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

Peihong Hu; Bo Tian; Hang Gu; Haoran Liu; Qiang Li

doi:10.21037/jtd-24-978

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

J Thorac Dis. 2024 Nov 30;16(11):7617-7629. doi: 10.21037/jtd-24-978. Epub 2024 Nov 29.

Authors

Peihong Hu^#^{1

2}, Bo Tian^#¹, Hang Gu^{1

3}, Haoran Liu^{1

4}, Qiang Li¹

Affiliations

¹ Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
² School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
³ Graduate School, Chengdu Medical College, Chengdu, China.
⁴ Graduate School, North Sichuan Medical College, Nanchong, China.

^# Contributed equally.

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 (WIPF1, GLIPR1, IL15) and immune checkpoints (ICOS, CTLA4, LAG3) (P<0.001). Increased abundance of G2/M checkpoint-related genes (MARCKS, CASP8AP2) and infiltration of CD8⁺ T cells and M2 macrophages were noted in the high PS group (P<0.05).

Conclusions: TIGIT expression is significantly correlated with LUAD prognosis and can effectively predict patient outcomes.

Keywords: T cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT); lung adenocarcinoma (LUAD); mechanisms of pathology; pathomics; prognostic model.