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.