Establishment of a Prognostic Model for Hepatocellular Carcinoma Based on Bioinformatics and the Role of NR6A1 in the Progression of HCC.

BACKGROUND AND AIMS

Generally acceptable prognostic models for hepatocellular carcinoma (HCC) are not available. This study aimed to establish a prognostic model for HCC by identifying immune-related differentially expressed genes (IR-DEGs) and to investigate the potential role of in the progression of HCC.

METHODS

Bioinformatics analysis using The Cancer Genome Atlas and ImmPort databases was used to identify IR-DEGs. Lasso Cox regression and multivariate Cox regression analysis were used to establish a prognostic model of HCC. Kaplan-Meier analysis and the receiver operating characteristic (ROC) curves were used to evaluate the performance of the prognostic model, which was further verified in the International Cancer Genome Consortium (ICGC) database. Gene set enrichment analysis was used to explore the potential pathways of . Cell counting kit 8, colony formation, wound healing, and Transwell migration assays using Huh7 cells, and tumor formation models in nude mice were conducted.

RESULTS

A prognostic model established based on ten identified IR-DEGs including and , effectively predicted the prognosis of HCC patients, was confirmed by the ROC curves and verified in ICGC database. expression was significantly up-regulated in HCC patients, and was significantly associated with a low survival rate. Gene set enrichment analysis showed the enrichment of cell cycle, mTOR, WNT, and ERBB signaling pathways in patients with high expression. promoted cell proliferation, invasiveness, migration, and malignant tumor formation and growth and .

CONCLUSIONS

An effective prognostic model for HCC, based on a novel signature of 10 immune-related genes, was established. was up-regulated in HCC and was associated with a poor prognosis of HCC. promoted cell proliferation, migration, and growth of HCC, most likely through the cell cycle, mTOR, WNT, and ERBB signaling pathways.