A Anoikis-Related Risk Model: Predicts Prognosis and Immunotherapy Response for Hepatocellular Carcinoma

Abstract

Abstract Hepatocellular Carcinoma (HCC) is the leading cause of cancer-related deaths globally. Most HCC patients are already in advanced stages of the disease when a confirmed diagnosis was made with prone to metastasis and a poor prognosis. Anoikis resistance plays a critical role in tumor invasion and metastasis. whereas the role of anoikis in HCC remains unclear. According to univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis, anoikis-related genes (ARGs) associated with the overall rate (OS) were selected. Then, 3 prognostic ARGs (PDK4, STK11 and TFDP1) were identified by multivariate Cox regression, and to establish a risk model. According to the risk score, HCC patients were divided into high- and low-risk group. The OS rate and immune infiltration between two groups were evaluated by Kaplan-Meier, CIBERSORT and ssGSEA analysis. The OS rate of HCC patients in low-risk group was longer than that in the high-risk group. The results of nomogram showed that the ARGs prognostic signature was an independent prognostic predictor. In addition, consensus clustering analysis could cluster the patients into two subgroups with different immune infiltration. Besides, functional enrichment and drug sensitivity were also conducted between high- and low-risk groups. This study was the first to integrate multiple ARGs to establish a risk-predictive model, and might provide a new perspective for individualized and accurate therapy strategies for HCC patients.