates to the field of tumor marker genes, in particular to a mesothelioma prognosis prediction method, a molecular marker, a kit and application thereof.
Background
Mesothelioma is a fatal cancer derived from serosa of multiple compartments of the body, and the involved areas include pleura, pericardium, testes and their accessory structures. The specific pathological mechanism of mesothelioma development is not yet clear. The prognosis is extremely poor, with an average survival of about one year from the first appearance of symptoms. There is therefore a need for methods and markers for mesothelioma prediction.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a mesothelioma prognosis prediction method, a molecular marker, a kit and application thereof.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a method of mesothelioma prognosis prediction, the method comprising the steps of:
(1) Extracting RNA from the sample, and converting the RNA into cDNA by using reverse transcriptase;
(2) Amplifying and quantifying the cDNA obtained in the step (1) by using a primer and SYBR Green;
(3) Detecting the expression levels of CARS1, CDKN1A, TFRC, FANCD2, FDFT1, HSPB1, SLC1A5, SLC7A11 and DPP4 through the step (2), and obtaining corresponding expression signals;
(4) The mesothelioma prognosis risk score is noted as Y as a result of mesothelioma prognosis prediction, and the relation of Y and the expression signal in step (3) is that ：Y=0.6976×CARS1+0.3167×CDKN1A+0.0887×TFRC+0.8286×FANCD2+0.1581×FDFT1+0.4534×HSPB1+0.2378×SLC1A5+（-0.0554）×SLC7A11+（-0.0214）×DPP4.
Iron death is a regulated cell death mechanism dependent on iron ions and is characterized by disturbances in cellular iron homeostasis and abnormal accumulation of lipid peroxides. In recent years, a number of important studies have revealed a key role for iron death in a variety of pathological processes, in particular its contribution to proliferation and metastasis of malignant cells. These results suggest that iron death plays an important role in the development and progression of malignancy and can be an independent factor in predicting patient prognosis. However, there has been no study of the relationship between mesothelioma and iron death genes, and there are a large variety of iron death genes, whether they can be used as markers for mesothelioma, and which of the several iron death genes are related to mesothelioma, further study is required. To obtain the above mesothelioma prognosis prediction method, 24 iron death modulators were studied to be classified as key regulatory genes in the field of iron death, given their crucial role in monitoring the complexity of the iron death process. In order to identify iron death regulatory factors with obvious prognosis information, we perform univariate Cox regression analysis, find nine genes with prognostic importance, and nine genes are related to each other, nine genes are needed to be used as molecular markers for mesothelioma prognosis prediction to be combined so as to more accurately predict mesothelioma prognosis, lack of any one gene marker can lead to inaccuracy of prediction, nine predictive iron death regulatory factors are used for constructing a prognostic gene model ,Y=0.6976×CARS1+0.3167×CDKN1A+0.0887×TFRC+0.8286×FANCD2+0.1581×FDFT1+0.4534×HSPB1+0.2378×SLC1A5+（-0.0554）×SLC7A11+（-0.0214）×DPP4. by using multifactor Cox iterative regression, so that the mesothelioma prognosis prediction by using the iron death genes as the molecular markers is realized for the first time, and the marker combination with the best prediction accuracy is screened, so that the method has good universality, higher accuracy and sensitivity, and the AUC of 1 year, 3 year and 5 year prognosis is larger than 0.7.
Preferably, the primer is SEQ ID No:1、SEQ ID No:2、SEQ ID No:3、SEQ ID No:4、SEQ ID No:5、SEQ ID No:6、SEQ ID No:7、SEQ ID No:8、SEQ ID No:9、SEQ ID No:10、SEQ ID No:10、SEQ ID No:12、SEQ ID No:13、SEQ ID No:14、SEQ ID No:15、SEQ ID No:16、SEQ ID No:17、SEQ ID No:18;
The sequences of the primers are shown in Table 1 below:
| Primer | Sequence(3’-5’) | SEQ ID No: |
| CARS1-F | GGTGACGTGGTATTGCTGTG | 1 |
| CARS1-R | CTCTTCTCCCGATACTGCTCG | 2 |
| CDKN1A-F | TGTCCGTCAGAACCCATGC | 3 |
| CDKN1A-R | AAAGTCGAAGTTCCATCGCTC | 4 |
| TFRC-F | ACCATTGTCATATACCCGGTTCA | 5 |
| TFRC-R | CAATAGCCCAAGTAGCCAATCAT | 6 |
| FANCD2-F | ACATACCTCGACTCATTGTCAGT | 7 |
| FANCD2-R | TCGGAGGCTTGAAAGGACATC | 8 |
| FDFT1-F | CCACCCCGAAGAGTTCTACAA | 9 |
| FDFT1-R | TGCGACTGGTCTGATTGAGATA | 10 |
| HSPB1-F | ACGGTCAAGACCAAGGATGG | 11 |
| HSPB1-R | AGCGTGTATTTCCGCGTGA | 12 |
| SLC1A5-F | TCATGTGGTACGCCCCTGT | 13 |
| SLC1A5-R | GCGGGCAAAGAGTAAACCCA | 14 |
| SLC7A11-F | TCTCCAAAGGAGGTTACCTGC | 15 |
| SLC7A11-R | AGACTCCCCTCAGTAAAGTGAC | 16 |
| DPP4-F | TACAAAAGTGACATGCCTCAGTT | 17 |
| DPP4-R | TGTGTAGAGTATAGAGGGGCAGA | 18 |
。
Preferably, the prognosis prediction method is used for predicting prognosis for 1-5 years.
The prognosis prediction method has good universality, higher accuracy and sensitivity for predicting the predicted AUC of 1 year, 3 years and 5 years to be more than 0.7.
The invention also provides a molecular marker for mesothelioma prognosis, the biomarker being an iron death-related gene comprising: CARS1, CDKN1A, TFRC, FANCD2, FDFT1, HSPB1, SLC1A5, SLC7a11 and DPP4.
Iron death plays an important role in the development and progression of malignancy and can be an independent factor in predicting patient prognosis. However, there has been no study of the relationship between mesothelioma and iron death genes, and there are a large variety of iron death genes, whether they can be used as markers for mesothelioma, and which of the several iron death genes are related to mesothelioma, further study is required. The study classified 24 iron death modulators as key regulatory genes in the iron death field, given their crucial role in monitoring the complexity of the iron death process. In order to identify iron death regulatory factors with obvious prognosis information, we perform univariate Cox regression analysis, find nine genes with prognostic importance, and nine genes are related to each other, nine genes are needed to be used as molecular markers for mesothelioma prognosis prediction to be combined so as to more accurately predict mesothelioma prognosis, lack of any one gene marker can lead to inaccuracy of prediction, nine predictive iron death regulatory factors are used for constructing a prognostic gene model ,Y=0.6976×CARS1+0.3167×CDKN1A+0.0887×TFRC+0.8286×FANCD2+0.1581×FDFT1+0.4534×HSPB1+0.2378×SLC1A5+（-0.0554）×SLC7A11+（-0.0214）×DPP4. by using multifactor Cox iterative regression, so that the mesothelioma prognosis prediction by using the iron death genes as the molecular markers is realized for the first time, and the marker combination with the best prediction accuracy is screened, so that the method has good universality, higher accuracy and sensitivity, and the AUC of 1 year, 3 year and 5 year prognosis is larger than 0.7.
Preferably, the mesothelioma prognosis prediction risk score is recorded as Y as a mesothelioma prognosis prediction result, and the relationship of Y and the expression signal of the molecular marker is that ：Y=0.6976×CARS1+0.3167×CDKN1A+0.0887×TFRC+0.8286×FANCD2+0.1581×FDFT1+0.4534×HSPB1+0.2378×SLC1A5+（-0.0554）×SLC7A11+（-0.0214）×DPP4.
The invention also provides a kit comprising a molecular marker for mesothelioma prognosis and a primer; the biomarker is an iron death-related gene comprising: CARS1, CDKN1A, TFRC, FANCD2, FDFT1, HSPB1, SLC1A5, SLC7a11 and DPP4; the sequences of the ：SEQ ID No:1、SEQ ID No:2、SEQ ID No:3、SEQ ID No:4、SEQ ID No:5、SEQ ID No:6、SEQ ID No:7、SEQ ID No:8、SEQ ID No:9、SEQ ID No:10、SEQ ID No:10、SEQ ID No:12、SEQ ID No:13、SEQ ID No:14、SEQ ID No:15、SEQ ID No:16、SEQ ID No:17、SEQ ID No:18; primers are shown in Table 1.
The kit is a mesothelioma prognosis model and a diagnosis kit which are built based on the iron death related genes, and can accurately predict the prognosis of mesothelioma patients.
Preferably, the kit further comprises SYBR Green and a reverse transcriptase.
Preferably, the kit further comprises instructions describing the method according to claims 1-3.
The invention has the beneficial effects that: the mesothelioma prognosis prediction method, the molecular marker and the kit have good universality, high accuracy and sensitivity, and AUC of 1 year, 3 years and 5 years of prognosis prediction is more than 0.7.
Drawings
FIG. 1 shows the prognostic value of molecular markers in mesothelioma in the mesothelioma prognosis method of the invention. Overall survival curves of CARS 1A, CDKN1A B, TFRC C, FANCD 2D, FDFT 1E, HSPB 1F, SLC A5G, SLC A11H and DPP 4I in high/low expression groups in mesothelioma patients.
FIG. 2 is a prognostic gene model constructed for the mesothelioma prognosis prediction method of the present invention.
Distribution of risk score, survival status and nine prognostic iron death modulator expression in mesothelioma. Overall survival curves and ROC curves measuring predictive value for B, C high/low risk inter-group dermatome patients.
FIG. 3 is a diagram showing the construction of predicted nomas in the mesothelioma prognosis method of the present invention. A, B considers the risk ratio and P-value of clinical parameters and iron death gene model risk scores in mesothelioma in univariate and multivariate Cox regression analysis.
Detailed Description
For a better description of the objects, technical solutions and advantages of the present invention, the present invention will be further described with reference to the following specific examples.
Example 1
As a mesothelioma prognosis prediction method of the embodiment of the invention, the method comprises the following steps:
(1) Extracting RNA from the sample, and converting the RNA into cDNA by using reverse transcriptase;
(2) Amplifying and quantifying the cDNA obtained in the step (1) by using a primer and SYBR Green;
(3) Detecting the expression levels of CARS1, CDKN1A, TFRC, FANCD2, FDFT1, HSPB1, SLC1A5, SLC7A11 and DPP4 through the step (2), and obtaining corresponding expression signals;
(4) The mesothelioma prognosis risk score is noted as Y as a result of mesothelioma prognosis prediction, and the relation of Y and the expression signal in step (3) is that ：Y=0.6976×CARS1+0.3167×CDKN1A+0.0887×TFRC+0.8286×FANCD2+0.1581×FDFT1+0.4534×HSPB1+0.2378×SLC1A5+（-0.0554）×SLC7A11+（-0.0214）×DPP4.
The sequence of the primer ：SEQ ID No:1、SEQ ID No:2、SEQ ID No:3、SEQ ID No:4、SEQ ID No:5、SEQ ID No:6、SEQ ID No:7、SEQ ID No:8、SEQ ID No:9、SEQ ID No:10、SEQ ID No:10、SEQ ID No:12、SEQ ID No:13、SEQ ID No:14、SEQ ID No:15、SEQ ID No:16、SEQ ID No:17、SEQ ID No:18; primer is shown in table 1;
the prognosis prediction method is used for predicting prognosis for 1-5 years.
Example 2
As a molecular marker for mesothelioma prognosis in an embodiment of the present invention, the biomarker is an iron death-related gene comprising: CARS1, CDKN1A, TFRC, FANCD2, FDFT1, HSPB1, SLC1A5, SLC7a11 and DPP4;
The mesothelioma prognosis risk score is recorded as Y as a mesothelioma prognosis result, and the relation between Y and the expression signal of the molecular marker is that ：Y=0.6976×CARS1+0.3167×CDKN1A+0.0887×TFRC+0.8286×FANCD2+0.1581×FDFT1+0.4534×HSPB1+0.2378×SLC1A5+（-0.0554）×SLC7A11+（-0.0214）×DPP4.
Example 3
As a kit of an embodiment of the present invention, the kit includes a molecular marker for mesothelioma prognosis and a primer;
The biomarker is an iron death-related gene comprising: CARS1, CDKN1A, TFRC, FANCD2, FDFT1, HSPB1, SLC1A5, SLC7a11 and DPP4; the sequence of the primer ：：SEQ ID No:1、SEQ ID No:2、SEQ ID No:3、SEQ ID No:4、SEQ ID No:5、SEQ ID No:6、SEQ ID No:7、SEQ ID No:8、SEQ ID No:9、SEQ ID No:10、SEQ ID No:10、SEQ ID No:12、SEQ ID No:13、SEQ ID No:14、SEQ ID No:15、SEQ ID No:16、SEQ ID No:17、SEQ ID No:18; primer is shown in table 1;
the kit also comprises SYBR Green and reverse transcriptase;
The kit also includes instructions describing the method as described in example 1.
Experimental method
1. Collecting and processing data: we obtained the RNA-seq dataset and relevant clinical information for 87 mesothelioma patients from the TCGA database, up to day 2021, month 4 and 1, as shown in Table 2. We used the R (version 4.0.5) and R Bioconductor packages to analyze our data. Expression data of the RNA-seq dataset obtained from 87 mesothelioma patients in the TCGA database were first normalized to million transcripts per kilobase pair (TPM) values to ensure consistency and comparability of the data prior to further analysis. The following are specific steps normalized to TPM values: step one: raw Read Counts (Raw Read Counts) are calculated, and Raw sequence Read Counts are obtained from the TCGA database, which represent the Raw expression level of each gene. Step two: converted to read counts per kilobase pair (RPK): for each gene, its read count per kilobase pair (RPK) needs to be calculated first. The formula is as follows:
Wherein, the gene length refers to the length of the coding region of the gene in kilobase pairs (kb).
Step three: millions of transcripts per kilobase pair (TPM) values are calculated, and to ensure that data is comparable across samples, the RPK value needs to be converted to a TPM value. The TPM calculation considers the sum of the RPK values of all genes in order to normalize each sample. The formula is as follows:
Where the denominator is the sum of the RPK values of all genes, it ensures that the TPM value of each sample sums to one million in all genes, so that comparisons can be made between different samples, as it takes into account the effects of sequencing depth and gene length.
TABLE 2 RNA-seq dataset of mesothelioma patients and related clinical information
2. Building a prediction model: the collection of 24 iron death regulatory factors, including the TPM obtained from LPCAT3,HSPA5,CARS1,CDKN1A,CS,GLS2,ALOX15,SAT1,ACSL4,EMC2,RPL8,FANCD2,NFE2L2,DPP4,TFRC,ATP5MC3,GPX4,FDFT1,MT1G,NCOA4,SLC7A11,HSPB1,CISD1,SLC1A5 genes, was evaluated for predictive significance of iron death regulatory factors using a Cox regression model. P-value, hazard Ratio (HRs) and 95% Confidence Intervals (CIs) were obtained using Kaplan-Meier survival analysis plots, and univariate based analysis was performed using log rank test and Cox proportional risk analysis. Notably, iron death regulatory factors exhibiting significant prognostic significance were marked as subjects for follow-up examination. After this, a predictive model was created based on the selected iron death regulatory factor, using multi-factor cox iterative regression. Subsequently, patients diagnosed with TCGA mesothelioma were divided into two subgroups of low risk and high risk according to the risk score calculated at the median. The OS (total lifetime) duration of the two subgroups was compared using Kaplan-Meier survival estimates. To assess the predictive accuracy of genes and risk scores, a temporal ROC performance analysis was performed. A predictive graph was created to predict the survival probabilities for 1 year, 3 years, and 5 years, taking patient characteristics into account. 3. Verification of iron death risk scoring model: the study analyzed frozen tissues and data containing identifiable personal information provided by patients receiving surgical treatment or biopsies at the university of Zhongshan Sun Yixian commemorative hospital between 2015 and 2022. The focus of the study is to confirm patients diagnosed with mesothelioma by detailed pathological examination. Key criteria for participant inclusion include: the exact mesothelioma diagnosis, no anti-cancer treatment prior to biopsy, comprehensive clinical pathology records and extensive follow-up data. Samples and data were from the pathology institute and oncology department of the hospital. The study was conducted in accordance with the relevant legal and institutional guidelines, and was approved by the ethical committee of the university of Zhongshan Sun Yixian commemorative hospital, which was approved by the ethical committee, and informed consent was eliminated. . Expression levels of CARS1, CDKN1A, TFRC, FANCD2, FDFT1, HSPB1, SLC1A5, SLC7a11 and DPP4 were detected by RT-PCR and risk scores were calculated according to the above models, after which COX univariate and multivariate analyses were performed on the clinical pathology features and risk scores, and COX regression forest plots were drawn using "survivinal" (version 3.4.0), "survminer" (version 0.4.9) and "forestplot" (version 2.0.1) of R software (version 4.2.2).
The 24 iron death regulatory factors are shown in table 3.
TABLE 3 24 iron death regulatory factors
| ACSL4 | acyl-CoA synthetase long-chain family member 4 |
| ALOX15 | arachidonate 15-lipoxygenase |
| ATP5G3 | ATP synthase, H+ transporting, mitochondrial Fo complex subunit C3 |
| CARS1 | cysteinyl tRNA synthetase 1 |
| CDKN1A | cyclin-dependent kinase inhibitor 1 |
| CISD1 | CDGSH iron sulfur domain 1 |
| CS | citrate synthase |
| DPP4 | dipeptidyl-dippeptidase-4 |
| FANCD2 | Fanconi anemia complementation group D2 |
| FDFT1 | farnesyl-diphosphate farnesyltransferase 1 |
| GLS2 | glutaminase 2 |
| GPX4 | glutathione peroxidase 4 |
| HSPA5 | heat shock protein family A member 5 |
| HSPB1 | heat shock protein beta 1 |
| LPCAT3 | lysophosphatidylcholine acyltransferase 3 |
| MT1G | metallothionein-1G |
| NCOA4 | nuclear receptor coactivator 4 |
| NFE2L2 | nuclear factor, erythroid 2 like 2 |
| RPL8 | ribosomal protein L8 |
| SAT1 | spermidine/spermine N1-acetyltransferase 1 |
| SLC1A5 | solute carrier family 1 Member 5 |
| SLC7A11 | solute carrier family 7 member 11 |
| TFRC | transferrin receptor |
| TTC35/EMC2 | ER membrane protein complex subunit 2 |
4. The specific steps of PCR can be summarized as follows: real-time quantitative reverse transcription polymerase chain reaction (RT-PCR) experiments mainly involve extracting RNA from mesothelioma tissue samples, assessing its purity and concentration by uv spectroscopy, converting RNA to cDNA using reverse transcriptase, and amplifying and quantifying cDNA in a real-time PCR instrument using primers for CARS1, CDKN1A, TFRC, FANCD2, FDFT1, HSPB1, SLC1A5, SLC7a11 and DPP4 and SYBR Green. The specific primer sequences are shown in Table 1.
Experimental results
1. The collection of 24 iron death regulatory factors, including LPCAT3,HSPA5,CARS1,CDKN1A,CS,GLS2,ALOX15,SAT1,ACSL4,EMC2,RPL8,FANCD2,NFE2L2,DPP4,TFRC,ATP5MC3,GPX4,FDFT1,MT1G,NCOA4,SLC7A11,HSPB1,CISD1,SLC1A5, was evaluated for predictive significance of iron death regulatory factors using a Cox regression model. P-value, hazard Ratio (HRs) and 95% Confidence Intervals (CIs) were obtained using Kaplan-Meier survival analysis plots, and univariate based analysis was performed using log rank test and Cox proportional risk analysis. From the above analysis, nine of twenty-four iron death genes were found to have prognostic significance and their corresponding Kaplan-Meier survival curves were shown. The results show that patients with mesothelioma with elevated expression levels of CARS1 (fig. 1A, p=0), CDKN1A (fig. 1b, p=0.006), TFRC (fig. 1c, p=0.045), FANCD2 (fig. 1d, p=0), FDFT1 (fig. 1e, p=0), HSPB1 (fig. 1f, p=0.007), SLC1A5 (fig. 1g, p=0.001), SLC7a11 (fig. 1h, p=0.001) and DPP4 (fig. 1i, p=0.048) with reduced expression levels show unfavorable survival. These nine predictive iron death modifiers were used to construct a prognostic gene model using multifactorial cox iterative regression.
2. Using the risk scores, patients with malignant mesothelioma were divided into two cohorts, and their risk score distribution, survival results, and gene expression patterns of these nine regulators are shown in fig. 2C. As the risk score increases, the patient's risk of mortality increases correspondingly, while their total survival time decreases, as shown in fig. 2C. According to Kaplan-Meier analysis, mesothelioma patient populations with high risk scores had significantly reduced overall survival compared to the low risk control group (median total survival time = 1 year versus 2.1 years, p = 6.29 x 10 -6, fig. 2B). Further, as shown in fig. 2C, AUC indicators were 0.804, 0.865, and 0.873 in ROC curves at time points of 1 year, 3 years, and 5 years, respectively.
3. Verification of the risk scoring model: the invention collects 36 cases of frozen tissue specimens of patients with cancer group mesothelioma from a university Sun Yixian commemorative hospital of Zhongshan, adopts RT-PCR to detect the expression levels of 9 iron death related genes in the model, and calculates the risk scores of the prognosis models of the iron death genes of the patients according to the models. We constructed a prognostic nomogram that estimates patient survival probability in combination with patient clinical pathology and risk scores, both single-factor and multi-factor analyses suggested that risk scores and pM-staging were independent prognostic indicators for mesothelioma patients (fig. 3a, b).
The experimental results show that the mesothelioma prognosis prediction method, the molecular marker and the kit have good universality, high accuracy and sensitivity, and the AUC of the prediction prognosis of 1 year, 3 years and 5 years is more than 0.7.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the scope of the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted equally without departing from the spirit and scope of the technical solution of the present invention.
Claims (8)
1. A method of mesothelioma prognosis, the method comprising the steps of:
(1) Extracting RNA from the sample, and converting the RNA into cDNA by using reverse transcriptase;
(2) Amplifying and quantifying the cDNA obtained in the step (1) by using a primer and SYBR Green;
(3) Detecting the expression levels of CARS1, CDKN1A, TFRC, FANCD2, FDFT1, HSPB1, SLC1A5, SLC7A11 and DPP4 through the step (2), and obtaining corresponding expression signals;
(4) The mesothelioma prognosis risk score is noted as Y as a result of mesothelioma prognosis prediction, and the relation of Y and the expression signal in step (3) is that ：Y=0.6976×CARS1+0.3167×CDKN1A+0.0887×TFRC+0.8286×FANCD2+0.1581×FDFT1+0.4534×HSPB1+0.2378×SLC1A5+（-0.0554）×SLC7A11+（-0.0214）×DPP4.
2. The method of claim 1, wherein the primer is SEQ ID No:1、SEQ ID No:2、SEQ ID No:3、SEQ ID No:4、SEQ ID No:5、SEQ ID No:6、SEQ ID No:7、SEQ ID No:8、SEQ ID No:9、SEQ ID No:10、SEQ ID No:10、SEQ ID No:12、SEQ ID No:13、SEQ ID No:14、SEQ ID No:15、SEQ ID No:16、SEQ ID No:17、SEQ ID No:18;
The sequences of the primers are shown in the following table:
。
3. The mesothelioma prognosis method according to claim 1, wherein the prognosis prediction method is used for 1-5 years of prognosis prediction.
4. A molecular marker for mesothelioma prognosis, wherein the biomarker is an iron-death-related gene comprising: CARS1, CDKN1A, TFRC, FANCD2, FDFT1, HSPB1, SLC1A5, SLC7a11 and DPP4.
5. The molecular marker for mesothelioma prognosis prediction according to claim 4, wherein the mesothelioma prognosis prediction risk score is denoted as Y as a result of mesothelioma prognosis prediction, and the relationship between Y and the expression signal of the molecular marker is ：Y=0.6976×CARS1+0.3167×CDKN1A+0.0887×TFRC+0.8286×FANCD2+0.1581×FDFT1+0.4534×HSPB1+0.2378×SLC1A5+（-0.0554）×SLC7A11+（-0.0214）×DPP4.
6. A kit comprising a molecular marker for mesothelioma prognosis and a primer;
The biomarker is an iron death-related gene comprising: CARS1, CDKN1A, TFRC, FANCD2, FDFT1, HSPB1, SLC1A5, SLC7a11 and DPP4; the primer is ：SEQ ID No:1、SEQ ID No:2、SEQ ID No:3、SEQ ID No:4、SEQ ID No:5、SEQ ID No:6、SEQ ID No:7、SEQ ID No:8、SEQ ID No:9、SEQ ID No:10、SEQ ID No:10、SEQ ID No:12、SEQ ID No:13、SEQ ID No:14、SEQ ID No:15、SEQ ID No:16、SEQ ID No:17、SEQ ID No:18;
The sequences of the primers are shown in the following table:
。
7. the kit of claim 6, further comprising SYBR Green and a reverse transcriptase.
8. The kit of claim 6, further comprising instructions describing the method of claims 1-3.
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