Integrative analysis of multi-omics data identified PLG as key gene related to Anoikis resistance and immune phenotypes in hepatocellular carcinoma

作者：Xueyan Wang, Lei Gao, Haiyuan Li, Yanling Ma, Bofang Wang, Baohong Gu, X. Li, Lin Xiang, Yuping Bai, Chenhui Ma, Hao Chen · 发表于：Journal of Translational Medicine · 年份：2024 · DOI：10.1186/s12967-024-05858-5 · 被引用次数：6 · 研究领域：Ferroptosis and cancer prognosis、Hepatocellular Carcinoma Treatment and Prognosis、Clusterin in disease pathology

PURPOSE: The extracellular matrix (ECM) plays a pivotal role in the initiation and progression of hepatocellular carcinoma (HCC) by facilitating the proliferation of HCC cells and enabling resistance to Anoikis. ECM also provide structural support that aids in the invasion of HCC cells, thereby influencing the tumor microenvironment. Due to genetic variations and molecular heterogeneity, significant challenges exist in the treatment of HCC, particularly with immunotherapy, which frequently leads to immune tolerance and suboptimal immune responses. Therefore, there is an urgent need for a multi-omics-based classification system for HCC that clarifies the molecular mechanisms underlying the establishment of immune phenotypes and Anoikis resistance in HCC cells. In this study, we employed advanced clustering algorithms to analyze and integrate multi-omics data from HCC patients, with the objective of identifying key genes that possess prognostic potential associated with the Anoikis resistance phenotype. This methodology resulted in the development of a consensus machine learning-driven signature (CMLS), which demonstrates robust predictive capabilities by examining variations in epigenetics, transcription, and immune metabolism, as well as their effects on the core differential gene, plasminogen (PLG). RESULTS: The integrated multi-omics approach has identified PLG as a critical node within the gene regulatory network associated with Anoikis resistance and immunometabolic pheno...