Xinyu Gu1, Jun Guan1, Jia Xu1, Qiuxian Zheng1, Chao Chen1, Qin Yang1, Chunhong Huang1, Gang Wang1, Haibo Zhou1, Zhi Chen1, Haihong Zhu2. 1. State 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 University, NO. 79 Qingchun Road, Hangzhou, 310003, Zhejiang, China. 2. State 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 University, NO. 79 Qingchun Road, Hangzhou, 310003, Zhejiang, China. zhuhh72@zju.edu.cn.
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.
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 HCCpatients.
Entities:
Keywords:
HCC; Immune environment; Immunotherapy; Prognosis; Risk model
Authors: Sanjeev Mariathasan; Shannon J Turley; Dorothee Nickles; Alessandra Castiglioni; Kobe Yuen; Yulei Wang; Edward E Kadel; Hartmut Koeppen; Jillian L Astarita; Rafael Cubas; Suchit Jhunjhunwala; Romain Banchereau; Yagai Yang; Yinghui Guan; Cecile Chalouni; James Ziai; Yasin Şenbabaoğlu; Stephen Santoro; Daniel Sheinson; Jeffrey Hung; Jennifer M Giltnane; Andrew A Pierce; Kathryn Mesh; Steve Lianoglou; Johannes Riegler; Richard A D Carano; Pontus Eriksson; Mattias Höglund; Loan Somarriba; Daniel L Halligan; Michiel S van der Heijden; Yohann Loriot; Jonathan E Rosenberg; Lawrence Fong; Ira Mellman; Daniel S Chen; Marjorie Green; Christina Derleth; Gregg D Fine; Priti S Hegde; Richard Bourgon; Thomas Powles Journal: Nature Date: 2018-02-14 Impact factor: 49.962