Identification and validation of the fibrosis-related molecular subtypes of hepatocellular carcinoma by bioinformatics.

We identified novel Molecular subtypes according to the expression of fibrosis-related genes (FRGs) and constructed a prognostic model using different expression genes (DEGs) for patients with Hepatocellular carcinoma (HCC). We downloaded the clinical data and transcriptome data of HCC from The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, and International Cancer Genome Consortium (ICGC) database. We identified two fibrosis-related molecular subtypes of HCC by consensus unsupervised clustering analysis. Interestingly, these two molecular subtypes significantly differed in overall survival (OS) and clinical characteristics. Besides, the most minor absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analysis were performed to develop a novel prognostic model by three genes (including KPNA2, LPCAT1, and AKR1D1). There was a statistically significant difference in OS between the high-risk and low-risk groups. The area under the ROC curve (AUC) of OS in 1-, 3-, and 5-year were satisfactory. Besides, the risk score was connected with critical clinical characteristics and could be an independent factor in predicting prognosis. Then, the nomogram was built by incorporating risk scores with clinical parameters. Additionally, the risk score was remarkedly correlated with TME and drug susceptibility. Finally, the results of H&E staining and immunohistochemistry of Ki67 showed that the tumor of higher-risk patients are more malignant. The FRGs-based subtype and signature explain the HCC heterogeneity, which might provide a new method to develop a more efficient treatment.