A Novel Prognostic Model of Hepatocellular Carcinoma per Two NAD+ Metabolic Synthesis-Associated Genes

Hepatocellular carcinoma (HCC) is a formidable challenge to global human health, while recent years have witnessed the important role of NAD+ in tumorigenesis and progression. However, the expression pattern and prognostic value of NAD+ in HCC still remain elusive. Gene expression files and corresponding clinical pathological files associated with HCC were obtained from the Cancer Genome Atlas (TCGA) database, and genes associated with NAD+ were retrieved from the GSEA and differentially analyzed in tumor and normal tissues. A consensus clustering analysis was conducted by breaking down TCGA patients into four distinct groups, while Kaplan-Meier curves were generated to investigate the disparity in clinical pathology and endurance between clusters. A prognostic model based on NAD+-associated genes was established and assessed by combining LASSO-Cox regression, uni- and multi-variate Cox regression, and ROC curve analyses. Investigations were conducted to determine the expression of distinct mRNAs and proteins in both HCC and non-tumor tissues. A novel two-gene signature including poly (ADP-Ribose) polymerase 2 (PARP2) and sirtuin 6 (SIRT6) was obtained through LASSO-Cox regression and was identified to have favorable prognostic performance in HCC patients from TCGA. Analyses of both single and multiple variables showed that the prognostic model was a distinct prognostic factor in the endurance of liver cancer patients in both the training and trial groups. The nomogram also exhibited clinical significance in the prognosis of HCC patients. Immunohistochemistry, qRT-PCR, and Western blotting revealed that HCC samples exhibited higher PARP2 and SIRT6 expression levels than those of normal controls. This study identified a robust prognostic model comprising two NAD+-associated genes using bioinformatic methods, which is accurate in predicting the survival outcome of HCC patients. This model might benefit the early diagnosis of HCC and further facilitate the management of individualized medical service and clinical decision-making.