Construction and Validation of a Novel Butyrylation-Related Gene Signature Related to Prognosis, Clinical Implications, and Immune Microenvironment Characterization of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a common and highly lethal malignant tumor that poses a serious threat to human health. The post-transcriptional modification of proteins known as butyrylation has emerged as a critical factor in tumorigenesis, playing a pivotal role in the initiation and progression of cancer. This study aimed to develop a prognostic risk model for HCC using butyrylation-related genes (BRGs). Differentially expressed BRGs were identified from the LIHC-TCGA data sets, and a prognostic risk model was constructed using LASSO and multivariate regression analysis. The model's robustness was further confirmed in the GSE14520 cohort. The clinicopathological characteristics, immune features, enrichment pathways, and antitumor drug sensitivity of the BRG signature were also assessed. Additionally, a nomogram was created to improve the predictive accuracy of the model. A set of 16 BRGs, including MMP1, ACOT7, AGPAT5, FLAD1, PDSS1, HSPD1, FKBP1A, AKR1B10, HDAC1, HDAC2, MAPT, ACADS, ACAT1, ACSL6, PDE2A, and PON1, were identified. Kaplan-Meier survival analysis showed that patients in the high-risk group had worse overall survival (OS) and progression-free survival (PFS) compared with those in the low-risk group. Univariate and multivariate Cox regressions, along with LASSO analysis, consistently indicated that the BRG signature is an independent prognostic factor for HCC. Clinical line plots accurately predicted 1, 3, and 5 year survival with AUC values of 0.805, 0.729, and 0.710, respectively. Additionally, the distribution of immune cells varied between different risk groups, and the low-risk group showed more potential for immunotherapy and chemotherapy. This study provides a novel biological basis for prognostic prediction in HCC and offers insights into personalized treatment strategies, including candidate drug selection, for clinicians to guide therapeutic decisions.