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

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.

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Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

10.21203/rs.3.rs-17851/v1 ◽

2020 ◽

Author(s):

Li Liu ◽

She Tian ◽

Zhu Li ◽

Yongjun Gong ◽

Hao Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Survival Curve ◽

Principal Component ◽

High Risk Group ◽

Low Risk ◽

Cox Regression Analysis ◽

Immune Related Genes

Abstract Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

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An autophagy-related long non-coding RNA signature for patients with colorectal cancer

Physiology International ◽

10.1556/2060.2021.00125 ◽

2021 ◽

Author(s):

Dongyan Zhao ◽

Xizhen Sun ◽

Sidan Long ◽

Shukun Yao

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Cox Regression Analysis

AbstractAimLong non-coding RNAs (lncRNAs) have been identified to regulate cancers by controlling the process of autophagy and by mediating the post-transcriptional and transcriptional regulation of autophagy-related genes. This study aimed to investigate the potential prognostic role of autophagy-associated lncRNAs in colorectal cancer (CRC) patients.MethodsLncRNA expression profiles and the corresponding clinical information of CRC patients were collected from The Cancer Genome Atlas (TCGA) database. Based on the TCGA dataset, autophagy-related lncRNAs were identified by Pearson correlation test. Univariate Cox regression analysis and the least absolute shrinkage and selection operator analysis (LASSO) Cox regression model were performed to construct the prognostic gene signature. Gene set enrichment analysis (GSEA) was used to further clarify the underlying molecular mechanisms.ResultsWe obtained 210 autophagy-related genes from the whole dataset and found 1187 lncRNAs that were correlated with the autophagy-related genes. Using Univariate and LASSO Cox regression analyses, eight lncRNAs were screened to establish an eight-lncRNA signature, based on which patients were divided into the low-risk and high-risk group. Patients’ overall survival was found to be significantly worse in the high-risk group compared to that in the low-risk group (log-rank p = 2.731E-06). ROC analysis showed that this signature had better prognostic accuracy than TNM stage, as indicated by the area under the curve. Furthermore, GSEA demonstrated that this signature was involved in many cancer-related pathways, including TGF-β, p53, mTOR and WNT signaling pathway.ConclusionsOur study constructed a novel signature from eight autophagy-related lncRNAs to predict the overall survival of CRC, which could assistant clinicians in making individualized treatment.

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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.

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Identification of Seven-Gene Hypoxia Signature for Predicting Overall Survival of Hepatocellular Carcinoma

10.21203/rs.3.rs-121134/v1 ◽

2020 ◽

Author(s):

YuPing Bai ◽

Wenbo Qi ◽

Le Liu ◽

Jing Zhang ◽

Lan Pang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Prognostic Models ◽

Cox Regression Analysis ◽

Significant Difference

Abstract Background: Hepatocellular carcinoma is ranked fifth among the most common cancer worldwide. Hypoxia can induce tumor growth, but the relationship with HCC prognosis remains unclear. Our study aims to construct a hypoxia-related multigene model to predict the prognosis of HCC. Methods: RNA-seq expression data and related clinical information were download from TCGA database and ICGC database, respectively. Univariate/multivariate Cox regression analysis was used to construct prognostic models. KM curve analysis, and ROC curve were used to evaluate the prognostic models, which were further verified in the clinical traits and ICGC database. GSEA analyzed pathway enrichment in high-risk groups. Nomogram was constructed to predict the personalized treatment of patients. Finally, real-time fluorescence quantitative PCR(RT-qPCR) was used to detect the expressions of KDELR3 and SCARB1 in normal hepatocytes and 4 hepatocellular carcinoma cells. Results: Through a series of analyses, 7 prognostic markers related to HCC survival were constructed. HCC patients were divided into the high and low risk group, and the results of KM curve showed that there was a significant difference between the two groups. Stratified analysis，found that there were significant differences in risk values of different ages, genders, stages and grades, which could be used as independent predictors. In addition, we assessed the risk value in the clinical traits analysis and found that it could accelerate the progression of cancer, while the results of GSEA enrichment analysis showed that the high-risk group patients were mainly distributed in the cell cycle and other pathways. Then, Nomogram was constructed to predict the overall survival of patients. Finally, RT-qPCR showed that KDELR3 and SCARB1 were highly expressed in HepG2 and L02, respectively. Conclusion: This study provides a potential diagnostic indicator for HCC patients, and help clinicians to deepen the comprehension in HCC pathogenesis so as to make personalized medical decisions.

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Identification of Biomarkers of Hepatocellular Carcinoma Gene Prognosis Based on Immune-Related lncRNA Signature of Transcriptome Data

10.21203/rs.3.rs-57303/v1 ◽

2020 ◽

Author(s):

Andi Ma ◽

Yukai Sun ◽

Racheal O. Ogbodu ◽

Ling Xiao ◽

Haibing Deng ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis ◽

Cox Regression Analysis

Abstract Background: It is well known that long non-coding RNAs (lncRNAs) play a vital role in cancer. We aimed to explore the prognostic value of potential immune-related lncRNAs in hepatocellular carcinoma (HCC). Methods: Validated the established lncRNA signature of 343 patients with HCC from The Cancer Genome Atlas (TCGA) and 81 samples from Gene Expression Omnibus (GEO). Immune-related lncRNAs for HCC prognosis were evaluated using Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. LASSO analysis was performed to calculate a risk score formula to explore the difference in overall survival between high- and low-risk groups in TCGA, which was verified using GEO, Gene Ontology (GO), and pathway-enrichment analysis. These analyses were used to identify the function of screened genes and construct a co-expression network of these genes. Results: Using computational difference algorithms and lasso Cox regression analysis, the differentially expressed and survival-related immune-related genes (IRGs) among patients with HCC were established as five novel immune-related lncRNA signatures (AC099850.3, AL031985.3, PRRT3-AS1, AC023157.3, MSC-AS1). Patients in the low‐risk group showed significantly better survival than patients in the high‐risk group ( P = 3.033e−05). The signature identified can be an effective prognostic factor to predict patient survival. The nomogram showed some clinical net benefits predicted by overall survival. In order to explore its underlying mechanism, several methods of enrichment were elucidated using Gene Set Enrichment Analysis. Conclusion: Identifying five immune-related lncRNA signatures has important clinical implications for predicting patient outcome and guiding tailored therapy for patients with HCC with further prospective validation.

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Identification of Immune-Related Genes for Establishment of Prognostic Index in Hepatocellular Carcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.760079 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yinfang Li ◽

Ling Zou ◽

Xuejun Liu ◽

Judong Luo ◽

Hui Liu

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Prognostic Index ◽

Hub Genes ◽

Cox Regression Analysis ◽

Risk Patients ◽

Immune Related Genes

Background: Immune checkpoint inhibitor (ICI) therapy has been proved to be a promising therapy to many types of solid tumors. However, effective biomarker for estimating the response to ICI therapy and prognosis of hepatocellular carcinoma (HCC) patients remains underexplored. The aim of this study is to build a novel immune-related prognostic index based on transcriptomic profiles.Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to identify immune-related hub genes that are differentially expressed in HCC cohorts. Next, univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to detect hub genes associated to overall survival (OS). To validate the immune-related prognostic index, univariate and multivariate Cox regression analysis were performed. CIBERSORT and ESTIMATE were used to explore the tumor microenvironment and immune infiltration level.Results: The differential expression analysis detected a total of 148 immune-related genes, among which 25 genes were identified to be markedly related to overall survival in HCC patients. LASSO analysis yielded 10 genes used to construct the immune-related gene prognostic index (IRGPI), by which a risk score is computed to estimate low vs. high risk indicating the response to ICI therapy and prognosis. Further analysis confirmed that this immune-related prognostic index is an effective indicator to immune infiltration level, response to ICI treatment and OS. The IRGPI low-risk patients had better overall survival (OS) than IRGPI high-risk patients on two independent cohorts. Moreover, we found that IRGPI high-risk group was correlated with high TP53 mutation rate, immune-suppressing tumor microenvironment, and these patients acquired less benefit from ICI therapy. In contrast, IRGPI-low risk group was associated with low TP53 and PIK3CA mutation rate, high infiltration of naive B cells and T cells, and these patients gained relatively more benefit from ICI therapy.

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A Prognostic Model for Hepatocellular Carcinoma Patients Based on Signature Ferroptosis-related Genes

10.21203/rs.3.rs-614871/v1 ◽

2021 ◽

Author(s):

Sizhe Wan ◽

Yiming Lei ◽

Mingkai Li ◽

Bin Wu

Keyword(s):

Overall Survival ◽

T Cells ◽

High Risk ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Tnm Stage ◽

Functional Enrichment

Abstract BackgroundWith the increasing number of HCC patients, it is necessary to accurately predicting the prognosis of these patients. Ferroptosis has been confirmed to be closely related to HCC progression. However, there is still a challenge in predicting the survival of HCC patients through ferroptosis-related genes.MethodThe RNA-seq data and corresponding clinical data of HCC from TCGA database were downloaded to establish a prognosis model, and data of ICGC and GSE14520 as the validation set. The risk score was constructed with 5 genes identified by univariate and LASSO Cox regression analysis. Then, risk score, TNM stage and cirrhosis were included to construct a nomogram, through univariate and multivariate Cox regression analysis.Results5 genes were identified from 70 ferroptosis-related DEGs to construct a gene signature to predict HCC patient survival from TCGA cohort. PCA and heatmap results show that there are obvious differences in patients with different score groups. Then, we included risk score, TNM stage and cirrhosis to construct a nomogram to further predict the overall survival of the patients. Survival analysis indicates that overall survival of the low- risk group is significantly higher than that of the high-risk group. Similarly, the data in the GSE14520 cohort also confirmed good performance for the nomogram. Furthermore, KEGG and GO functional enrichment analyses indicates the difference in overall survival between groups is closely related to immune-related pathways. Finally, through analyzing the immune status of all patients, we found that compared with patients in the low-risk group, “Macrophages M0”, “T cells CD8”, and “T cells regulatory” of the high-risk group were significantly higher.ConclusionThe nomogram based on ferroptosis-related genes has a good performance for the prognosis of HCC patients. The model may provide a reference for evaluation of HCC patients by targeting ferroptosis.

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Identification of Seven-Gene Hypoxia Signature for Predicting Overall Survival of Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.637418 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yuping Bai ◽

Wenbo Qi ◽

Le Liu ◽

Jing Zhang ◽

Lan Pang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Disease Free Survival ◽

High Risk Group ◽

Prognostic Models ◽

Cox Regression Analysis ◽

Significant Difference

BackgroundHepatocellular carcinoma (HCC) is ranked fifth among the most common cancer worldwide. Hypoxia can induce tumor growth, but the relationship with HCC prognosis remains unclear. Our study aims to construct a hypoxia-related multigene model to predict the prognosis of HCC.MethodsRNA-seq expression data and related clinical information were download from TCGA database and ICGC database, respectively. Univariate/multivariate Cox regression analysis was used to construct prognostic models. KM curve analysis, and ROC curve were used to evaluate the prognostic models, which were further verified in the clinical traits and ICGC database. GSEA analyzed pathway enrichment in high-risk groups. Nomogram was constructed to predict the personalized treatment of patients. Finally, real-time fluorescence quantitative PCR (RT-qPCR) was used to detect the expressions of KDELR3 and SCARB1 in normal hepatocytes and 4 HCC cells. The expressions of SCARB1 in hepatocellular carcinoma tissue in 46 patients were detected by immunohistochemistry, and the correlation between its expressions and disease free survival of patient was calculated.ResultsThrough a series of analyses, seven prognostic markers related to HCC survival were constructed. HCC patients were divided into the high and low risk group, and the results of KM curve showed that there was a significant difference between the two groups. Stratified analysis, found that there were significant differences in risk values of different ages, genders, stages and grades, which could be used as independent predictors. In addition, we assessed the risk value in the clinical traits analysis and found that it could accelerate the progression of cancer, while the results of GSEA enrichment analysis showed that the high-risk group patients were mainly distributed in the cell cycle and other pathways. Then, Nomogram was constructed to predict the overall survival of patients. Finally, RT-qPCR showed that KDELR3 and SCARB1 were highly expressed in HepG2 and L02, respectively. Results of IHC staining showed that SCARB1 was highly expressed in cancer tissues compared to adjacent normal liver tissues and its expression was related to hepatocellular carcinoma differentiation status. The Kaplan-Meier survival showed a poor percent survival in the SCARB1 high group compared to that in the SCARB1 low group.ConclusionThis study provides a potential diagnostic indicator for HCC patients, and help clinicians to deepen the comprehension in HCC pathogenesis so as to make personalized medical decisions.

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Development of a prognostic scoring model for predicting the survival of elderly patients with hepatocellular carcinoma

PeerJ ◽

10.7717/peerj.8497 ◽

2020 ◽

Vol 8 ◽

pp. e8497 ◽

Cited By ~ 2

Author(s):

Sizhe Wan ◽

Yuan Nie ◽

Xuan Zhu

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Factors ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Large Population ◽

Risk Groups ◽

Low Risk ◽

Cox Regression Analysis ◽

Scoring Model

Background The number of elderly hepatocellular carcinoma (HCC) patients is increasing, and precisely assessing of the prognosis of these patients is necessary. We developed a prognostic scoring model to predict survival in elderly HCC patients. Methods We extracted data from 4,076 patients ≥65 years old from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided them into training and validation groups. Cox regression analysis was used to screen for meaningful independent prognostic factors. The receiver operating characteristic curve reflected the model’s discrimination power. Results Age, race, American Joint Committee on Cancer stage, degree of tumour differentiation, tumour size, alpha-fetoprotein and tumour therapy were independent prognostic factors for survival in elderly HCC patients. We developed a prognostic scoring model based on the seven meaningful variables to predict survival in elderly HCC patients. The AUCs of the model were 0.805 (95% CI [0.788–0.821]) and 0.788 (95% CI [0.759–0.816]) in the training and validation groups, respectively. We divided the patients into low-risk groups and high-risk groups according to the optimal cut-off value. The Kaplan–Meier survival curve showed that in the training and validation groups, the survival rate of the low-risk group was significantly higher than that of the high-risk group (P < 0.001). Conclusion Based on a large population, we constructed a prognostic scoring model for predicting survival in elderly HCC patients. The model may provide a reference for clinicians for preoperative and postoperative evaluations of elderly HCC patients.

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Development and validation of a novel ten-gene prognostic signature for hepatocellular carcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e16665 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e16665-e16665

Author(s):

Taicheng Zhou ◽

Zhihua Cai ◽

Ning Ma ◽

Wenzhuan Xie ◽

Chan Gao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Prognosis Prediction

e16665 Background: Hepatocellular carcinoma (HCC) remains a major challenge for public health worldwide and long-term outcomes remained dismal despite availability of curative treatment. We aimed to construct a multi-gene model for prognosis prediction to inform clinical management of HCC. Methods: RNA-seq data of paired tumor and normal tissue samples of HCC patients from the TCGA and GEO database were used to identify differentially expressed genes (DEGs). DEGs shared by both cohorts along with patients’ survival data of the TCGA cohort were further analyzed using univariate Cox regression and LASSO Cox regression to build a prognostic 10-gene signature, followed by validation of the signature via ICGC cohort and identification of independent prognostic predictors. A nomogram for prognosis prediction was built and Gene Set Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Results: Of 571 patients (70.93% men and 29.07% women; median age [IQR], 65 [56-72] years), a signature of 10 genes was constructed using the training cohort. In the testing and validation cohorts, the signature significantly stratified patients into low- vs high-risk groups in terms of overall survival across and within subpopulations with stage I/II and III/IV disease and remained as an independent prognostic factor in multivariate analyses (hazard ratio range, 0.13 [95% CI, 0.07-0.24; P < 0 .001] to 0.38 [95% CI, 0.2-0.71; P < 0.001]) after adjusting for clinicopathological factors. Prognosis was significantly worse in the high-risk group than in the low-risk group across cohorts (P < 0.001 for all). The 10-gene signature achieved a higher accuracy (C-index, 0.84; AUCs for 1-, 3- and 5-year OS, 0.84, 0.81 and 0.85, respectively) than 8 previously reported multigene signatures (C-index range, 0.67 to 0.73; AUCs range, 0.68 to 0.79, 0.68 to 0.80 and 0.67 to 0.78, respectively) for estimation of survival in comparable cohorts. A nomogram incorporating tumor stage and signature-based risk group showed better predictive performance for 1- and 3- year survival than for 5 year survival. Moreover, GSEA revealed that the pathways related to cell cycle regulation were more prominently enriched in the high-risk group while the low-risk group had higher enrichment of metabolic process. Conclusions: Taken together, we established a robust 10-gene signature and a nomogram to predict overall survival of HCC patients, which may help recognize high-risk patients potentially benefiting from more aggressive treatment.

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