scholarly journals The Landscape of Iron metabolism-related Genes for Overall survival prediction in Patients with Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Zhipeng Zhu ◽  
Huang Cao ◽  
Heqing Huang ◽  
Hongliang Zhan ◽  
Zhengsheng Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
T Cell ◽  
High Risk ◽  
Iron Metabolism ◽  
Molecular Mechanisms ◽  
Cancer Genome ◽  
Survival Outcomes ◽  
Survival Prediction ◽  
Novel Model

Abstract Hepatocellular carcinoma (HCC) is the seventh most commonly occurring cancer and the second most common cause of cancer-related death worldwide. Despite improvements in early detection and treatment, the morbidity and mortality remain high because of complex molecular mechanisms and cellular heterogeneity in HCC. Immunotherapy therapies have been identified to be an effective treatment strategy for HCC. However, novel model is still needed to predict the survival and clinical immunotherapy response in HCC. Here we established a gene signature using iron metabolism-related genes form the Cancer Genome Atlas (TCGA), and the survival outcomes were validated from International Cancer Genome Consortium(ICGC). Distinct subtypes (high- and low-risk subtypes) were characterized by different clinical outcomes. The high-risk subtype was featured by better survival outcomes, upregulation of immune checkpoints expression, including programmed death-ligand 1 (PD-L1) expression, cytotoxic T-lymphocyte associated protein 4(CTLA-4) expression, T-cell immunoglobulin mucin 3(TIM-3) expression and T cell Ig and ITIM domain (TIGIT) expression, upregulated cell cycle relevant pathways and better response for immunotherapy. Thus our finding suggested that the novel model may be useful as a biomarker for prognostic predication, immunotherapy and cell cycle inhibitors may be efficacious for high-risk subtype of HCC patients.

scholarly journals The Landscape of Iron metabolism-related Genes for Overall survival prediction in Patients with Hepatocellular carcinoma

2021 ◽  
Author(s):  
Zhipeng Zhu ◽  
Huang Cao ◽  
Anran Sun ◽  
Hongliang zhan ◽  
Zhengsheng Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
High Risk ◽  
Iron Metabolism ◽  
Gene Signature ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Survival Outcomes ◽  
Survival Prediction ◽  
Novel Model

Abstract Background Hepatocellular carcinoma (HCC) is the seventh most commonly occurring cancer and the second most common cause of cancer-related death worldwide. Despite improvements in early detection and treatment, the morbidity and mortality remain high because of complex molecular mechanisms and cellular heterogeneity in HCC. However, novel model is still needed to predict the survival and clinical immunotherapy response in HCC.Methods 13 iron metabolism-related gene sets were identified from the GSEA. DEGs associated with iron metabolism were calculated between patients who survived < 1 year and more than 3 years for subsequent analysis. Univariate cox proportional hazard regression and LASSO analysis were performed to construct a gene signature. The Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC), Univariate and Multivariate Cox regression analysis, stratification analysis, Principal Component Analysis (PCA) analysis were used to assess the prognostic value of the gene signature. Furthermore, the reliability and validity were validated in external testing cohort, internal testing cohort. Moreover, Weighted gene co-expression network analysis(WGCNA), Gene set enrichment analysis(GSEA), Gene Ontology (GO) and KEGG analysis were performed to reveal signaling pathways, and two independent prognostic factors were combined to build Nomogram for predicting HCC prognosis. Finally, expression level of genes of gene signature in clinical samples was performed using quantitative real-time RT-PCR (qRT-PCR) analyses.Results Here we established a gene signature using iron metabolism-related genes form the Cancer Genome Atlas (TCGA), and the survival outcomes were validated from International Cancer Genome Consortium(ICGC). Distinct subtypes (high- and low-risk subtypes) were characterized by different clinical outcomes. The high-risk subtype was featured by better survival outcomes, upregulated cell cycle relevant pathways and better response for immunotherapy.Conclusions Our finding suggested that the novel model may be useful as a biomarker for prognostic predication, immunotherapy and cell cycle inhibitors may be efficacious for high-risk subtype of HCC patients.

scholarly journals Identification of Aging-Related Genes Associated With Clinical and Prognostic Features of Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Xingte Chen ◽  
Lei Wang ◽  
Liang Hong ◽  
Zhixiong Su ◽  
Xiaohong Zhong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Prognostic Features

Background: Aging is a well-studied concept, but no studies have comprehensively analyzed the association between aging-related genes (AGs) and hepatocellular carcinoma (HCC) prognosis.Methods: Gene candidates were selected from differentially expressed genes and prognostic genes in The Cancer Genome Atlas (TCGA) database. A gene risk score for overall survival prediction was established using the least absolute shrinkage and selection operator (LASSO) regression analysis, and this was validated using data from the International Cancer Genome Consortium (ICGC) database. Functional analysis was conducted using gene ontology enrichment, Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis, and immune microenvironment and tumor stemness analyses.Results: Initially, 72 AGs from the TCGA database were screened as differentially expressed between normal and tumor tissues and as genes associated with HCC prognosis. Then, seven AGs (POLA1, CDK1, SOCS2, HDAC1, MAPT, RAE1, and EEF1E1) were identified using the LASSO regression analysis. The seven AGs were used to develop a risk score in the training set, and the risk was validated to have a significant prognostic value in the ICGC set (p &lt; 0.05). Patients with high risk scores had lower tumor differentiation, higher stage, and worse prognosis (all p &lt; 0.05). Multivariate Cox regression analyses also confirmed that the risk score was an independent prognostic factor for HCC in both the TCGA and ICGC sets (all p &lt; 0.05). Further analysis showed that a high risk score was correlated with the downregulation of metabolism and tumor immunity.Conclusion: The risk score predicts HCC prognosis and could thus be used as a biomarker not only for predicting HCC prognosis but also for deciding on treatment.

scholarly journals Six Metabolism Related mRNAs Predict the Prognosis of Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiwen Wu ◽  
Tian Lan ◽  
Muqi Li ◽  
Junfeng Liu ◽  
Xukun Wu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Score ◽  
Validation Cohort ◽  
Risk Group ◽  
Cancer Genome ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Training Cohort

Background: Hepatocellular carcinoma (HCC) is one of the most common aggressive solid malignant tumors and current research regards HCC as a type of metabolic disease. This study aims to establish a metabolism-related mRNA signature model for risk assessment and prognosis prediction in HCC patients.Methods: HCC data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Enrichment Analysis (GSEA) website. Least absolute shrinkage and selection operator (LASSO) was used to screen out the candidate mRNAs and calculate the risk coefficient to establish the prognosis model. A high-risk group and low-risk group were separated for further study depending on their median risk score. The reliability of the prediction was evaluated in the validation cohort and the whole cohort.Results: A total of 548 differential mRNAs were identified from HCC samples (n = 374) and normal controls (n = 50), 45 of which were correlated with prognosis. A total of 373 samples met the screening criteria and there were randomly divided into the training cohort (n = 186) and the validation cohort (n = 187). In the training cohort, six metabolism-related mRNAs were used to construct a prognostic model with a LASSO regression model. Based on the risk model, the overall survival rate of the high-risk cohort was significantly lower than that of the low-risk cohort. The results of a time-ROC curve proved that the risk score (AUC = 0.849) had a higher prognostic value than the pathological grade, clinical stage, age or gender.Conclusion: The model constructed by the six metabolism-related mRNAs has a significant value for survival prediction and can be applied to guide the evaluation of HCC and the designation of clinical therapy.

scholarly journals A Starvation-Based 9-mRNA Signature Correlates With Prognosis in Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Dengliang Lei ◽  
Yue Chen ◽  
Yang Zhou ◽  
Gangli Hu ◽  
Fang Luo
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Liver Cancer ◽  
Cancer Patients ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Biological Processes ◽  
Gene Set Enrichment

BackgroundHepatocellular carcinoma (HCC) is one of the world’s most prevalent and lethal cancers. Notably, the microenvironment of tumor starvation is closely related to cancer malignancy. Our study constructed a signature of starvation-related genes to predict the prognosis of liver cancer patients.MethodsThe mRNA expression matrix and corresponding clinical information of HCC patients were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GSEA) was used to distinguish different genes in the hunger metabolism gene in liver cancer and adjacent tissues. Gene Set Enrichment Analysis (GSEA) was used to identify biological differences between high- and low-risk samples. Univariate and multivariate analyses were used to construct prognostic models for hunger-related genes. Kaplan-Meier (KM) and receiver-operating characteristic (ROC) were used to assess the model accuracy. The model and relevant clinical information were used to construct a nomogram, protein expression was detected by western blot (WB), and transwell assay was used to evaluate the invasive and metastatic ability of cells.ResultsFirst, we used univariate analysis to identify 35 prognostic genes, which were further demonstrated to be associated with starvation metabolism through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We then used multivariate analysis to build a model with nine genes. Finally, we divided the sample into low- and high-risk groups according to the median of the risk score. KM can be used to conclude that the prognosis of high- and low-risk samples is significantly different, and the prognosis of high-risk samples is worse. The prognostic accuracy of the 9-mRNA signature was also tested in the validation data set. GSEA was used to identify typical pathways and biological processes related to 9-mRNA, cell cycle, hypoxia, p53 pathway, and PI3K/AKT/mTOR pathway, as well as biological processes related to the model. As evidenced by WB, EIF2S1 expression was increased after starvation. Overall, EIF2S1 plays an important role in the invasion and metastasis of liver cancer.ConclusionsThe 9-mRNA model can serve as an accurate signature to predict the prognosis of liver cancer patients. However, its mechanism of action warrants further investigation.

scholarly journals A Nomogram Based on a Three-Gene Signature Derived from AATF Coexpressed Genes Predicts Overall Survival of Hepatocellular Carcinoma Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Jun Liu ◽  
Jianjun Lu ◽  
Zhanzhong Ma ◽  
Wenli Li
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Clinical Practice ◽  
Correlation Coefficient ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Gene Signature ◽  
Cancer Genome ◽  
Survival Prediction

Background. Hepatocellular carcinoma (HCC) is a common cancer with an extremely high mortality rate. Therefore, there is an urgent need in screening key biomarkers of HCC to predict the prognosis and develop more individual treatments. Recently, AATF is reported to be an important factor contributing to HCC. Methods. We aimed to establish a gene signature to predict overall survival of HCC patients. Firstly, we examined the expression level of AATF in the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA), and the International Union of Cancer Genome (ICGC) databases. Genes coexpressed with AATF were identified in the TCGA dataset by the Poisson correlation coefficient and used to establish a gene signature for survival prediction. The prognostic significance of this gene signature was then validated in the ICGC dataset and used to build a combined prognostic model for clinical practice. Results. Gene expression data and clinical information of 2521 HCC patients were downloaded from three public databases. AATF expression in HCC tissue was higher than that in matched normal liver tissues. 644 genes coexpressed with AATF were identified by the Poisson correlation coefficient and used to establish a three-gene signature (KIF20A, UCK2, and SLC41A3) by the univariate and multivariate least absolute shrinkage and selection operator Cox regression analyses. This three-gene signature was then used to build a combined nomogram for clinical practice. Conclusion. This integrated nomogram based on the three-gene signature can predict overall survival for HCC patients well. The three-gene signature may be a potential therapeutic target in HCC.

Genomic landscape of HBV-related hepatocellular carcinoma in south China patients with high mutational frequency of TP53 and TERT promoter.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15670-e15670
Author(s):  
Jiazhou Ye ◽  
Yinguang Wang ◽  
Rong Liang ◽  
Xue Wu ◽  
Yang Shao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Wnt Signaling ◽  
South China ◽  
Molecular Mechanisms ◽  
Somatic Mutations ◽  
Biological Effects ◽  
Cell Cycle Control ◽  
Tert Promoter ◽  
Genomic Landscape

e15670 Background: Development of hepatocellular carcinoma (HCC) is a complex process with accumulations of polygene abnormalities and multi-pathway misregulation. Hepatitis B virus (HBV) exposure can cause liver damage and promote hepatocarcinogenesis via various biological effects. We aimed to investigate the molecular mechanisms underlying the etiology of HBV-related HCC development, and provide new insights into novel molecular targets. Methods: 84 HBV-positive HCC patients from Guangxi Province, South China, who underwent hepatic resection, were enrolled in this study. Genomic alterations were analyzed in pair-matched tumor and adjacent normal tissue using a hybridization capture-based next-generation sequencing (NGS) assay targeting 422 cancer-relevant genes. Results: In total, 691 somatic mutations, 166 copy number variations and 10 gene fusions were detected in 81 (96.4%) of 84 tumor samples. The most commonly mutated gene is TP53 in this cohort (84% of the patients), which is much higher than its frequency in the reported overall HCC patients. TERT promoter has somatic mutations in 32% of the patients, reactivation of which has been implicated in multiple cancer types. Dysfunction in the cell cycle control pathway (TP53, RB1, CCND1, CDKN2A and CCNE1) was dominant, followed by PI3K/AKT cascade (PIK3CA, AKT3, MTOR, TSC1 and TSC2), while genes of WNT signaling pathway (CTNNB1, APC and AXIN2) were mutated at a lower frequency. In addition, 69 variants in 25 DNA damage repair (DDR) genes were identified in 37 (45.7%) patients. Patients with DDR mutations had a higher tumor mutation burden (TMB) than those without DDR mutations. Conclusions: This study revealed a unique genomic landscape of HBV-related HCC. Besides TP53 being the highest mutated gene, a significant fraction of patients was identified with TERT promoter mutations, suggesting that TERT may play a role in HBV-related hepatocarcinogenesis as a novel molecular marker. Furthermore, the most common biological processes affected by HBV status in HCC were cell cycle control, PI3K/AKT and WNT signaling pathways. The possible synergistic effects of HBV in hepatocarcinogenesis warrant further investigations.

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

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.

scholarly journals Identification and Validation of Ubiquitin-Specific Proteases as a Novel Prognostic Signature for Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Wenkai Ni ◽  
Saiyan Bian ◽  
Mengqi Zhu ◽  
Qianqian Song ◽  
Jianping Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Cancer Genome ◽  
Advanced Tumor Stage ◽  
Genomic Alterations ◽  
Advanced Tumor

PurposeUbiquitin-specific proteases (USPs), as a sub-family of deubiquitinating enzymes (DUBs), are responsible for the elimination of ubiquitin-triggered modification. USPs are recently correlated with various malignancies. However, the expression features and clinical significance of USPs have not been systematically investigated in hepatocellular carcinoma (HCC).MethodsGenomic alterations and expression profiles of USPs were investigated in CbioPortal and The Cancer Genome Atlas (TCGA) Liver hepatocellular carcinoma (LIHC) dataset. Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were conducted to establish a risk signature for HCC prognosis in TCGA LIHC cohort. Subsequently, Kaplan-Meier analysis, receiver operating characteristic (ROC) curves and univariate/multivariate analyses were performed to evaluate the prognostic significance of the risk signature in TCGA LIHC and international cancer genome consortium (ICGC) cohorts. Furthermore, we explored the alterations of the signature genes during hepatocarcinogenesis and HCC progression in GSE89377. In addition, the expression feature of USP39 was further explored in HCC tissues by performing western blotting and immunohistochemistry.ResultsGenomic alterations and overexpression of USPs were observed in HCC tissues. The consensus analysis indicated that the USPs-overexpressed sub-Cluster was correlated with aggressive characteristics and poor prognosis. Cox regression with LASSO algorithm identified a risk signature formed by eight USPs for HCC prognosis. High-risk group stratified by the signature score was correlated with advanced tumor stage and poor survival HCC patients in TCGA LIHC cohort. In addition, the 8-USPs based signature could also robustly predict overall survival of HCC patients in ICGC(LIRI-JP) cohort. Furthermore, gene sets enrichment analysis (GSEA) showed that the high-risk score was associated with tumor-related pathways. According to the observation in GSE89377, USP39 expression was dynamically increased with hepatocarcinogenesis and HCC progression. The overexpression of USP39 was further determined in a local HCC cohort and correlated with poor prognosis. The co-concurrence analysis suggested that USP39 might promote HCC by regulating cell-cycle- and proliferation- related genes.ConclusionThe current study provided a USPs-based signature, highlighting its robust prognostic significance and targeted value for HCC treatment.

scholarly journals Identification of Core Genes Related to Progression and Prognosis of Hepatocellular Carcinoma and Small-Molecule Drug Predication

2021 ◽  
Vol 12 ◽  
Author(s):  
Nan Jiang ◽  
Xinzhuo Zhang ◽  
Dalian Qin ◽  
Jing Yang ◽  
Anguo Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Small Molecule ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Gene Expression Signature ◽  
Cox Regression Analysis ◽  
Small Molecule Drugs

BackgroundHepatocellular carcinoma (HCC) is one of the most leading causes of cancer death with a poor prognosis. However, the underlying molecular mechanisms are largely unclear, and effective treatment for it is limited. Using an integrated bioinformatics method, the present study aimed to identify the key candidate prognostic genes that are involved in HCC development and identify small-molecule drugs with treatment potential.Methods and ResultsIn this study, by using three expression profile datasets from Gene Expression Omnibus database, 1,704 differentially expressed genes were identified, including 671 upregulated and 1,033 downregulated genes. Then, weighted co-expression network analysis revealed nine modules are related with pathological stage; turquoise module was the most associated module. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway analyses (KEGG) indicated that these genes were enriched in cell division, cell cycle, and metabolic related pathways. Furthermore, by analyzing the turquoise module, 22 genes were identified as hub genes. Based on HCC data from gene expression profiling interactive analysis (GEPIA) database, nine genes associated with progression and prognosis of HCC were screened, including ANLN, BIRC5, BUB1B, CDC20, CDCA5, CDK1, NCAPG, NEK2, and TOP2A. According to the Human Protein Atlas and the Oncomine database, these genes were highly upregulated in HCC tumor samples. Moreover, multivariate Cox regression analysis showed that the risk score based on the gene expression signature of these nine genes was an independent prognostic factor for overall survival and disease-free survival in HCC patients. In addition, the candidate small-molecule drugs for HCC were identified by the CMap database.ConclusionIn conclusion, the nine key gene signatures related to HCC progression and prognosis were identified and validated. The cell cycle pathway was the core pathway enriched with these key genes. Moreover, several candidate molecule drugs were identified, providing insights into novel therapeutic approaches for HCC.

Identification of Iron Metabolism-Related Genes Signature and Novel Role of FLVCR1 in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Jianhui Chen ◽  
Chuan HU ◽  
Reguang Pan ◽  
Xuedan Du ◽  
Haotian Fu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Iron Metabolism ◽  
Clustering Analysis ◽  
Cox Regression ◽  
Treatment Strategies ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Lasso Regression ◽  
Prognostic Signature

Abstract Background: Hepatocellular carcinoma (HCC) is the main and highly malignant histological subtype of liver cancer. We tried to construct a novel signature with iron metabolism-related genes to provide new therapeutic targets and improve the prognosis for HCC patients.Methods: The gene expression data of 70 iron metabolism-related genes and its relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Consensus clustering analysis was performed to determine clusters of HCC patients with different OS. Cox regression and LASSO regression analyses were used to establish a prognostic signature. Receiver operating characteristic (ROC) and Kaplan–Meier analyses were carried out to examine the predicated performance of the signature.Results: Consensus clustering analysis determined two clusters of HCC patients with different OS(p<0.01), TNM stage(p<0.05) and pathological grade(p<0.05). A nine-gene prognostic signature established with iron metabolism-related genes can independently predicate the prognostic of HCC patients. The ROC curves showed a great performance of the signature. In addition, FLVCR1, a hub gene with the highest mutation frequency in our signature, showed the significantly prognostic value in HCC patients. High FLVCR1 expression was significantly associated with poor prognosis and aggressive progression in HCC patients. The promoter methylation level of FLVCR1 was lower in HCC samples with aggressive progression status. The FLVCR1 expression was positively correlated with the infiltration level of B cell, CD4+ T cell, macrophage, neutrophil and dendritic cell. Conclusion: Our study first established a signature related to iron metabolism and identified FLVCR1 as a potential therapeutic target. These findings provided more treatment strategies for HCC patients.

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