A Prognostic Model of 15 Immune-Related Gene Pairs Associated With Tumor Mutation Burden for Hepatocellular Carcinoma.

Junyu Huo,Yunjin Zang,Liqun Wu

doi:10.3389/fmolb.2020.581354

Abstract

IntroductionTumor mutation burden (TMB) is an emerging biomarker for immunotherapy of hepatocellular carcinoma (HCC), but its value for clinical application has not been fully revealed.Materials and MethodsWe used the Wilcox test to identify the differentially expressed immune-related genes (DEIRGs) in groups with high and low TMB as well as screened the immune gene pairs related to prognosis using univariate Cox regression analysis. A LASSO Cox regression prognostic model was developed by combining The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) with the International Cancer Genome Consortium (ICGC) LIRI-JP cohort, and internal (TCGA, ICGC) and external (GSE14520) validation analyses were conducted on the predictive value of the model. We also explored the relationship between the prognostic model and tumor microenvironment via the ESTIMATE algorithm and performed clinical correlation analysis by the chi-square test, revealing its underlying molecular mechanism with the help of Gene Set Enrichment Analysis (GSEA).ResultsThe prognostic model consisting of 15 immune gene pairs showed high predictive value for short- and long-term survival of HCC in three independent cohorts. Based on univariate multivariate Cox regression analysis, the prognostic model could be used to independently predict the prognosis in each independent cohort. The immune score, stromal score, and estimated score values were lower in the high-risk group than in the low-risk group. As shown by the chi-square test, the prognostic model exhibited an obvious correlation with the tumor stage [American Joint Committee on Cancer tumor–node–metastasis (AJCC-TNM) (p < 0.001), Barcelona Clinic Liver Cancer (BCLC) (p = 0.003)], histopathological grade (p = 0.033), vascular invasion (p = 0.009), maximum tumor diameter (p = 0.013), and background of liver cirrhosis (p < 0.001). GSEA revealed that the high-risk group had an enrichment of many oncology features, including the cell cycle, mismatch repair, DNA replication, RNA degradation, etc.ConclusionOur research developed and validated a reliable prognostic model associated with TMB for HCC, which may help to further enrich the therapeutic targets of HCC.

Highlights

Tumor mutation burden (TMB) is an emerging biomarker for immunotherapy of hepatocellular carcinoma (HCC), but its value for clinical application has not been fully revealed
A least absolute shrinkage and selection operator (LASSO) Cox regression prognostic model was developed by combining The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) with the International Cancer Genome Consortium (ICGC) LIRI-JP cohort, and internal (TCGA, ICGC) and external (GSE14520) validation analyses were conducted on the predictive value of the model
The prognostic model consisting of 15 immune gene pairs showed high predictive value for short- and long-term survival of HCC in three independent cohorts

Summary

Introduction

Tumor mutation burden (TMB) is an emerging biomarker for immunotherapy of hepatocellular carcinoma (HCC), but its value for clinical application has not been fully revealed. Tumor mutation burden (TMB) is defined as the total replacement and insertion/deletion mutation number for each megabase in the exon coding region regarding the evaluated gene of a tumor sample (Galuppini et al, 2019). It is a new biomarker for predicting the benefit of the treatment of tumor immune checkpoint inhibitors (ICIs) for various kinds of tumors (Goodman et al, 2017), such as lung cancer, colorectal cancer, prostate cancer, and breast cancer (Antonarakis, 2019; Schrock et al, 2019; Alborelli et al, 2020; Jang et al, 2020). Increasing evidence has shown that the higher the TMB is, the more new antigens can be recognized by T cells and the better the effect of immunotherapy is (Kim et al, 2019), research on the interaction between TMB and HCC prognosis is still relatively insufficient

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