Construction and validation of immune prognosis model for lung adenocarcinoma based on machine learning.

ABSTRACT:

Introduction

Lung adenocarcinoma is a leading subtype of lung cancer with high rates of recurrence and metastasis. Identifying novel prognostic biomarkers is essential for improving patient outcomes.

Methods

Transcriptomic and clinicopathological data from TCGA (55 tumor samples and 38 normal samples) were used to construct a prognostic model, with 30 samples for internal validation. An external validation cohort (10 tumor-normal pairs) was obtained from the First Affiliated Hospital of Wenzhou Medical University. Differentially expressed genes and immune-related genes from the IMMPORT database were integrated using WGCNA. Three machine learning algorithms-Random Forest, LASSO, and SVM-RFE-were applied to identify key hub genes. A multivariate Cox regression model was built

SUBMITTER: Zheng J 

PROVIDER: S-EPMC12321856 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature