Exploration and Verification of a Six-RNA Binding Proteins-based Prognosis Evaluating Model for Hepatocellular Carcinoma

Abstract BackgroundRNA binding protein (RBP) plays a crucial role in tumorigenesis at post-transcriptional level in various cancer types. Nevertheless, the role of RBPs in liver hepatocellular carcinoma (LIHC) remains obscure. We attempted to uncover the association between RBPs and the prognosis of LIHC patients. MethodsWe analyzed the transcriptome and corresponding clinical data of LIHC patients from the cancer genome atlas (TCGA) (training cohort) and international cancer genome consortium (ICGC) (validating cohort) database with a series of bioinformatics methods. Differently expressed RNA-binding proteins (DERBPs) were screened and subjected to functional enrichment analysis and co-expression network establishment. Overall survival (OS) related DERBPs and our prognosis risk model were confirmed by univariate, LASSO and multivariate regression analysis in training cohort. Survival analysis, Receiver operating characteristic curve (ROC) and nomogram were conducted in both training and validating groups to confirm the performance of our model. Human protein atlas (HPA) database and Kaplan-Meier plotter were used to verify the expression and prognostic significance of the hub RBPs respectively.Results There were 330 RBPs were found significantly different in TCGA. Functional analysis indicated most of the DERBPs were majored in RNA processing, alternative splicing and metabolism, etc. 6 RBPs (UPF3B, MRPL54, ZC3H13, DHX58, PPARGC1A, EIF2AK4) were recognized as OS related and enrolled into our prognostic model. Survival analysis showed the risk signature was negatively correlated with the OS of LIHC patients in both training (p = 5.808e-06) and validating (p = 3.38e-03) groups. The area under curves (AUC) of the receiver operating characteristics (ROC) curve in training and validating cohorts was 0.756, 0.781 respectively which indicating the good performance of our model. The risk signature was an independent hazardous factor in multivariate COX regression analysis either in TCGA (HR = 1.626;95% CI 1.394 -1.897, p < 0.001) or ICGC (HR = 1.939;95% CI 1.324 -2.838, p < 0.001). Nomogram and calibration curve indicated our model had best performance in predicting 3-year survival rate.ConclusionsWe constructed a six-RBPs based risk signature model which had moderate efficiency in LIHC patients’ prognosis forecasting which may assist practitioners to make better decision in the management of LIHC.

Download Full-text

Establishment of a Prognostic Model for Hepatocellular Carcinoma Based on Endoplasmic Reticulum Stress-Related Gene Analysis

Frontiers in Oncology ◽

10.3389/fonc.2021.641487 ◽

2021 ◽

Vol 11 ◽

Author(s):

Peng Liu ◽

Jinhong Wei ◽

Feiyu Mao ◽

Zechang Xin ◽

Heng Duan ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Endoplasmic Reticulum ◽

Regression Analysis ◽

Endoplasmic Reticulum Stress ◽

Cox Regression ◽

Prognostic Biomarker ◽

Cancer Genome ◽

Survival Prediction ◽

Training Cohort ◽

Cox Regression Analysis

Hepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide and its incidence continues to increase year by year. Endoplasmic reticulum stress (ERS) caused by protein misfolding within the secretory pathway in cells and has an extensive and deep impact on cancer cell progression and survival. Growing evidence suggests that the genes related to ERS are closely associated with the occurrence and progression of HCC. This study aimed to identify an ERS-related signature for the prospective evaluation of prognosis in HCC patients. RNA sequencing data and clinical data of patients from HCC patients were obtained from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC). Using data from TCGA as a training cohort (n=424) and data from ICGC as an independent external testing cohort (n=243), ERS-related genes were extracted to identify three common pathways IRE1, PEKR, and ATF6 using the GSEA database. Through univariate and multivariate Cox regression analysis, 5 gene signals in the training cohort were found to be related to ERS and closely correlated with the prognosis in patients of HCC. A novel 5-gene signature (including HDGF, EIF2S1, SRPRB, PPP2R5B and DDX11) was created and had power as a prognostic biomarker. The prognosis of patients with high-risk HCC was worse than that of patients with low-risk HCC. Multivariate Cox regression analysis confirmed that the signature was an independent prognostic biomarker for HCC. The results were further validated in an independent external testing cohort (ICGC). Also, GSEA indicated a series of significantly enriched oncological signatures and different metabolic processes that may enable a better understanding of the potential molecular mechanism mediating the progression of HCC. The 5-gene biomarker has a high potential for clinical applications in the risk stratification and overall survival prediction of HCC patients. In addition, the abnormal expression of these genes may be affected by copy number variation, methylation variation, and post-transcriptional regulation. Together, this study indicated that the genes may have potential as prognostic biomarkers in HCC and may provide new evidence supporting targeted therapies in HCC.

Download Full-text

Integrated Analysis of Immunity- and Ferroptosis-Related Biomarker Signatures to Improve the Prognosis Prediction of Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2020.614888 ◽

2020 ◽

Vol 11 ◽

Author(s):

Xuanlong Du ◽

Yewei Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Molecular Mechanisms ◽

Cox Regression ◽

Prognostic Index ◽

Cancer Genome ◽

Functional Enrichment ◽

Integrated Analysis ◽

Cox Regression Analysis ◽

Biomarker Signature

BackgroundHepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and poor prognoses around the world. Ferroptosis is a new form of cell death, and some studies have found that it is related to cancer immunotherapy. The aim of our research was to find immunity- and ferroptosis-related biomarkers to improve the treatment and prognosis of HCC by bioinformatics analysis.MethodsFirst, we obtained the original RNA sequencing (RNA-seq) expression data and corresponding clinical data of HCC from The Cancer Genome Atlas (TGCA) database and performed differential analysis. Second, we used immunity- and ferroptosis-related differentially expressed genes (DEGs) to perform a computational difference algorithm and Cox regression analysis. Third, we explored the potential molecular mechanisms and properties of immunity- and ferroptosis-related DEGs by computational biology and performed a new prognostic index based on immunity- and ferroptosis-related DEGs by multivariable Cox analysis. Finally, we used HCC data from International Cancer Genome Consortium (ICGC) data to perform validation.ResultsWe obtained 31 immunity (p < 0.001)- and 14 ferroptosis (p < 0.05)-related DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Then, we screened five immunity- and two ferroptosis-related DEGs (HSPA4, ISG20L2, NRAS, IL17D, NDRG1, ACSL4, and G6PD) to establish a predictive model by multivariate Cox regression analysis. Receiver operating characteristic (ROC) and Kaplan–Meier (K–M) analyses demonstrated a good performance of the seven-biomarker signature. Functional enrichment analysis including Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that the seven-biomarker signature was mainly associated with HCC-related biological processes such as nuclear division and the cell cycle, and the immune status was different between the two risk groups.ConclusionOur results suggest that this specific seven-biomarker signature may be clinically useful in the prediction of HCC prognoses beyond conventional clinicopathological factors. Moreover, it also brings us new insights into the molecular mechanisms of HCC.

Download Full-text

Identification of Immune-Related Prognostic mRNA and lncRNA in Patients with Hepatocellular Carcinoma

Journal of Oncology ◽

10.1155/2022/5313149 ◽

2022 ◽

Vol 2022 ◽

pp. 1-16

Author(s):

Dan Chen ◽

Xiaoting Li ◽

Hui Li ◽

Kai Wang ◽

Xianghua Tian

Keyword(s):

Hepatocellular Carcinoma ◽

Flow Cytometry ◽

T Cells ◽

Regression Analysis ◽

Survival Analysis ◽

Immune Cells ◽

Cox Regression ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Immune Related Genes

Background. As the most common hepatic malignancy, hepatocellular carcinoma (HCC) has a high incidence; therefore, in this paper, the immune-related genes were sought as biomarkers in liver cancer. Methods. In this study, a differential expression analysis of lncRNA and mRNA in The Cancer Genome Atlas (TCGA) dataset between the HCC group and the normal control group was performed. Enrichment analysis was used to screen immune-related differentially expressed genes. Cox regression analysis and survival analysis were used to determine prognostic genes of HCC, whose expression was detected by molecular experiments. Finally, important immune cells were identified by immune cell infiltration and detected by flow cytometry. Results. Compared with the normal group, 1613 differentially expressed mRNAs (DEmRs) and 1237 differentially expressed lncRNAs (DElncRs) were found in HCC. Among them, 143 immune-related DEmRs and 39 immune-related DElncRs were screened out. These genes were mainly related to MAPK cascade, PI3K-AKT signaling pathway, and TGF-beta. Through Cox regression analysis and survival analysis, MMP9, SPP1, HAGLR, LINC02202, and RP11-598F7.3 were finally determined as the potential diagnostic biomarkers for HCC. The gene expression was verified by RT-qPCR and western blot. In addition, CD4 + memory resting T cells and CD8 + T cells were identified as protective factors for overall survival of HCC, and they were found highly expressed in HCC through flow cytometry. Conclusion. The study explored the dysregulation mechanism and potential biomarkers of immune-related genes and further identified the influence of immune cells on the prognosis of HCC, providing a theoretical basis for the prognosis prediction and immunotherapy in HCC patients.

Download Full-text

Identification and validation of an immune-related prognostic signature for hepatocellular carcinoma

10.21203/rs.3.rs-25879/v1 ◽

2020 ◽

Author(s):

Xinxin Xia ◽

Hui Liu ◽

Yuejun Li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Immune Gene ◽

Regression Analyses ◽

Prognostic Signature ◽

Biological Mechanisms ◽

Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality. The immune system plays vital roles in HCC initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients. Methods: Gene expression data were retrieved from The Cancer Genome Atlas database. Univariate Cox regression analysis was carried out to identify differentially expressed genes that associated with overall survival. The IRPS was established via Lasso and multivariate Cox regression analysis. Both Cox regression analyses were conducted to determine the independent prognostic factors for HCC. Next, the association between the IRPS and clinical-related factors were evaluated. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset. Gene set enrichment analyses (GSEA) were conducted to understand the biological mechanisms of the IRPS. Results: A total of 62 genes were identified to be candidate immune-related prognostic genes. Transcription factors-immunogenes network was generated to explore the interactions among these candidate genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be an independent risk factor influencing the prognosis of HCC patients. The relationships between the IRPS and infiltration immune cells demonstrated that the IRPS was associated with immune cell infiltration. GSEA identified significantly enriched pathways, which might assist in elucidating the biological mechanisms of the IRPS. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients.Conclusions: The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.

Download Full-text

Development and validation of a robust immune-related risk signature for hepatocellular carcinoma

10.21203/rs.3.rs-16346/v1 ◽

2020 ◽

Author(s):

Zaoqu Liu ◽

Dechao Jiao ◽

Xueliang Zhou ◽

Yuan Yao ◽

Zhaonan Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Functional Enrichment ◽

Differentially Expressed ◽

Risk Genes ◽

Cox Regression Analysis ◽

Related Risk

Abstract Background: A growing amount of evidence has suggested immune-related genes (IRGs) play a key role in the development of hepatocellular carcinoma (HCC). However, there have been no investigations proposing a reliable prognostic signature in terms of tumor immunology. This study aimed to develop a robust signature based on IRGs in HCC.Methods: A total of 597 HCC patients were enrolled. The TCGA database was utilized for discovery, and the ICGC database was utilized for validation. Multiple algorithms (including univariate Cox, LASSO, and multivariate Cox regression) were performed to identify key prognostic IRGs and establish an immune-related risk signature. Bioinformatics analysis and R soft tools were utilized to annotate underlying biological functions. Results: A total of 1416 differentially expressed mRNAs (DEMs) were screened in the TCGA cohort, of which 90 were differentially expressed IRGs (DEIRGs). Using univariate Cox regression analysis, we identified 33 prognostically relevant DEIRGs. Using LASSO regression and multivariate Cox regression analysis, we extracted 8 optimal DEIRGs (APLN, CDK4, CXCL2, ESR1, IL1RN, PSMD2, SEMA3F, and SPP1) to construct a risk signature with the ability to distinguish cases as having a high or low risk of unfavorable prognosis in the TCGA cohort, and the signature was verified in the ICGC cohort. The signature was prognostically significant in all stratified cohorts and was deemed an independent prognostic factor for HCC. We also built a nomogram with good performance by combining the signature with clinicopathological factors to increase the accuracy of predicting HCC prognosis. By investigating the relationship of the risk score and 8 risk genes from our signature with clinical traits, we found that the aberrant expression of the immune-related risk genes is correlated with the development of HCC. Moreover, the high-risk group was higher than the low-risk group in terms of tumor mutation burden (TMB), immune cell infiltration, and the expression of immune checkpoints (PD-1, PD-L1, and CTLA-4), and functional enrichment analysis indicated the signature enriched an intensive immune phenotype.Conclusions: This study developed a robust immune-related risk signature and built a predictive nomogram that reliably predict overall survival in HCC, which may be helpful for clinical management and personalized immunotherapy decisions.

Download Full-text

A Six-LncRNA Expression Signature Associated with Prognosis of Colorectal Cancer Patients

Cellular Physiology and Biochemistry ◽

10.1159/000494868 ◽

2018 ◽

Vol 50 (5) ◽

pp. 1882-1890 ◽

Cited By ~ 3

Author(s):

Jian Zhao ◽

Jian Xu ◽

An-quan Shang ◽

Rui Zhang

Keyword(s):

Colorectal Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Receiver Operating Characteristics ◽

Operating Characteristics ◽

Cox Regression Analysis ◽

Lncrna Expression ◽

Expression Signature ◽

Receiver Operating ◽

Testing Set

Background/Aims: Colorectal cancer (CRC) is one of the most common malignant tumor with high migration and invasion capacity. Long non-coding RNAs (lncRNAs) have been identified to influence multiple cancers progression through competitively binding microRNAs (miRNAs). In this study, we proposed to develop a lncRNA-based signature for CRC survival outcomes. Methods: LncRNA expression profiles of CRC patients were extracted from the Gene Expression Omnibus (GEO) data sets GSE38832 (training set) and GSE29621 (testing set) . Associations between lncRNA expression and CRC disease free survival (DFS) were evaluated through univariate Cox regression analysis, and prognosis signature constructed by combination of weighted lncRNA expression values were obtained through multivariate Cox regression analysis. Robustness of the prognosis signature was evaluated through receiver operating characteristics analysis in the testing set. Results: A weighted prognosis signature of six lncRNAs, including LINC01583, LINC00276, LUNAR1, DKFZp434J0226, SFTA1P and OGFOD3, was yielded from multivariate Cox regression analysis. Samples with significantly different DFS dislayed distinct signatures, indicating considerable predictory accuracy of this expression signature. Conclusion: Robustness of the prognosis signature was evaluated in the testing set through Kaplan-Meier and receiver operating characteristics (ROC) analysis. Furthermore, functional enrichment analysis of lncRNAs suggested significant enrichment of cancer related pathways. Our results revealed the promise of lncRNAs as prognostic biomarkers.

Download Full-text

Identification and Validation of a Novel Metabolism‑Related Prognostic Signature in Hepatocellular Carcinoma

10.21203/rs.3.rs-50552/v1 ◽

2020 ◽

Author(s):

Zhihao Wang ◽

Kidane Siele Embaye ◽

Qing Yang ◽

Lingzhi Qin ◽

Chao Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Value ◽

Cox Regression ◽

Cancer Genome ◽

Regression Analyses ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Tcga Dataset

Abstract Background: Given that metabolic reprogramming has been recognized as an essential hallmark of cancer cells, this study sought to investigate the potential prognostic values of metabolism-related genes(MRGs) for hepatocellular carcinoma (HCC) diagnosis and treatment. Methods: The metabolism-related genes sequencing data of HCC samples with clinical information were obtained from the International Cancer Genome Consortium(ICGC) and The Cancer Genome Atlas (TCGA). The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate Cox regression analysis were performed to identify metabolism-related DEGs that related to overall survival(OS). A novel metabolism-related prognostic signature was developed using the least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analyses . Furthermore, the signature was validated in the TCGA dataset. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in HCC. Results: A total of 178 differentially expressed MRGs were detected between the ICGA dataset and the TCGA dataset. We found that 17 MRGs were most significantly associated with OS by using the univariate Cox proportional hazards regression analysis in HCC. Then, the Lasso and multivariate Cox regression analyses were applied to construct the novel metabolism-relevant prognostic signature, which consisted of six MRGs. The prognostic value of this prognostic model was further successfully validated in the TCGA dataset. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. Six MRGs (FLVCR1, MOGAT2, SLC5A11, RRM2, COX7B2, and SCN4A) showed high prognostic performance in predicting HCC outcomes, and were further associated with tumor TNM stage, gender, age, and pathological stage. Finally, the signature was found to be associated with various clinicopathological features. Conclusions: In summary, our data provided evidence that the metabolism-based signature could serve as a reliable prognostic and predictive tool for overall survival in patients with HCC.

Download Full-text

Identification and Validation of a Prognostic Model Based on Three Autophagy-Related Genes in Hepatocellular Carcinoma

BioMed Research International ◽

10.1155/2021/5564040 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Fanbo Qin ◽

Junyong Zhang ◽

Jianping Gong ◽

Wenfeng Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Score ◽

Prognostic Value ◽

Prognostic Model ◽

Cox Regression ◽

Cancer Genome ◽

Clinical Parameters ◽

Cox Regression Analysis ◽

Model Based

Background. Accumulating studies have demonstrated that autophagy plays an important role in hepatocellular carcinoma (HCC). We aimed to construct a prognostic model based on autophagy-related genes (ARGs) to predict the survival of HCC patients. Methods. Differentially expressed ARGs were identified based on the expression data from The Cancer Genome Atlas and ARGs of the Human Autophagy Database. Univariate Cox regression analysis was used to identify the prognosis-related ARGs. Multivariate Cox regression analysis was performed to construct the prognostic model. Receiver operating characteristic (ROC), Kaplan-Meier curve, and multivariate Cox regression analyses were performed to test the prognostic value of the model. The prognostic value of the model was further confirmed by an independent data cohort obtained from the International Cancer Genome Consortium (ICGC) database. Results. A total of 34 prognosis-related ARGs were selected from 62 differentially expressed ARGs identified in HCC compared with noncancer tissues. After analysis, a novel prognostic model based on ARGs (PRKCD, BIRC5, and ATIC) was constructed. The risk score divided patients into high- or low-risk groups, which had significantly different survival rates. Multivariate Cox analysis indicated that the risk score was an independent risk factor for survival of HCC after adjusting for other conventional clinical parameters. ROC analysis showed that the predictive value of this model was better than that of other conventional clinical parameters. Moreover, the prognostic value of the model was further confirmed in an independent cohort from ICGC patients. Conclusion. The prognosis-related ARGs could provide new perspectives on HCC, and the model should be helpful for predicting the prognosis of HCC patients.

Download Full-text

A Five-Gene Signature for Recurrence Prediction of Hepatocellular Carcinoma Patients

BioMed Research International ◽

10.1155/2020/4037639 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Zeyu Wang ◽

Ningning Zhang ◽

Jiayu Lv ◽

Cuihua Ma ◽

Jie Gu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Early Stage ◽

Gene Signature ◽

Functional Enrichment ◽

High Recurrence Rate ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Recurrence Prediction

Background. Hepatocellular carcinoma (HCC) is one of the most aggressive malignancies with poor prognosis. There are many selectable treatments with good prognosis in Barcelona Clinic Liver Cancer- (BCLC-) 0, A, and B HCC patients, but the most crucial factor affecting survival is the high recurrence rate after treatments. Therefore, it is of great significance to predict the recurrence of BCLC-0, BCLC-A, and BCLC-B HCC patients. Aim. To develop a gene signature to enhance the prediction of recurrence among HCC patients. Materials and Methods. The RNA expression data and clinical data of HCC patients were obtained from the Gene Expression Omnibus (GEO) database. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to screen primarily prognostic biomarkers in GSE14520. Multivariate Cox regression analysis was introduced to verify the prognostic role of these genes. Ultimately, 5 genes were demonstrated to be related with the recurrence of HCC patients and a gene signature was established. GSE76427 was adopted to further verify the accuracy of gene signature. Subsequently, a nomogram based on gene signature was performed to predict recurrence. Gene functional enrichment analysis was conducted to investigate the potential biological processes and pathways. Results. We identified a five-gene signature which performs a powerful predictive ability in HCC patients. In the training set of GSE14520, area under the curve (AUC) for the five-gene predictive signature of 1, 2, and 3 years were 0.813, 0.786, and 0.766. Then, the relative operating characteristic (ROC) curves of five-gene predictive signature were verified in the GSE14520 validation set, the whole GSE14520, and GSE76427, showed good performance. A nomogram comprising the five-gene signature was built so as to show a good accuracy for predicting recurrence-free survival of HCC patients. Conclusion. The novel five-gene signature showed potential feasibility of recurrence prediction for early-stage HCC.

Download Full-text

Identifying Eight Ferroptosis-Related Signatures for Prediction of Hepatocellular Carcinoma Overall Survival

10.21203/rs.3.rs-761186/v1 ◽

2021 ◽

Author(s):

Dandong Luo ◽

Qiang Tao ◽

HuiJuan Jiang ◽

JunLing Zhu ◽

Ning Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Cancer Genome ◽

Low Risk ◽

Functional Enrichment ◽

Cox Regression Analysis ◽

Alternative Treatment Option

Abstract Background Hepatocellular carcinoma (HCC) is characterized by widespread epidemiology and extraordinary heterogeneity, with challenging prognosis prediction. Ferroptosis is a regulatory cell death driven by iron-dependent lipid peroxidation. The main aim of this study was to determine the predictive value of ferroptosis-related genes (FRGs) in HCC. Methods Herein, the data of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) public databases. In the ICGC cohort, a multigenic signature was constructed using the LASSO Cox regression model. Next, patients in the TCGA cohort were used to verify the reliability of the model. Results Results showed that 30.07% of the differentially expressed genes (DEGs) in the ICGC cohort were associated with ferroptosis. Among them, 35 genes were identified as intersected genes associated with overall survival in both cohorts. Moreover, an 8-gene signature for prediction of HCC patients was constructed and the patients were divided it into low-risk and high-risk groups. The results indicated that the overall survival (OS) of patients in the high-risk group was lower than OS of patients in the low-risk group (P < 0.001 in both cohorts). Multivariate Cox regression analysis indicated that the risk score was an independent predictor of OS (HR > 1, P < 0.001). Receiver operating curves (ROC) demonstrated the predictive power of the signature. Furthermore, functional enrichment analysis revealed the existence of significantly correlated immune-related pathways, and their immune states were different between groups. Conclusions In summary, the genetic signature described in this study was associated with ferroptosis and it can be used to predict the prognosis of HCC. Therefore, targeted treatment of ferroptosis may be an alternative treatment option for HCC.

Download Full-text