Development and validation of a hepatocellular carcinoma classifier based on macrophage-related gene set for rapid evaluation of patient response types and prognosis

Abstract Tumor-associated macrophages (TAMs) participate in and shape the tumor microenvironment of hepatocellular carcinoma (HCC), which is closely related to the formation of tumor heterogeneity. The aim of this study is to distinguish different subtypes of patients according to the activity level of macrophage functional gene set in HCC. We collected 1203 tissue samples from TCGA, ICGC and GEO databases. Using macrophage-associated gene set (MRRGS) from GSEA database, the score of MRRGS was calculated based on gene set variation analysis (GSVA). The key MRRGS was screened by univariate COX regression analysis and LASSO regression. Finally, non-negative matrix factorization (NMF) was used to classify HCC subtypes. Six immune cell infiltration algorithms, immune checkpoint expression differences, tumor immunity and rejection (TIDE) analysis, mutation data analysis, stem cell index based on mRNA expression (mRNAsi) were used to evaluate and reveal the differences of immunity, mutation and tumor cell malignancy among different HCC subtypes. Weighted gene coexpression network (WGCNA) is used to analyze the functional mechanism involved in MRRGS. CAMP and drug sensitivity analysis are used to explore drugs for different HCC subtypes. Two machine learning algorithms assist in screening characteristic genes among subtypes to facilitate subtype discrimination. Our study divides patients into two subtype (C1 and C2) by defining 12 MRRGS, which are similar to hot and cold tumors mentioned in previous studies. The stability of the macrophage functional classifier was validated in two independent HCC cohorts and this classifier can well predict the ability of patients to respond to immunotherapy, TACE treatment and various drug. Based on the above results, we built a bioinformatics tool to help users quickly distinguish patient subtypes and prognosis. In addition, immune signals (such as PD1-PDL1 signals), mutations, metabolic abnormalities, viral infection and chemical erosion in the environment are important upstream foundations of HCC heterogeneity caused by macrophages. This provides insights into the clinical treatment and management of HCC.