Identification of Hepatocellular Carcinoma Subtypes Based on Global Gene Expression Profiling to Predict the Prognosis and Potential Therapeutic Drugs.

ABSTRACT: Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous tumor, and distinguishing its subtypes holds significant value for diagnosis, treatment, and the prognosis. Methods: Unsupervised clustering analysis was conducted to classify HCC subtypes. Subtype signature genes were identified using LASSO, SVM, and logistic regression. Survival-related genes were identified using Cox regression, and their expression and function were validated via qPCR and gene interference. GO, KEGG, GSVA, and GSEA were used to determine enriched signaling pathways. ESTIMATE and CIBERSORT were used to calculate the stromal score, tumor purity, and immune cell infiltration. TIDE was employed to predict the patient response to immunotherapy. Finally, drug sensitivity was analyzed using the

SUBMITTER: Zhang C 

PROVIDER: S-EPMC11761595 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature