scholarly journals Development and validation of an immune-related prognostic signature for ovarian cancer based on weighted gene co-expression network analysis

2020 ◽  
Author(s):  
Yuanyuan An ◽  
Qing Yang
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Network Analysis ◽  
Immune Responses ◽  
Cancer Patients ◽  
Cox Regression ◽  
M2 Macrophage ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Geo Database

Abstract Background Ovarian cancer is one of the most lethal diseases of women. The prognosis of ovarian cancer patients was closely correlated with immune cell expression and immune responses. Therefore, it is important to identify a robust prognostic signature, which not only correlates with prognoses but also with immune responses in ovarian cancer, thus, providing immune-related patient therapies. Methods Weighted gene co-expression network analysis (WGCNA) was used to identify candidate genes correlated with ovarian cancer prognoses. Univariate and multivariate cox regression analyses were used to construct the prognostic signature. The Kaplan-Meier method was used to predict survival, and the immune-related bioinformatics analysis was performed using R software. The relationship between the signature and clinical parameters was analyzed with GraphPad Prism 7 and SPSS software. Results Gene expression from The Cancer Genome Atlas dataset was used to perform the WGCNA analysis, and identified candidate prognostic-related genes in patients with ovarian cancer. According to the Cox regression analysis, the prognostic signature was constructed, which divided patients into two groups. The high-risk group showed the least favorable prognosis. Three independent cohorts from the Gene Expression Omnibus (GEO) database were used for the validation studies. According to the immune analyses, the GEO database signatures were significantly correlated with the immune statuses of ovarian cancer patients. By analyzing the combination of the prognostic signature and total mutational burden (TMB), ovarian cancer patients were divided into four groups. In these groups, memory B cell, resting memory CD4 T cell, M2 macrophage, resting mast cell and neutrophil were found significant distinctions among these groups. Conclusions This novel signature predicted the prognosis of ovarian cancer patients precisely and independently and showed significant correlations with immune responses. Therefore, this prognostic signature might could indicate targeted immunotherapies for ovarian cancer patients.

scholarly journals Development and Validation of an Immune-Related Prognostic Signature for Ovarian Cancer Based on Weighted Gene Coexpression Network Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yuanyuan An ◽  
Qing Yang
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Immune Responses ◽  
Cancer Patients ◽  
Cox Regression ◽  
Gene Coexpression Network ◽  
Prognostic Signature ◽  
Gene Coexpression ◽  
Coexpression Network Analysis ◽  
Geo Database

Background. Ovarian cancer is one of the most lethal diseases of women. The prognosis of ovarian cancer patients was closely correlated with immune cell expression and immune responses. Therefore, it is important to identify a robust prognostic signature, which correlates not only with prognoses but also with immune responses in ovarian cancer, thus, providing immune-related patient therapies. Methods. The weighted gene coexpression network analysis (WGCNA) was used to identify candidate genes correlated with ovarian cancer prognoses. Univariate and multivariate Cox regression analyses were used to construct the prognostic signature. The Kaplan-Meier method was used to predict survival, and the immune-related bioinformatics analysis was performed using the R software. The relationship between the signature and clinical parameters was analyzed with the GraphPad Prism 7 and SPSS software. Results. Gene expression from The Cancer Genome Atlas dataset was used to perform the WGCNA analysis, and candidate prognostic-related genes in patients with ovarian cancer were identified. According to the Cox regression analysis, the prognostic signature was constructed, which divided patients into two groups. The high-risk group showed the least favorable prognosis. Three independent cohorts from the Gene Expression Omnibus (GEO) database were used for the validation studies. According to the immune analyses, the GEO database signatures were significantly correlated with the immune statuses of ovarian cancer patients. By analyzing the combination of the prognostic signature and total mutational burden (TMB), ovarian cancer patients were divided into four groups. In these groups, memory B cell, resting memory CD4 T cell, M2 macrophage, resting mast cell, and neutrophil were found with significant distinctions among these groups. Conclusions. This novel signature predicted the prognosis of ovarian cancer patients precisely and independently and showed significant correlations with immune responses. Therefore, this prognostic signature could indicate targeted immunotherapies for ovarian cancer patients.

scholarly journals Alternative splicing acts as an independent prognosticator in ovarian carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yan Ouyang ◽  
Kaide Xia ◽  
Xue Yang ◽  
Shichao Zhang ◽  
Li Wang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Regression Analysis ◽  
Alternative Splicing ◽  
Ovarian Carcinoma ◽  
Cancer Patients ◽  
Cox Regression ◽  
Excision Repair ◽  
Splicing Factors ◽  
Prognostic Signature ◽  
Cox Regression Analysis

AbstractAlternative splicing (AS) events associated with oncogenic processes present anomalous perturbations in many cancers, including ovarian carcinoma. There are no reliable features to predict survival outcomes for ovarian cancer patients. In this study, comprehensive profiling of AS events was conducted by integrating AS data and clinical information of ovarian serous cystadenocarcinoma (OV). Survival-related AS events were identified by Univariate Cox regression analysis. Then, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to construct the prognostic signatures within each AS type. Furthermore, we established a splicing-related network to reveal the potential regulatory mechanisms between splicing factors and candidate AS events. A total of 730 AS events were identified as survival-associated splicing events, and the final prognostic signature based on all seven types of AS events could serve as an independent prognostic indicator and had powerful efficiency in distinguishing patient outcomes. In addition, survival-related AS events might be involved in tumor-related pathways including base excision repair and pyrimidine metabolism pathways, and some splicing factors might be correlated with prognosis-related AS events, including SPEN, SF3B5, RNPC3, LUC7L3, SRSF11 and PRPF38B. Our study constructs an independent prognostic signature for predicting ovarian cancer patients’ survival outcome and contributes to elucidating the underlying mechanism of AS in tumor development.

scholarly journals A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yue Zhao ◽  
Shao-Min Yang ◽  
Yu-Lan Jin ◽  
Guang-Wu Xiong ◽  
Pin Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Overall Survival ◽  
Scoring System ◽  
Cox Regression ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Prognostic Signature ◽  
Clinical Value ◽  
Cox Regression Analysis

The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan–Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment.

scholarly journals Development of a Multi-gene-based Immune Prognostic Signature in Ovarian Cancer

2020 ◽  
Author(s):  
tiefeng cao ◽  
huimin shen
Keyword(s):  
Ovarian Cancer ◽  
Network Analysis ◽  
Prognostic Biomarkers ◽  
Survival Prediction ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background：Chemotherapeutic resistance is responsible for treatment failure. Immunotherapy is important in ovarian cancer (OC). Systematic exploration of immunogenic landscape and reliable immune gene-based prognostic biomarkers or signature is necessary to be identified. This study aims to identify the immune gene-based prognostic biomarkers and regulatory factors, further to develop an individualized prediction signature.Methods: This study systematically explored the gene expression profiles from RNA-seq data set for The Cancer Genome Atlas (TCGA) ovarian cancer. Differentially expressed and survival-associated immune genes and transcription factors (TFs) were identified using immune genes from ImmPort dataset and TFs from Cistoma database. We developed the prognostic signature based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, Network analysis was performed to uncover the potential molecular mechanisms of immune-related genes with the help of computational biology. Results: The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected infiltration of some immune cell subtypes.Conclusions: We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, prognosis, even immunotherapy response of OC patients.

Homologous recombination deficiency associated with response to poly (ADP-ribose) polymerase inhibitors in ovarian cancer patients: The first real-word data from China.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e17543-e17543
Author(s):  
Xiaoxiang Chen ◽  
Jing Ni ◽  
Xia Xu ◽  
Wenwen Guo ◽  
Xianzhong Cheng ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Regression Analysis ◽  
Homologous Recombination ◽  
Cancer Patients ◽  
Real World ◽  
Cox Regression ◽  
Wild Type ◽  
Cox Regression Analysis ◽  
Homologous Recombination Deficiency ◽  
Polymerase Inhibitors

e17543 Background: Homologous recombination deficiency (HRD) is the first phenotypically defined predictive biomarker for Poly (ADP-ribose) polymerase inhibitors (PARPi) in ovarian cancer. However, the proportion of HRD positive in real world and the relationship of HRD status with PARPi in Chinese ovarian cancer patients remains unknown. Methods: A total of sixty-four ovarian cancer patients underwent PARPi, both Olaparib and Niraparib, were enrolled from August 2018 to January 2021 in Jiangsu Institute of Cancer Hospital. HRD score which was the sum of loss of heterozygosity (LOH), telomeric allelic imbalance (TAI) and large-scale state transitions (LST) events were calculated using tumor DNA-based next generation sequencing (NGS) assays. HRD-positive was defined by either BRCA1/2 pathogenic or likely pathogenic mutation or HRD score ≥42. Progression-free survival (PFS) was analyzed with a log-rank test using HRD status and summarized using Kaplan-Meier methodology. Univariate and multiple cox-regression analysis were conducted to investigate all possible clinical factors. Results: 71.9% (46/64) patients were HRD positive and the rest 28.1% (18/64) were HRD negative, which was higher than the HRD positive proportion reported in Western countries. The PFS among HRD positive patients was significantly longer than those HRD negative patients (medium PFS 8.9 m vs 3.6 m, hazard ratio [HR]: 0.22, p < 0.001). Among them, 23 patients who were BRCA wild type but HRD positive had longer PFS than those with BRCA wild type and HRD negative (medium PFS 9.2 m vs 3.6 m, HR: 0.20, p < 0.001). Univariate cox-regression analysis found that HRD status, previous treatment lines, secondary cytoreductive surgery (SCS) were significantly associated with PFS after PARPi treatment. After multiple regression correction, HRD status (HR: 0.39, 95% CI: [0.20-0.76], p = 0.006), ECOG score (HR: 2.53, 95% CI: [1.24-5.17], p = 0.011) and SCS (HR: 2.21, 95% CI: [1.09-4.48], p = 0.028) were the independent factors. Subgroup analysis in ECOG = 0 subgroup (N = 36), HRD positive patients had significant longer PFS than HRD negative patients (medium PFS 10.3 m vs 5.8 m, HR: 0.14, p < 0.001). Also in the subgroup of patients without SCS, PFS in patients with HRD was longer than patients without HRD (medium PFS 10.2 m vs 5.7 m, HR: 0.29, p = 0.003). Conclusions: This is the first real-world data of HRD status in ovarian cancer patients from China and demonstrate that HRD is a valid biomarker for PARP inhibitors in Chinese ovarian cancer patients.

scholarly journals Development of a Multi-gene-based Immune Prognostic Signature in Ovarian Cancer

2021 ◽  
Author(s):  
tiefeng cao ◽  
huimin shen
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Network Analysis ◽  
Survival Prediction ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background： Various components of the immune system play a critical role in the prognosis and treatment response in ovarian cancer (OC). Immunotherapy has been recognized as a hallmark of cancer but the effect is contradictional. Reliable immune gene-based prognostic biomarkers or regulatory factors are necessary to be systematically explored to develop an individualized prediction signature.Methods: This study systematically explored the gene expression profiles in patients with ovarian cancer from RNA-seq data set for The Cancer Genome Atlas (TCGA). Differentially expressed immune genes and transcription factors (TFs) were identified using the collected immune genes from ImmPort dataset and TFs from Cistoma database. Survival associated immune genes and TFs were identified in terms of overall survival. The prognostic signature was developed based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, we performed network analysis to uncover the potential regulators of immune-related genes with the help of computational biology. Results: The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected the immune cells landscape and infiltration of some immune cell subtypes.Conclusions: We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, and prognosis of OC patients.

scholarly journals The Relationship Between Retroperitoneal Lymphadenectomy and Survival in Advanced Ovarian Cancer Patients

2020 ◽  
Author(s):  
Chenyan Fang ◽  
Yingli Zhang ◽  
Lingqin Zhao ◽  
Xi Chen ◽  
Liang Xia ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Postoperative Complications ◽  
Lymph Nodes ◽  
Cancer Patients ◽  
Cox Regression ◽  
Advanced Ovarian Cancer ◽  
Residual Tumor ◽  
Retroperitoneal Lymphadenectomy ◽  
Cox Regression Analysis ◽  
Significant Difference

Abstract Background Systematic retroperitoneal lymphadenectomy has been widely used in the surgical treatment of advanced ovarian cancer patients. Nevertheless, the corresponding therapeutic may not provide a survival benefit. The aim of this study was to assess the effect of systematic retroperitoneal lymphadenectomy in such patients. Methods Patients with advanced ovarian cancer (stage III-IV, according to the classification presented by the International Federation of Gynecology and Obstetrics) who were admitted and treated in Zhejiang Cancer Hospital from January 2004 to December 2013 were enrolled and reviewed retrospectively. All patients were optimally or suboptimally debulked (absent or residual tumor <1 cm) and divided into two groups. Group A (no-lymphadenectomy group, n =170): patients did not undergo lymph node resection; lymph nodes resection or biopsy were selective. Group B (n=240): patients underwent systematic retroperitoneal lymphadenectomy. Results A total of 410 eligible patients were enrolled in the study. The patients’ median age was 51 years old (range, 28–72 years old). The 5-year overall survival (OS) and 2-year progression-free survival (PFS) rates were 78% and 24% in the no-lymphadenectomy group and 76% and 26% in the lymphadenectomy group (P=0.385 and 0.214, respectively). Subsequently, there was no significant difference in 5-year OS and 2-year PFS between the two groups stratified to histological types (serous type or non-serous type), the clinical evaluation of negative lymph nodes or with macroscopic peritoneal metastasis beyond pelvic (IIIB-IV). Multivariate Cox regression analysis indicated that systematic retroperitoneal lymphadenectomy was not a significant factor influencing the patients’ survival. Patients in the lymphadenectomy group had a higher incidence of postoperative complications (incidence of infection treated with antibiotics was 21.7% vs. 12.9% [P=0.027]; incidence of lymph cysts was 20.8% vs. 2.4% [P < 0.001]). Conclusions Our study showed that systematic retroperitoneal lymphadenectomy did not significantly improve survival of advanced ovarian cancer patients with residual tumor <1 cm or absent after cytoreductive surgery, and were associated with a higher incidence of postoperative complications.

scholarly journals The Relationship Between Retroperitoneal Lymphadenectomy and Survival in Advanced Ovarian Cancer Patients

2020 ◽  
Author(s):  
Chenyan Fang ◽  
Yingli Zhang ◽  
Lingqin Zhao ◽  
Xi Chen ◽  
Liang Xia ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Postoperative Complications ◽  
Lymph Nodes ◽  
Cancer Patients ◽  
Cox Regression ◽  
Advanced Ovarian Cancer ◽  
Residual Tumor ◽  
Retroperitoneal Lymphadenectomy ◽  
Cox Regression Analysis ◽  
Significant Difference

Abstract Background Systematic retroperitoneal lymphadenectomy has been widely used in the surgical treatment of advanced ovarian cancer patients. Nevertheless, the corresponding therapeutic may not provide a survival benefit. The aim of this study was to assess the effect of systematic retroperitoneal lymphadenectomy in such patients. Methods Patients with advanced ovarian cancer (stage III-IV, according to the classification presented by the International Federation of Gynecology and Obstetrics) who were admitted and treated in Zhejiang Cancer Hospital from January 2004 to December 2013 were enrolled and reviewed retrospectively. All patients were optimally or suboptimally debulked (absent or residual tumor <1 cm) and divided into two groups. Group A (no-lymphadenectomy group, n =170): patients did not undergo lymph node resection; lymph nodes resection or biopsy were selective. Group B (n=240): patients underwent systematic retroperitoneal lymphadenectomy. Results A total of 410 eligible patients were enrolled in the study. The patients’ median age was 51 years old (range, 28–72 years old). The 5-year overall survival (OS) and 2-year progression-free survival (PFS) rates were 78% and 24% in the no-lymphadenectomy group and 76% and 26% in the lymphadenectomy group (P=0.385 and 0.214, respectively). Subsequently, there was no significant difference in 5-year OS and 2-year PFS between the two groups stratified to histological types (serous type or non-serous type), the clinical evaluation of negative lymph nodes or with macroscopic peritoneal metastasis beyond pelvic (IIIB-IV). Multivariate Cox regression analysis indicated that systematic retroperitoneal lymphadenectomy was not a significant factor influencing the patients’ survival. Patients in the lymphadenectomy group had a higher incidence of postoperative complications (incidence of infection treated with antibiotics was 21.7% vs. 12.9% [P=0.027]; incidence of lymph cysts was 20.8% vs. 2.4% [P < 0.001]). Conclusions Our study showed that systematic retroperitoneal lymphadenectomy did not significantly improve survival in advanced ovarian cancer patients with residual tumor <1 cm or absent after cytoreductive surgery, and were associated with a higher incidence of postoperative complications.

A stromal-associated gene expression signature predicting for survival in a series of patients with advanced high-grade serous ovarian cancer

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 5552-5552
Author(s):  
T. Bonome ◽  
G. Samimi ◽  
M. Randonovich ◽  
J. Brady ◽  
S. Ghosh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Cox Regression ◽  
Patient Survival ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Expression Data ◽  
Prognostic Signature ◽  
Qrt Pcr ◽  
Microarray Expression

5552 Background: Prognostic gene expression signatures have been derived for undissected serous ovarian epithelial tumors, yet the specific contribution of stromal cells to patient survival has not been addressed. The aim of this study is to identify stromal genes impacting patient survival in the context of serous ovarian cancer. Methods: Expression profiling utilizing Affymetrix U133 Plus 2.0 oligonucleotide arrays was completed for 50 microdissected stromal samples derived from high-grade, late-stage serous tumors displaying a broad spectrum of survival endpoints. A semi-supervised dimension reduction method employing multivariate Cox regression and principal components analysis was applied to the expression data to identify genes associated with patient survival and establish a predictive model. qRT-PCR was employed to validate the microarray expression data. Results: Cox regression analysis identified 267 significant genes. The first 6 principal components of these genes, representing >65% of total variance, entered a multivariate Cox model through which the relative hazard of future patients can be predicted. To confirm our finding, the microarray data underwent leave-one-out validation. The patients were equally divided into low- and high-risk groups and non-parametric Kaplan-Meier analysis and log rank test demonstrated the two groups were significantly different in survival (p = 0.0115). Genes associated with cell survival and migration were identified in the prognostic signature. For validation, qRT-PCR data for all 50 specimens was correlated with microarray expression values for a series of select prognostic genes. Conculsions: In this study, we characterized and validated a stromal dervied prognostic signature associated with poor patient survival. Contained in this novel predictor may be stromal targets suitable for the design of new therapeutic interventions, or use as independent diagnostic markers. No significant financial relationships to disclose.

scholarly journals Construction and Validation of a Reliable Six-Gene Prognostic Signature Based on the TP53 Alteration for Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Gene Set Enrichment ◽  
Cox Regression Analysis ◽  
Gene Set

BackgroundThe high mutation rate of TP53 in hepatocellular carcinoma (HCC) makes it an attractive potential therapeutic target. However, the mechanism by which TP53 mutation affects the prognosis of HCC is not fully understood.Material and ApproachThis study downloaded a gene expression profile and clinical-related information from The Cancer Genome Atlas (TCGA) database and the international genome consortium (ICGC) database. We used Gene Set Enrichment Analysis (GSEA) to determine the difference in gene expression patterns between HCC samples with wild-type TP53 (n=258) and mutant TP53 (n=116) in the TCGA cohort. We screened prognosis-related genes by univariate Cox regression analysis and Kaplan–Meier (KM) survival analysis. We constructed a six-gene prognostic signature in the TCGA training group (n=184) by Lasso and multivariate Cox regression analysis. To assess the predictive capability and applicability of the signature in HCC, we conducted internal validation, external validation, integrated analysis and subgroup analysis.ResultsA prognostic signature consisting of six genes (EIF2S1, SEC61A1, CDC42EP2, SRM, GRM8, and TBCD) showed good performance in predicting the prognosis of HCC. The area under the curve (AUC) values of the ROC curve of 1-, 2-, and 3-year survival of the model were all greater than 0.7 in each independent cohort (internal testing cohort, n = 181; TCGA cohort, n = 365; ICGC cohort, n = 229; whole cohort, n = 594; subgroup, n = 9). Importantly, by gene set variation analysis (GSVA) and the single sample gene set enrichment analysis (ssGSEA) method, we found three possible causes that may lead to poor prognosis of HCC: high proliferative activity, low metabolic activity and immunosuppression.ConclusionOur study provides a reliable method for the prognostic risk assessment of HCC and has great potential for clinical transformation.

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