ArticleJournal of thoracic disease2024
Predicting prognosis in lung adenocarcinoma by predicting TIGIT expression: a pathomics model.
Article in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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1 citing paper in PubMed.
- Erratum: Predicting prognosis in lung adenocarcinoma by predicting TIGIT expression: a pathomics model.Journal of thoracic disease · 2025Article
Corrections and comments
- Erratum issued
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5 authors.
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No grant is acknowledged in the PubMed record.
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 using min-redundancy, recursive feature elimination (RFE) and stepwise regression algorithms, and a logistic regression algorithm was used to establish the pathomics model. Receiver operating characteristics, calibration, and decision curves were used for model evaluation. The pathomics score (PS) was used to predict TIGIT gene expression and analyze prognostic value and pathological mechanisms through Spearman correlation. Results: The study included 443 clinical samples and 327 pathological images. Prognostic analysis showed significantly higher TIGIT expression in tumor tissues (P<0.001), with TIGIT being a protective factor for overall survival (OS) in LUAD [hazard ratio (HR) =0.65; 95% confidence interval (CI): 0.44-0.95; P=0.03]. GSEA revealed significant enrichment of differentially expressed genes in the TGF-β and MAPK signaling pathways. From 465 pathological features, the four best features were selected to construct a pathomics model with good predictive performance. Higher PS values were observed in the TIGIT high-expression group, correlating with improved OS (P=0.009). PS was positively correlated with the epithelial-mesenchymal transition related (EMT-related) genes ( Conclusions: TIGIT expression is significantly correlated with LUAD prognosis and can effectively predict patient outcomes.
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