Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient’s quality life decreases dramatically and drug therapy ineffective. Liver transplantation most effective treatment, but lack donor required for transplantation, high cost procedure post-transplant rejection make this method unsuitable patients. Methods: aim study was find potential diagnostic biomarkers associated with HBV-LC bioinformatics analysis classify into specific subtypes consensus clustering. This will provide new perspective early diagnosis, treatment prevention HCC in Two study-relevant datasets, GSE114783 GSE84044, were retrieved GEO database. We screened feature genes using differential analysis, weighted gene co-expression network (WGCNA), three machine learning algorithms including least absolute shrinkage selection operator (LASSO), support vector recursive elimination (SVM-RFE), random forest (RF) total five methods. After that, we constructed an artificial neural (ANN) model. A cohort consisting GSE123932, GSE121248 GSE119322 used external validation. To better predict risk development, also built nomogram And multiple enrichment analyses samples performed understand biological processes which they significantly enriched. different analyzed Immune infiltration approach. Results: Using data downloaded GEO, developed ANN model based on six genes. clustering classified them two subtypes, C1 C2, it hypothesized patients subtype C2 might have milder symptoms immune analysis. Conclusion: column line graphs showed excellent predictive power, providing diagnosis possible HBV-LC. delineation facilitate future