Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes
Abstract Background Although the tumour immune microenvironment is known to significantly influence immunotherapy outcomes, its association with changes in gene expression patterns in hepatocellular carcinoma (HCC) during immunotherapy and its effect on prognosis have not been clarified. Methods A t...
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BMC
2021-01-01
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Series: | Journal of Translational Medicine |
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Online Access: | https://doi.org/10.1186/s12967-020-02691-4 |
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doaj-16ea39f77b0347c1a2dee35b017790aa |
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Article |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xinyu Gu Jun Guan Jia Xu Qiuxian Zheng Chao Chen Qin Yang Chunhong Huang Gang Wang Haibo Zhou Zhi Chen Haihong Zhu |
spellingShingle |
Xinyu Gu Jun Guan Jia Xu Qiuxian Zheng Chao Chen Qin Yang Chunhong Huang Gang Wang Haibo Zhou Zhi Chen Haihong Zhu Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes Journal of Translational Medicine HCC Risk model Immune environment Prognosis Immunotherapy |
author_facet |
Xinyu Gu Jun Guan Jia Xu Qiuxian Zheng Chao Chen Qin Yang Chunhong Huang Gang Wang Haibo Zhou Zhi Chen Haihong Zhu |
author_sort |
Xinyu Gu |
title |
Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes |
title_short |
Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes |
title_full |
Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes |
title_fullStr |
Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes |
title_full_unstemmed |
Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes |
title_sort |
model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes |
publisher |
BMC |
series |
Journal of Translational Medicine |
issn |
1479-5876 |
publishDate |
2021-01-01 |
description |
Abstract Background Although the tumour immune microenvironment is known to significantly influence immunotherapy outcomes, its association with changes in gene expression patterns in hepatocellular carcinoma (HCC) during immunotherapy and its effect on prognosis have not been clarified. Methods A total of 365 HCC samples from The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) dataset were stratified into training datasets and verification datasets. In the training datasets, immune-related genes were analysed through univariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO)-Cox analyses to build a prognostic model. The TCGA-LIHC, GSE14520, and Imvigor210 cohorts were subjected to time-dependent receiver operating characteristic (ROC) and Kaplan–Meier survival curve analyses to verify the reliability of the developed model. Finally, single-sample gene set enrichment analysis (ssGSEA) was used to study the underlying molecular mechanisms. Results Five immune-related genes (LDHA, PPAT, BFSP1, NR0B1, and PFKFB4) were identified and used to establish the prognostic model for patient response to HCC treatment. ROC curve analysis of the TCGA (training and validation sets) and GSE14520 cohorts confirmed the predictive ability of the five-gene-based model (AUC > 0.6). In addition, ROC and Kaplan–Meier analyses indicated that the model could stratify patients into a low-risk and a high-risk group, wherein the high-risk group exhibited worse prognosis and was less sensitive to immunotherapy than the low-risk group. Functional enrichment analysis predicted potential associations of the five genes with several metabolic processes and oncological signatures. Conclusions We established a novel five-gene-based prognostic model based on the tumour immune microenvironment that can predict immunotherapy efficacy in HCC patients. |
topic |
HCC Risk model Immune environment Prognosis Immunotherapy |
url |
https://doi.org/10.1186/s12967-020-02691-4 |
work_keys_str_mv |
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doaj-16ea39f77b0347c1a2dee35b017790aa2021-01-10T12:16:12ZengBMCJournal of Translational Medicine1479-58762021-01-0119111310.1186/s12967-020-02691-4Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomesXinyu Gu0Jun Guan1Jia Xu2Qiuxian Zheng3Chao Chen4Qin Yang5Chunhong Huang6Gang Wang7Haibo Zhou8Zhi Chen9Haihong Zhu10State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityAbstract Background Although the tumour immune microenvironment is known to significantly influence immunotherapy outcomes, its association with changes in gene expression patterns in hepatocellular carcinoma (HCC) during immunotherapy and its effect on prognosis have not been clarified. Methods A total of 365 HCC samples from The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) dataset were stratified into training datasets and verification datasets. In the training datasets, immune-related genes were analysed through univariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO)-Cox analyses to build a prognostic model. The TCGA-LIHC, GSE14520, and Imvigor210 cohorts were subjected to time-dependent receiver operating characteristic (ROC) and Kaplan–Meier survival curve analyses to verify the reliability of the developed model. Finally, single-sample gene set enrichment analysis (ssGSEA) was used to study the underlying molecular mechanisms. Results Five immune-related genes (LDHA, PPAT, BFSP1, NR0B1, and PFKFB4) were identified and used to establish the prognostic model for patient response to HCC treatment. ROC curve analysis of the TCGA (training and validation sets) and GSE14520 cohorts confirmed the predictive ability of the five-gene-based model (AUC > 0.6). In addition, ROC and Kaplan–Meier analyses indicated that the model could stratify patients into a low-risk and a high-risk group, wherein the high-risk group exhibited worse prognosis and was less sensitive to immunotherapy than the low-risk group. Functional enrichment analysis predicted potential associations of the five genes with several metabolic processes and oncological signatures. Conclusions We established a novel five-gene-based prognostic model based on the tumour immune microenvironment that can predict immunotherapy efficacy in HCC patients.https://doi.org/10.1186/s12967-020-02691-4HCCRisk modelImmune environmentPrognosisImmunotherapy |