Integrative transcriptome analysis identifies a crotonylation gene signature for predicting prognosis and drug sensitivity in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) stands as the most prevalent and treatment-resistant malignant tumour, characterized by a dismal prognosis. Croton acylation (CA) has recently gained attention as a critical factor in cancer pathogenesis. This study sought to rapidly identify prognostic features of HCC linked to CA. Differential analysis was conducted between tumour tissues and adjacent non-tumour tissues in the TCGA-LIHC and GSE76427 datasets to uncover differentially expressed genes (DEG1 and DEG2). The intersection of DEG1 and DEG2 highlighted DEGs with consistent expression patterns. Single-sample gene set enrichment analysis scores were calculated for 18 lysine crotonylation-related genes (LCRGs) identified in prior research, showing significant differences between tumour and normal groups. Subsequently, weighted gene co-expression network analysis was employed to identify key module genes correlated with the LCRG score. Candidate genes were identified by overlapping consistently expressed DEGs with key module genes. Prognostic features were identified, and risk scores were determined via regression analysis. Patients were categorized into risk groups based on the optimal cutoff value. Gene set enrichment analysis (GSEA) and immunoassays were also performed. The prognostic features were further validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A total of 88 candidate genes were identified from 1179 consistently expressed DEGs and 4200 key module genes. Seven prognostic features were subsequently identified: TMCO3, RAP2A, ITGAV, ZFYVE26, CHST9, HMGN4, and KLHL21. GSEA revealed that DEGs between risk groups were primarily associated with chylomicron metabolism, among other pathways. Additionally, activated CD4+ T cells demonstrated the strongest positive correlation with risk scores, and most immune checkpoints showed significant differences between risk groups, with ASXL1 exhibiting the strongest correlation with risk scores. The Tumour Immune Dysfunction and Exclusion score was notably higher in the high-risk group. Moreover, in both the TCGA-LIHC and ICGC-LIRI-JP datasets, the expression of other prognostic features was elevated in tumour tissues, with the exception of CHST9. RT-qPCR confirmed the increased expression of TMCO3, RAP2A, ITGAV, ZFYVE26, and HMGN4. This study establishes a risk model for HCC based on seven crotonylation-associated prognostic features, offering a theoretical framework for the diagnosis and treatment of HCC.