Abstract
BackgroundHepatocellular carcinoma (HCC) was the sixth common malignancies characteristic with highly aggressive in the world. It was well established that tumor mutation burden (TMB) act as indicator of immunotherapeutic responsiveness in various tumors. However, the role of TMB in tumor immune microenvironment (TIME) is still obscure.MethodThe mutation data was analyzed by employing “maftools” package. Weighted gene co-expression network analysis (WGCNA) was implemented to determine candidate module and significant genes correlated with TMB value. Differential analysis was performed between different level of TMB subgroups employing R package “limma”. Gene ontology (GO) enrichment analysis was implemented with “clusterProfiler”, “enrichplot” and “ggplot2” packages. Then risk score signature was developed by systematical bioinformatics analyses. K-M survival curves and receiver operating characteristic (ROC) plot were further analyzed for prognostic validity. To depict comprehensive context of TIME, XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT, and CIBERSORT-ABS algorithm were employed. Additionally, the potential role of risk score on immune checkpoint blockade (ICB) immunotherapy was further explored. The quantitative real-time polymerase chain reaction was performed to detect expression of HTRA3.ResultsTMB value was positively correlated with older age, male gender and early T status. A total of 75 intersection genes between TMB-related genes and differentially expressed genes (DEGs) were screened and enriched in extracellular matrix-relevant pathways. Risk score based on three hub genes significantly affected overall survival (OS) time, infiltration of immune cells, and ICB-related hub targets. The prognostic performance of risks score was validated in the external testing group. Risk-clinical nomogram was constructed for clinical application. HTRA3 was demonstrated to be a prognostic factor in HCC in further exploration. Finally, mutation of TP53 was correlated with risk score and do not interfere with risk score-based prognostic prediction.ConclusionCollectively, a comprehensive analysis of TMB might provide novel insights into mutation-driven mechanism of tumorigenesis further contribute to tailored immunotherapy and prognosis prediction of HCC.
Highlights
Primary liver cancer is one of the most prevalent and aggressive malignancies whose incidence has raised rapidly in the world [1,2,3]
Risk score based on three hub genes significantly affected overall survival (OS) time, infiltration of immune cells, and immune checkpoint blockade (ICB)-related hub targets
Collectively, a comprehensive analysis of tumor mutation burden (TMB) might provide novel insights into mutation-driven mechanism of tumorigenesis further contribute to tailored immunotherapy and prognosis prediction of Hepatocellular carcinoma (HCC)
Summary
Primary liver cancer is one of the most prevalent and aggressive malignancies whose incidence has raised rapidly in the world [1,2,3]. Approximately 80% of liver cancer cases were hepatocellular carcinoma (HCC) [2]. Given that its high heterogeneity and etiologies differs well among different patients, tumor‐ node‐metastasis (TNM) staging as widely used clinical classification achieved little in predicting overall survival and clinical outcome [8,9,10]. It is imperative, to generate robust tools for prognosis prediction and therapeutic response assessment, further facilitate precision and individualized treatment. The role of TMB in tumor immune microenvironment (TIME) is still obscure
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