Back to Search Start Over

Epithelial-mesenchymal transition-related gene prognostic index and phenotyping clusters for hepatocellular carcinoma patients.

Authors :
Wang, Xiaojing
Zeng, Wangyuan
Yang, Lu
Chang, Tanjie
Zeng, Jiangzheng
Source :
Cancer Genetics. Jun2023, Vol. 274, p41-50. 10p.
Publication Year :
2023

Abstract

• An EMT-related genes prognostic index could predict HCC prognosis. • The cluster C1 patients responded well to immune checkpoint inhibitors. • The cluster C2 patients were sensitive to chemotherapeutic and antiangiogenic agents. Epithelial-mesenchymal transition (EMT) contributes to high tumor heterogeneity and the immunosuppressive environment of the HCC tumor microenvironment (TME). Here, we developed EMT-related genes phenotyping clusters and systematically evaluated their impact on HCC prognosis, the TME, and drug efficacy prediction. We identified HCC specific EMT-related genes using weighted gene co-expression network analysis (WGCNA). An EMT-related genes prognostic index (EMT-RGPI) capable of effectively predicting HCC prognosis was then constructed. Consensus clustering of 12 HCC specific EMT-related hub genes uncovered two molecular clusters C1 and C2. Cluster C2 preferentially associated with unfavorable prognosis, higher stemness index (mRNAsi) value, elevated immune checkpoint expression, and immune cell infiltration. The TGF-β signaling, EMT, glycolysis, Wnt β-catenin signaling, and angiogenesis were markedly enriched in cluster C2. Moreover, cluster C2 exhibited higher TP53 and RB1 mutation rates. The TME subtypes and tumor immune dysfunction and exclusion (TIDE) score showed that cluster C1 patients responded well to immune checkpoint inhibitors (ICIs). Half-maximal inhibitory concentration (IC50) revealed that cluster C2 patients were more sensitive to chemotherapeutic and antiangiogenic agents. These findings may guide risk stratification and precision therapy for HCC patients. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
22107762
Volume :
274
Database :
Academic Search Index
Journal :
Cancer Genetics
Publication Type :
Academic Journal
Accession number :
163866556
Full Text :
https://doi.org/10.1016/j.cancergen.2023.03.006