Predicting prognosis in lung adenocarcinoma by predicting TIGIT expression: a pathomics model.

ABSTRACT:

Background

Traditional diagnostic methods have limited efficacy in predicting the prognosis of lung adenocarcinoma (LUAD), T cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT) is a new biomarker. This study aimed to evaluate TIGIT expression as a LUAD biomarker and predict patient prognosis using a pathological feature model.

Methods

Clinical data and pathological images from The Cancer Genome Atlas (TCGA) were analyzed. The prognostic value of TIGIT was verified by genetic prognostic analysis and gene set enrichment analysis (GSEA). The OTSU algorithm was used to segment LUAD pathological images, and features were extracted using the PyRadiomics package and standardized with z-scores. Feature selection was performed usi

SUBMITTER: Hu P 

PROVIDER: S-EPMC11635229 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature