Tumor protein p53 (TP53) is one of the most frequently mutated genes in hepatocellular carcinoma (HCC), an event that has been associated with a poor prognosis. Therefore, availability of an accurate prognostic signature would be beneficial for improving therapeutic efficacy and patient prognosis. In the present study, HCC genetic mutation data, transcriptomic data and clinical data were downloaded from The Cancer Genome Atlas database to screen for specific TP53-associated signatures based on differentially expressed genes. Subsequently, the predictive value of any signatures found for the overall survival (OS) and the immune response were investigated, followed by validation in clinical specimens. The present study revealed 270 mutant genes, of which 28% were TP53 mutations. In addition, 81 upregulated genes and 27 downregulated genes were identified. Enrichment analysis revealed that mutant TP53 was particularly enriched for pathways associated with the cell cycle and cell metabolism, and whilst clustered, most enriched for terms associated with metabolic processes and the immune response. The alcohol dehydrogenase 4 (ADH4) gene was selected using univariate and multivariate Cox regression analysis. A nomogram was constructed to validate this prognostic signature. Patients in the low-ADH4 expression group displayed significantly worse OS time regardless of the TP53 mutation status compared with the high-ADH4 expression group. In addition, a higher degree of B-cell infiltration was observed in the low-ADH4 expression group, revealing differential immune microenvironments. Subsequently, ADH4 expression and the prognostic prediction values were validated further in clinical HCC samples by IHC assay, Risk score, OS analysis and ROC analysis. To conclude, these data suggest that the TP53-associated immune-metabolic signature is a specific and independent prognostic biomarker for patients with HCC that will help to facilitate novel immunotherapy development.
Keywords: ADH4; HCC; TP53; immunometabolism; prognosis.
Copyright: © Zhang et al.