AUTHOR=Chen Wenbiao , Zhang Xujun , Bi Kefan , Zhou Hetong , Xu Jia , Dai Yong , Diao Hongyan 

TITLE=Comprehensive Study of Tumor Immune Microenvironment and Relevant Genes in Hepatocellular Carcinoma Identifies Potential Prognostic Significance

JOURNAL=Frontiers in Oncology

VOLUME=Volume 10 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.554165

DOI=10.3389/fonc.2020.554165

ISSN=2234-943X

ABSTRACT=Background: Tumor immune microenvironment (TIME) is the external immune system that regulates tumorigenesis. However, the cellular interactions in TIME of hepatocellular carcinoma (HCC) are poorly characterized. 
Methods: In this study, we used multidimensional bioinformatics methods to comprehensively analyze TIME cellular characteristics in 735 HCC patients. Besides, we explored the association between TIME molecular subtypes and gene types and clinicopathological features to construct a prognostic signature. 
Results: Based on their characteristics, we classified TIME and gene signatures into three phenotypes (TIME T1-3) and two gene clusters (Gene G1-2), respectively. Further analysis revealed Gene G1 were associated with immune activation and surveillance and include CD8+ T cells, natural killer cells activation and activated CD4+ memory T cells. On the contrary, Gene G2 was characterized by increase in M0 macrophages and regulatory T cells. After the calculation of principal component algorithms, a TIME score (TS) model that contained 78 different expression genes was constructed based on the TIME phenotypes and gene clusters. Further, we observed that Gene G2 cluster was characterized by high TS while Gene G1 was characterized by low TS which correlated with poor and favorable prognosis of HCC, respectively. Correlation analysis showed that TS had a positive correlation with several clinicopathologic signatures (such as grade, stage, tumor (T) and node (N)) and known somatic mutations of gene expression (such as TP53 and CTNNB1). The prognostic value of the TS model was verified using external data sets. 
Conclusion: We constructed a TS model based on differentially expressed genes among immune phenotypes and demonstrated that the TS model is an effective prognostic biomarker and predictor for HCC patients.