CN116403643A - Liver cancer prognostic marker screening system and method, liver cancer prognosis risk assessment system - Google Patents

Abstract

The invention provides a liver cancer prognosis marker screening system and method and a liver cancer prognosis risk assessment system. The liver cancer prognosis marker screening system comprises a data acquisition and analysis module and a screening module. The data acquisition and analysis module downloads the expression information of the liver cancer and paracancestral tissue genes and the total life cycle data of the sample from the TCGA and GEO databases, and analyzes the liver cancer copper death genes from the tumor copper death genes. The screening module performs gene differential expression analysis in liver cancer and paracancestral tissue samples in TCGA to construct differential expression gene sets; in addition, in the TCGA liver cancer tissue sample, carrying out correlation analysis on all genes and liver cancer copper death genes, and constructing a liver cancer copper death gene correlation gene set; and taking intersection sets of genes in the two sets, combining with prognosis data of corresponding samples, and sequentially screening by utilizing single factor analysis and LASSO regression to combine multiple factor analysis of stepwise regression to obtain the gene related to prognosis of liver cancer copper death. The invention can improve the accuracy of prognosis prediction and treatment selection.

Description

Liver cancer prognostic marker screening system and method, liver cancer prognosis risk assessment system

technical field

The invention relates to the field of biomedical technology, in particular to a liver cancer prognosis marker screening system and method, and a liver cancer prognosis risk assessment system.

Background technique

Liver cancer is one of the tumors with the highest cancer-related mortality worldwide, and the liver is the sixth most common primary cancer. Currently, diagnosis and treatment methods are increasing, but more than 50% of HCC patients are diagnosed at an advanced stage, and 70% of patients relapse within 5 years after treatment. Due to tumor chemotherapy resistance and recurrence, the 5-year survival rate of patients is still low. Early HCC is usually resectable, but advanced HCC often requires systemic treatment with sorafenib in addition to local treatment with ablation, transarterial chemoembolization, or external beam radiation. Early screening and early diagnosis and treatment are the first line of defense for patients with liver cancer to avoid missing the opportunity of surgery, chemotherapy resistance and tumor recurrence in the later stage. Early diagnosis can also identify and distinguish low-risk and high-risk patient populations, predict patient prognosis, and achieve precise and personalized treatment. Therefore, early diagnosis, assessment of patients' prognostic risk before treatment, and accurate stratification are the keys to selecting appropriate treatment methods, improving clinical treatment benefits, and improving patient prognosis. However, there is currently no effective and accurate tool for assessing the prognosis and risk of liver cancer.

Copper death is one of the ways of Programmed Cell Death (PCD). It is a recently discovered copper-dependent way of cell death. An active death process that occurs steadily. Clinical tumor therapy drugs usually achieve the therapeutic effect of temporarily inhibiting tumor growth by inducing PCD in cancer cells. Due to the discovery of copper death, suppression of copper death in tumor cells may be one of the reasons for tumor progression. Recent evidence has shown that copper death is closely related to the prognosis of cancer patients, and copper death-related genes have the potential to be important prognostic markers.

Contents of the invention

In order to overcome the above-mentioned technical defects, the first aspect of the present invention provides a screening system for liver cancer prognostic markers, which includes:

Data acquisition and analysis module, the data acquisition and analysis module is used to download the gene expression information of liver cancer and adjacent tumor paired tissues and the overall survival data of samples in TCGA and GEO databases, and is also used to perform differential expression from several tumor copper death genes Analysis, so as to analyze several liver cancer copper death genes that are differentially expressed in TCGA liver cancer paired samples;

A screening module, the screening module is used to analyze the differential expression of genes in liver cancer and paracancerous tissue samples in TCGA, and construct a set of differentially expressed genes according to the first screening criteria; in addition, in TCGA liver cancer tissue samples, all genes and liver cancer Correlation analysis was performed on copper death genes, and a gene set related to copper death genes in liver cancer was constructed according to the second screening criteria; then, the differentially expressed gene set and the genes in the gene set related to copper death genes in liver cancer were intersected, combined with the prognosis data of the corresponding samples, Using single factor analysis, LASSO regression combined with multivariate analysis of stepwise regression to screen out the genes related to the prognosis of copper death in liver cancer, that is, the prognostic markers of liver cancer.

Further, the screening system for liver cancer prognostic markers further includes a verification module, which is used to detect the mRNA expression levels of liver cancer prognostic markers using PCR technology in the real-world liver cancer sample cohort, with the median of the expression levels as the boundary Values were grouped by risk level and risk factor correlation analysis was performed to detect the correlation between the expression of each gene and the prognosis of the patient's survival status.

Further, the first screening criterion is |log ₂ multiple of difference|>1 and P<0.05, and the second screening criterion is |correlation coefficient|>0.4 and P<0.05.

Further, the tumor copper death gene and the liver cancer copper death gene are FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, GLS and CDKN2A.

Further, the liver cancer prognostic markers are SPC24, GOT2, SRXN1, SAC3D1, CDCA8, NCAPD2, ATAD5, SYDE2, MCM10, N4BP3, NR2C2AO, BESP1, ACAT2 and UNC119B.

The second aspect of the present application provides a liver cancer prognosis risk assessment system, which includes:

A training module, the training module is used to select gene expression data of liver cancer tissue samples in TCGA as a training set, and use the mRNA expression levels of the liver cancer prognosis markers screened by the above-mentioned liver cancer prognosis marker screening system to construct and calculate The prognostic risk scoring model of the risk value, the median of the risk values of all training set samples is selected as the boundary value of the classification risk level, and the risk scoring model is:

Among them, n is the total number of variables, Coef is the coefficient, χ is the variable, that is, the mRNA expression level detected by PCR of liver cancer prognostic markers, and Coef _i is the coefficient of the ith variable; the adjustment method of the coefficients corresponding to different variables is: through crossover Validation method, for the given value, perform cross-validation, select the value with the smallest cross-validation error, and then re-fit the model with all the data according to the obtained value;

The test module is used to detect the mRNA expression level of the PCR detection of liver cancer prognostic markers in real-world liver cancer samples and calculate the risk value, and use the cut-off value of the model obtained in the training set to group all real samples, and pass the survival The value of this cut-off value for evaluating the poor prognosis of liver cancer patients was verified by analysis.

The third aspect of the present application provides a method for screening liver cancer prognostic markers, which includes:

Step S1: Download the gene expression information of liver cancer and paracancerous paired tissues and the overall survival data of samples in TCGA and GEO databases, and also use it for differential expression analysis from several tumor copper death genes, so as to analyze the paired samples of liver cancer in TCGA. Several liver cancer copper death genes differentially expressed in

Step S2: Perform gene differential expression analysis in TCGA liver cancer and paracancerous tissue samples, and construct a differentially expressed gene set according to the first screening criteria; in addition, in TCGA liver cancer tissue samples, correlate all genes with liver cancer copper death genes Analysis, according to the second screening criteria to construct liver cancer copper death gene-related gene set;

Step S3: Intersect the genes in the differentially expressed gene set and the liver cancer copper death gene-related gene set;

Step S4: Combined with the prognosis data of the corresponding samples, the genes related to the prognosis of liver cancer copper death, namely the liver cancer prognostic markers, were screened by single factor analysis, LASSO regression method combined with stepwise regression multivariate analysis.

Further, the method for screening liver cancer prognostic markers further includes step S5: in the real-world liver cancer sample cohort, use PCR technology to detect the mRNA expression levels of liver cancer prognostic markers, and use the median of expression levels as the cut-off value to carry out risk level grouping and Risk factor correlation analysis was carried out to detect the correlation between the expression of each gene and the prognosis of the patient's survival status.

Further, the first screening criterion is |log ₂ multiple of difference|>1 and P<0.05, and the second screening criterion is |correlation coefficient|>0.4 and P<0.05.

Further, the tumor copper death gene and the liver cancer copper death gene are FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, GLS and CDKN2A; the liver cancer prognosis markers are SPC24, GOT2, SRXN1 , SAC3D1, CDCA8, NCAPD2, ATAD5, SYDE2, MCM10, N4BP3, NR2C2AO, BESP1, ACAT2 and UNC119B.

After adopting the above technical solution, compared with the prior art, it has the following beneficial effects:

The invention provides a screening method for copper death-related liver cancer prognosis markers. The screening and classification methods are based on bioinformatics and statistics, and can improve the accuracy of prognosis prediction and treatment selection. The riskScore, a liver cancer prognosis risk assessment tool screened by the screening and classification method of the present invention, has a high degree of consistency with the real prognosis data in the liver cancer cohort of our unit.

Description of drawings

Fig. 1 is a flowchart of the screening of liver cancer prognostic markers of the present invention.

Fig. 2 is the prognostic analysis of the present invention's prognostic risk value riskScore in the training set and liver cancer and survival status.

Fig. 3 is an analysis of the present invention's prognostic risk value riskScore in the test set and liver cancer survival staging.

Detailed ways

The advantages of the present invention will be further elaborated below in conjunction with the accompanying drawings and specific embodiments. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not limit the protection scope of the present invention.

Example 1 Screening of liver cancer prognostic markers

This embodiment provides a screening system for liver cancer prognostic markers, which includes a data collection and analysis module, a screening module and a verification module.

The data acquisition and analysis module is used to download the gene expression information of liver cancer and paracancerous paired tissues and the overall survival data of the samples in the TCGA and GEO databases. It is also used to perform differential expression analysis from several tumor copper death genes, thereby analyzing Several HCC copper death genes differentially expressed in HCC paired samples.

The screening module is used for gene differential expression analysis in liver cancer and paracancerous tissue samples in TCGA, and constructs a set of differentially expressed genes according to the first screening standard, the first screening standard is |log2 difference multiple|>1 and P<0.05; In addition, in TCGA liver cancer tissue samples, the correlation analysis was performed between all genes and liver cancer copper death genes, and a gene set related to liver cancer copper death genes was constructed according to the second screening criteria. The second screening criteria was |correlation coefficient|>0.4 and P<0.05; Then, the genes in the differentially expressed gene set and the gene set related to copper death in liver cancer were intersected, and combined with the prognosis data of the corresponding samples, the prognosis of copper death in liver cancer was obtained by univariate analysis, LASSO regression combined with stepwise regression multivariate analysis and screening. Related genes, namely liver cancer prognostic markers.

The verification module is used to detect the mRNA expression levels of liver cancer prognostic markers using PCR technology in the real-world liver cancer sample cohort, and use the median expression level as the cut-off value to carry out risk level grouping and risk factor correlation analysis, so as to detect each gene Correlation between expression and prognosis of patient survival status.

As shown in Figure 1, the process of screening liver cancer prognostic markers using the liver cancer prognostic marker screening system includes the following steps:

Step S1: Download the gene expression information of liver cancer and paracancerous paired tissues and the overall survival data of samples in TCGA and GEO databases, and also use it for differential expression analysis from several tumor copper death genes, so as to analyze the paired samples of liver cancer in TCGA. Several hepatocellular carcinoma copper death genes differentially expressed in

The data acquisition and analysis module is used to download the gene expression information of liver cancer and para-cancer paired tissues and the overall survival data of samples (50 pairs in total) from the Cancer Genome Atlas (TCGA) and GEO (Gene Expression Omnibus, GEO) online . According to the research results of the international authoritative journal Science (doi:10.1126/science.abf0529), 10 copper death-related genes were defined: FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, GLS and CDKN2A. The differential expression analysis of the above genes in TCGA liver cancer paired samples showed that all genes were differentially expressed. The tumor copper death gene and the liver cancer copper death gene are FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, GLS and CDKN2A.

Step S2: Perform differential expression analysis of genes in liver cancer and paracancerous tissue samples in TCGA, and construct a set of differentially expressed genes according to the first screening standard, which is |log2 difference multiple|>1 and P<0.05 ; In addition, in the TCGA liver cancer tissue samples, the correlation analysis was performed between all genes and liver cancer copper death genes, and a set of genes related to liver cancer copper death genes was constructed according to the second screening criteria. The second screening criteria was |correlation coefficient|>0.4 and P <0.05.

Step S3: Intersect the genes in the differentially expressed gene set and the liver cancer copper death gene-related gene set.

Step S4: Combined with the prognosis data of the corresponding samples, the genes related to the prognosis of liver cancer copper death, namely the liver cancer prognostic markers, were screened by single factor analysis, LASSO regression method combined with stepwise regression multivariate analysis.

Fourteen prognosis-related genes were screened, namely SPC24, GOT2, SRXN1, SAC3D1, CDCA8, NCAPD2, ATAD5, SYDE2, MCM10, N4BP3, NR2C2AO, BESP1, ACAT2 and UNC119B.

Step S5: In the real-world liver cancer sample cohort, use PCR technology to detect the mRNA expression levels of liver cancer prognostic markers, use the median expression level as the cut-off value to carry out risk level grouping and risk factor correlation analysis, so as to detect the expression of each gene Correlation with patient survival status.

In a cohort of 50 liver cancer tissue samples, the mRNA expression levels of SPC24, GOT2, SRXN1, SAC3D1, CDCA8, NCAPD2, ATAD5, SYDE2, MCM10, N4BP3, NR2C2AO, BESP1, ACAT2 and UNC119B detected by PCR technology were expressed in terms of expression values The median was used as the cut-off value for grouping (low-risk group, high-risk group) and risk factor correlation analysis to detect the correlation between the expression of each gene and the prognosis of the patient's survival status. P<0.05 considered the difference to be statistically significant. The primers used in PCR are listed in Table 1.

Table 1 Primer sequences of liver cancer prediction prognostic gene set

Primer sequence(5'to 3') serial number GOT2-F AAGAGGGACACCAATAGCAAAAA SEQID No.1 GOT2-R GCAGAACGTAAGGCTTTCCAT SEQ ID No.2 SPC24-F GAGGACACGACAGTCACAATC SEQID No.3 SPC24-R CTGGGGCCATGATGGATGC SEQID No4 SRXN1-F CAGGGAGGTGACTACTTCTACTC SEQID No.5 SRXN1-R CAGGTACACCCTTAGGTCTGA SEQID No.6 SAC3D1-F AAGCCCTGCATGAGGTTCTAC SEQID No.7 SAC3D1-R CACTGCACAGCGCAACTTG SEQID No.8 CDCA8-F GAAGGGCAGTAGTCGGGTG SEQID No.9 CDCA8-R TCACGGTCGAAGTCTTTTCAGA SEQID No.10 NCAPD2-F TGGAGGGGTGAATCAGTATGT SEQID No.11 NCAPD2-R GCGGGATACCACTTTTATCAGG SEQID No.12 ATAD5-F GTGAAGGACTGCGAGATTGAG SEQID No.13 ATAD5-R TGTCTCTAGTCTTTCCCTAGTGGT SEQID No.14 SYDE2-F GGTCCTCTGTGATACGCAGTG SEQID No.15 SYDE2-R CGGGCACGACCCTTCATTC SEQID No.16 MCM10-F TGTCCCTGCGCTACCAAGA SEQID No.17 MCM10-R GATGAGCTTTTGGGATCTGGAG SEQID No.18 N4BP3-F AACGAGCCTGCCGACTATG SEQID No.19 N4BP3-R ACTTTGCATGGATAGGAAGCC SEQ ID No. 20 NR2C2AP-F GCTGAATCGCAACACTCGG SEQID No.21 NR2C2AP-R CCCTGGTCTGAGTTCCAACA SEQID No.22 BFSP1-F AATGAGTGCGAATGTCAACTCC SEQ ID No. 23 BFSP1-R TTATGCAGCAAGGCTTCATCA SEQ ID No. 24 ACAT1-F AAGGCAGGCAGTATTGGGTG SEQ ID No. 25 ACAT1-R ACATCAGTTAGCCGTCTTTTAC SEQID No.26 UNC119B-F GCCGGGTCACCGAGAATTATT SEQ ID No. 27 UNC119B-R GAAACGCAAGGTTTGGCAATC SEQ ID No. 28 GAPDH-F TGTGGGCATCAATGGATTTGG SEQID No.29 GAPDH-R ACACCATGTATTCCGGGTCAAT SEQ ID No. 30

Example 2 Liver cancer prognosis risk assessment

This embodiment provides a liver cancer prognosis risk assessment system, which includes a training module and a testing module. It is an assessment tool and threshold for assigning risk levels.

The training module is used to select the gene expression data of liver cancer tissue samples in TCGA as the training set, and use the mRNA expression levels of the 14 liver cancer prognostic markers screened by the above-mentioned liver cancer prognostic marker screening system to construct a risk value calculation model. For the prognostic risk scoring model, the median of the risk values of all samples in the training set is selected as the cutoff value of the classified risk level. The risk scoring model is:

Among them, n is the total number of variables, Coef is the coefficient, χ is the variable, that is, the mRNA expression level detected by PCR of liver cancer prognostic markers, and Coef _i is the coefficient of the ith variable; the adjustment method of the coefficients corresponding to different variables is: through crossover Validation method, for the given value, perform cross-validation, select the value with the smallest cross-validation error, and then re-fit the model with all the data according to the obtained value;

The training module uses the poor prognosis biomarkers to perform a risk score on the prognosis of liver cancer patients to obtain the risk value riskScore, risk value = TNM stage + (SPC24 expression level × (-0.56)) + (GOT2 expression level × (-0.36)) +(SRXN1 expression×0.25)+(SAC3D1 expression×0.66)+(CDCA8 expression×0.98)+(NCAPD2 expression×(-0.49))+(ATAD5 expression×(-0.03))+(SYDE2 expression Amount×0.53)+(MCM10 expression amount×0.50)+(N4BP3 expression amount×0.34)+(NR2C2AP expression amount×(-0.56))+(BFSP1 expression amount×0.45)+(ACAT2 expression amount×(-0.30)) +(UNC119B expression level×(-0.24)). The median of the risk values of all training set samples is selected as the cut-off value of the classification risk level. Exemplarily, the TNM staging of liver cancer in this embodiment is the eighth edition of the 2017 AJCC. A risk value smaller than the cutoff value was defined as low risk of poor prognosis, and a risk value greater than the cutoff value was defined as high risk of poor prognosis). Risk score prognostic analysis, assessing the correlation between risk value and prognosis, P<0.05 considered the difference to be statistically significant. The difference in clinical stage between high risk and low risk of poor prognosis in the training module is shown in Figure 2.

The test module is used to detect the mRNA expression levels of 14 liver cancer prognostic markers detected by PCR in real-world liver cancer samples and calculate the risk value. All real samples are grouped by using the cut-off value of the model obtained in the training set, and the survival analysis is used to verify the After the cut-off value, it is used to evaluate the value of poor prognosis in patients with liver cancer.

The test module is used to verify the value of the cutoff value of the risk assessment system in evaluating the prognostic risk level in the liver cancer online data cohort sample. The data cohort contains gene expression data of 50 liver cancer samples. The expression levels of 14 prognostic genes were detected by PCR and the risk value was calculated. Survival status and prognosis clinical staging were analyzed after risk grouping by cut-off value. The differences in clinical staging between high-risk and low-risk patients with poor prognosis in the test module are shown in Figure 3, and P<0.05 was considered statistically significant.

The liver cancer prognosis risk assessment tool riskScore screened by the screening and classification method of the present invention has a high degree of consistency with the real prognosis data results in the liver cancer cohort, which can improve the accuracy of prognosis prediction and treatment selection.

It should be noted that the embodiments of the present invention have better implementability and are not intended to limit the present invention in any form. Any person skilled in the art may use the technical content disclosed above to change or modify equivalent effective embodiments However, any modifications or equivalent changes and modifications made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still belong to the scope of the technical solution of the present invention.

Claims (10)

1. A screening system for liver cancer prognostic markers, comprising: Data acquisition and analysis module, the data acquisition and analysis module is used to download the gene expression information of liver cancer and adjacent tumor paired tissues and the overall survival data of samples in TCGA and GEO databases, and is also used to perform differential expression from several tumor copper death genes Analysis, so as to analyze several liver cancer copper death genes that are differentially expressed in TCGA liver cancer paired samples; A screening module, the screening module is used to analyze the differential expression of genes in liver cancer and paracancerous tissue samples in TCGA, and construct a set of differentially expressed genes according to the first screening criteria; in addition, in TCGA liver cancer tissue samples, all genes and liver cancer Correlation analysis was performed on copper death genes, and a gene set related to copper death genes in liver cancer was constructed according to the second screening criteria; then, the differentially expressed gene set and the genes in the gene set related to copper death genes in liver cancer were intersected, combined with the prognosis data of the corresponding samples, Using single factor analysis, LASSO regression combined with multivariate analysis of stepwise regression to screen out the genes related to the prognosis of copper death in liver cancer, that is, the prognostic markers of liver cancer. 2. the liver cancer prognosis marker screening system as claimed in claim 1, is characterized in that, further comprises verification module, and described verification module is used for in real world liver cancer sample cohort, utilizes PCR technology to detect the mRNA expression of liver cancer prognosis marker The median expression level was used as the cut-off value to carry out risk level grouping and risk factor correlation analysis, so as to detect the correlation between the expression of each gene and the prognosis of the patient's survival status. 3. The screening system for liver cancer prognostic markers according to claim 1, wherein the first screening criterion is |log ₂ difference multiple|>1 and P<0.05, and the second screening criterion is |correlation coefficient |>0.4 and P<0.05. 4. The liver cancer prognosis marker screening system according to any one of claims 1-3, wherein the tumor copper death gene and the liver cancer copper death gene are all FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, and CDKN2A. 5. The liver cancer prognostic marker screening system according to any one of claims 1-4, wherein the liver cancer prognostic markers are SPC24, GOT2, SRXN1, SAC3D1, CDCA8, NCAPD2, ATAD5, SYDE2, MCM10, N4BP3, NR2C2AO, BESP1, ACAT2 and UNC119B. 6. A liver cancer prognosis risk assessment system, characterized in that it comprises: A training module, the training module is used to select the gene expression data of liver cancer tissue samples in TCGA as a training set, and use the PCR of the liver cancer prognosis markers screened by the liver cancer prognosis marker screening system according to any one of claims 1-5 The detected mRNA expression level is used to construct the prognostic risk scoring model for calculating the risk value, and the median of the risk value of all training set samples is selected as the cut-off value of the classification risk level, and the risk scoring model is:

Wherein, n is the total number of variables, Coef is a coefficient, χ is a variable, that is, the mRNA expression level detected by PCR of liver cancer prognostic markers, and Coef _i is the coefficient of the i variable; The test module is used to detect the mRNA expression level of the PCR detection of liver cancer prognostic markers in real-world liver cancer samples and calculate the risk value, and use the cut-off value of the model obtained in the training set to group all real samples, and pass the survival The value of this cut-off value for evaluating the poor prognosis of liver cancer patients was verified by analysis. 7. A method for screening liver cancer prognostic markers, comprising: Step S1: Download the gene expression information of liver cancer and paracancerous paired tissues and the overall survival data of samples in TCGA and GEO databases, and also use it for differential expression analysis from several tumor copper death genes, so as to analyze the paired samples of liver cancer in TCGA. Several liver cancer copper death genes differentially expressed in Step S2: Perform gene differential expression analysis in TCGA liver cancer and paracancerous tissue samples, and construct a differentially expressed gene set according to the first screening criteria; in addition, in TCGA liver cancer tissue samples, correlate all genes with liver cancer copper death genes Analysis, according to the second screening criteria to construct liver cancer copper death gene-related gene set; Step S3: Intersect the genes in the differentially expressed gene set and the liver cancer copper death gene-related gene set; Step S4: Combined with the prognosis data of the corresponding samples, the genes related to the prognosis of liver cancer copper death, namely the liver cancer prognostic markers, were screened by single factor analysis, LASSO regression method combined with stepwise regression multivariate analysis. 8. The method for screening liver cancer prognostic markers according to claim 7, further comprising step S5: in the real-world liver cancer sample queue, using PCR technology to detect the mRNA expression levels of liver cancer prognostic markers, expressed in terms of The median was used as the cut-off value to group risk levels and risk factor correlation analysis to detect the correlation between the expression of each gene and the prognosis of the patient's survival status. 9. The method for screening liver cancer prognostic markers according to claim 7, wherein the first screening criterion is |log ₂ difference multiple|>1 and P<0.05, and the second screening criterion is |correlation coefficient |>0.4 and P<0.05. 10. The method for screening liver cancer prognostic markers according to claim 7, wherein the tumor copper death gene and the liver cancer copper death gene are FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1 , GLS and CDKN2A; the liver cancer prognostic markers are SPC24, GOT2, SRXN1, SAC3D1, CDCA8, NCAPD2, ATAD5, SYDE2, MCM10, N4BP3, NR2C2AO, BESP1, ACAT2 and UNC119B.

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