Intratumoral microbial heterogeneity affected tumor immune microenvironment and determined clinical outcome of HBV-related HCC

亚型 肝细胞癌 微生物群 生物 肿瘤微环境 丙型肝炎病毒 乙型肝炎病毒 免疫系统 癌变 病毒 癌症研究 基因 免疫学 生物信息学 遗传学 程序设计语言 计算机科学
作者
Shengnan Li,Han Xia,Zeyu Wang,Xiehua Zhang,Tianqiang Song,Jia Li,Liang Xu,Ningning Zhang,Shu Fan,Qian Li,Qiaoling Zhang,Yingnan Ye,Jiayu Lv,Xiaofen Yue,Hongcheng Lv,Jinpu Yu,Wei Lü
出处
期刊:Hepatology [Wiley]
卷期号:78 (4): 1079-1091 被引量:15
标识
DOI:10.1097/hep.0000000000000427
摘要

Background and Aims: The intratumoral microbiome has been reported to regulate the development and progression of cancers. We aimed to characterize intratumoral microbial heterogeneity (IMH) and establish microbiome-based molecular subtyping of HBV-related HCC to elucidate the correlation between IMH and HCC tumorigenesis. Approach and Results: A case-control study was designed to investigate microbial landscape and characteristic microbial signatures of HBV-related HCC tissues adopting metagenomics next-generation sequencing. Microbiome-based molecular subtyping of HCC tissues was established by nonmetric multidimensional scaling. The tumor immune microenvironment of 2 molecular subtypes was characterized by EPIC and CIBERSORT based on RNA-seq and verified by immunohistochemistry. The gene set variation analysis was adopted to explore the crosstalk between the immune and metabolism microenvironment. A prognosis-related gene risk signature between 2 subtypes was constructed by the weighted gene coexpression network analysis and the Cox regression analysis and then verified by the Kaplan-Meier survival curve. IMH demonstrated in HBV-related HCC tissues was comparably lower than that in chronic hepatitis tissues. Two microbiome-based HCC molecular subtypes, defined as bacteria- and virus-dominant subtypes, were established and significantly correlated with discrepant clinical-pathologic features. Higher infiltration of M2 macrophage was detected in the bacteria-dominant subtype with to the virus-dominant subtype, accompanied by multiple upregulated metabolism pathways. Furthermore, a 3-gene risk signature containing CSAG4 , PIP4P2 , and TOMM5 was filtered out, which could predict the clinical prognosis of HCC patients accurately using the Cancer Genome Atlas data. Conclusions: Microbiome-based molecular subtyping demonstrated IMH of HBV-related HCC was correlated with a disparity in clinical-pathologic features and tumor microenvironment (TME), which might be proposed as a biomarker for prognosis prediction of HCC.
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