Identification and validation of immune-related gene signature models for predicting prognosis and immunotherapy response in hepatocellular carcinoma.

ABSTRACT:

Background

This study seeks to enhance the accuracy and efficiency of clinical diagnosis and therapeutic decision-making in hepatocellular carcinoma (HCC), as well as to optimize the assessment of immunotherapy response.

Methods

A training set comprising 305 HCC cases was obtained from The Cancer Genome Atlas (TCGA) database. Initially, a screening process was undertaken to identify prognostically significant immune-related genes (IRGs), followed by the application of logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods for gene modeling. Subsequently, the final model was constructed using support vector machines-recursive feature elimination (SVM-RFE). Following model evaluation, quantitative polymerase chain reaction (qPCR) was

SUBMITTER: Liu Z 

PROVIDER: S-EPMC11199539 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature