Identification of Anoikis-Related Prognostic Genes and Construction of a Prognostic Model for Hepatocellular Carcinoma Based on Single-Cell and Bulk Transcriptomic Analysis

Abstract

Background Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer-related deaths worldwide, posing a serious threat to human health and life. Extensive research into cell death mechanisms has revealed a link between anoikis and the development and progression of HCC. However, the specific mechanisms remain unclear. Methods We used AddModuleScore, single-sample gene set enrichment analysis (ssGSEA), and weighted gene co-expression network analysis (WGCNA) to identify anoikis-related genes at both single-cell and bulk transcriptome levels. An eight-gene prognostic model for HCC was constructed and validated in training and validation sets. We also examined differences between high-risk and low-risk groups in clinical pathological characteristics, single-cell features, gene mutation landscapes, immune microenvironments, responses to immunotherapy, and chemotherapy drug sensitivity. Results We successfully constructed a prognostic model for HCC containing eight AnoRGs (SLC2A2, ANXA2, ATP1B3, YWHAH, YWHAB, MAPRE1, ARPC2, and SMS), which demonstrated excellent performance in various aspects of prognostic prediction. Comparing the concordance index (C-index) of our model with those of previous studies, our model achieved the highest C-index, indicating superior predictive performance. Additionally, M0 macrophages may be associated with poor prognosis in HCC. Differences in biological functions, mutation profiles, and immune cell infiltration in the tumor microenvironment were observed between the high-risk and low-risk groups. Conclusion Our study constructed an anoikis-related signal based on single-cell and bulk RNA data, providing a promising tool for predicting prognosis, targeted prevention, and personalized drug treatment in HCC.