WGCNA and LASSO algorithm constructed an immune infiltration-related 5-gene signature and nomogram to improve prognosis prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common immunogenic malignant tumor. Although the new strategies of immunotherapy and targeted therapy have made considerable progress in treatment HCC, 5-year survival rate patients still very low. The identification prognostic signatures exploration immune microenvironment are crucial to optimization improvement molecular strategies. We studied potential clinical benefits inflammation regulator miR-93-3p mined its target genes. Weighted gene co-expression network analysis (WGCNA), univariate multivariate COX regression LASSO algorithm employed identify prognostic-related genes construct multi-gene signature-based risk model nomogram for prediction. Support vector machine (SVM) based Cibersort’s deconvolution set enrichment (GSEA) used evaluate changes tumor pathway differences. study found favorable performance identified 389 on novel 5-gene signature (cct5, cdk4, cenpa, dtnbp1 flvcr1) was developed has prominent significance training cohort (P < 0.0001) validation = 0.0016). constructed by combining AJCC stage further improves prediction ability signature. infiltration level multiple cells (especially T cells, B macrophages) were positively correlated with expression In addition, we that markers monitored regulated Meanwhile, several GSEA pathways related system enriched high-risk group. general, integrated WGCNA, SVM algorithms develop verify nomograms improve patients.