Identification and Validation of a Novel Six-Gene Expression Signature for Predicting Hepatocellular Carcinoma Prognosis

BackgroundHepatocellular carcinoma (HCC) is a highly lethal disease. Effective prognostic tools to guide clinical decision-making for HCC patients are lacking.ObjectiveWe aimed to establish a robust prognostic model based on differentially expressed genes (DEGs) in HCC.MethodsUsing datasets from The...

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Autores principales:	Zongcai Yan, Meiling He, Lifeng He, Liuxia Wei, Yumei Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	hepatocellular carcinoma prognosis risk score tumor microenvironment biomarkers differential gene expression analysis Immunologic diseases. Allergy RC581-607
Acceso en línea:	https://doaj.org/article/eeb37907097b4b0c919db5b06af7743e
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spelling	oai:doaj.org-article:eeb37907097b4b0c919db5b06af7743e2021-12-01T23:13:54ZIdentification and Validation of a Novel Six-Gene Expression Signature for Predicting Hepatocellular Carcinoma Prognosis1664-322410.3389/fimmu.2021.723271https://doaj.org/article/eeb37907097b4b0c919db5b06af7743e2021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fimmu.2021.723271/fullhttps://doaj.org/toc/1664-3224BackgroundHepatocellular carcinoma (HCC) is a highly lethal disease. Effective prognostic tools to guide clinical decision-making for HCC patients are lacking.ObjectiveWe aimed to establish a robust prognostic model based on differentially expressed genes (DEGs) in HCC.MethodsUsing datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Genome Consortium (ICGC), DEGs between HCC tissues and adjacent normal tissues were identified. Using TCGA dataset as the training cohort, we applied the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analyses to identify a multi-gene expression signature. Proportional hazard assumptions and multicollinearity among covariates were evaluated while building the model. The ICGC cohort was used for validation. The Pearson test was used to evaluate the correlation between tumor mutational burden and risk score. Through single-sample gene set enrichment analysis, we investigated the role of signature genes in the HCC microenvironment.ResultsA total of 274 DEGs were identified, and a six-DEG prognostic model was developed. Patients were stratified into low- or high-risk groups based on risk scoring by the model. Kaplan–Meier analysis revealed significant differences in overall survival and progression-free interval. Through univariate and multivariate Cox analyses, the model proved to be an independent prognostic factor compared to other clinic-pathological parameters. Time-dependent receiver operating characteristic curve analysis revealed satisfactory prediction of overall survival, but not progression-free interval. Functional enrichment analysis showed that cancer-related pathways were enriched, while immune infiltration analyses differed between the two risk groups. The risk score did not correlate with levels of PD-1, PD-L1, CTLA4, or tumor mutational burden.ConclusionsWe propose a six-gene expression signature that could help to determine HCC patient prognosis. These genes may serve as biomarkers in HCC and support personalized disease management.Zongcai YanMeiling HeLifeng HeLiuxia WeiYumei ZhangFrontiers Media S.A.articlehepatocellular carcinomaprognosisrisk scoretumor microenvironmentbiomarkersdifferential gene expression analysisImmunologic diseases. AllergyRC581-607ENFrontiers in Immunology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hepatocellular carcinoma prognosis risk score tumor microenvironment biomarkers differential gene expression analysis Immunologic diseases. Allergy RC581-607
spellingShingle	hepatocellular carcinoma prognosis risk score tumor microenvironment biomarkers differential gene expression analysis Immunologic diseases. Allergy RC581-607 Zongcai Yan Meiling He Lifeng He Liuxia Wei Yumei Zhang Identification and Validation of a Novel Six-Gene Expression Signature for Predicting Hepatocellular Carcinoma Prognosis
description	BackgroundHepatocellular carcinoma (HCC) is a highly lethal disease. Effective prognostic tools to guide clinical decision-making for HCC patients are lacking.ObjectiveWe aimed to establish a robust prognostic model based on differentially expressed genes (DEGs) in HCC.MethodsUsing datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Genome Consortium (ICGC), DEGs between HCC tissues and adjacent normal tissues were identified. Using TCGA dataset as the training cohort, we applied the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analyses to identify a multi-gene expression signature. Proportional hazard assumptions and multicollinearity among covariates were evaluated while building the model. The ICGC cohort was used for validation. The Pearson test was used to evaluate the correlation between tumor mutational burden and risk score. Through single-sample gene set enrichment analysis, we investigated the role of signature genes in the HCC microenvironment.ResultsA total of 274 DEGs were identified, and a six-DEG prognostic model was developed. Patients were stratified into low- or high-risk groups based on risk scoring by the model. Kaplan–Meier analysis revealed significant differences in overall survival and progression-free interval. Through univariate and multivariate Cox analyses, the model proved to be an independent prognostic factor compared to other clinic-pathological parameters. Time-dependent receiver operating characteristic curve analysis revealed satisfactory prediction of overall survival, but not progression-free interval. Functional enrichment analysis showed that cancer-related pathways were enriched, while immune infiltration analyses differed between the two risk groups. The risk score did not correlate with levels of PD-1, PD-L1, CTLA4, or tumor mutational burden.ConclusionsWe propose a six-gene expression signature that could help to determine HCC patient prognosis. These genes may serve as biomarkers in HCC and support personalized disease management.
format	article
author	Zongcai Yan Meiling He Lifeng He Liuxia Wei Yumei Zhang
author_facet	Zongcai Yan Meiling He Lifeng He Liuxia Wei Yumei Zhang
author_sort	Zongcai Yan
title	Identification and Validation of a Novel Six-Gene Expression Signature for Predicting Hepatocellular Carcinoma Prognosis
title_short	Identification and Validation of a Novel Six-Gene Expression Signature for Predicting Hepatocellular Carcinoma Prognosis
title_full	Identification and Validation of a Novel Six-Gene Expression Signature for Predicting Hepatocellular Carcinoma Prognosis
title_fullStr	Identification and Validation of a Novel Six-Gene Expression Signature for Predicting Hepatocellular Carcinoma Prognosis
title_full_unstemmed	Identification and Validation of a Novel Six-Gene Expression Signature for Predicting Hepatocellular Carcinoma Prognosis
title_sort	identification and validation of a novel six-gene expression signature for predicting hepatocellular carcinoma prognosis
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/eeb37907097b4b0c919db5b06af7743e
work_keys_str_mv	AT zongcaiyan identificationandvalidationofanovelsixgeneexpressionsignatureforpredictinghepatocellularcarcinomaprognosis AT meilinghe identificationandvalidationofanovelsixgeneexpressionsignatureforpredictinghepatocellularcarcinomaprognosis AT lifenghe identificationandvalidationofanovelsixgeneexpressionsignatureforpredictinghepatocellularcarcinomaprognosis AT liuxiawei identificationandvalidationofanovelsixgeneexpressionsignatureforpredictinghepatocellularcarcinomaprognosis AT yumeizhang identificationandvalidationofanovelsixgeneexpressionsignatureforpredictinghepatocellularcarcinomaprognosis
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Identification and Validation of a Novel Six-Gene Expression Signature for Predicting Hepatocellular Carcinoma Prognosis

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