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Updated: Apr 14, 2026
Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
Published on: September 8, 2023
Prognostic stratification in non-small cell lung cancer using a TIDE-informed transcriptomic signature: model
Jiaxuan Zhou1, Na Li1, Zimeng Li1
1Department of Radiology, the Key Laboratory of Advanced Interdisciplinary Studies Center, National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Background:
Non-small cell lung cancer (NSCLC) remains a major cause of cancer mortality. The Tumor Immune Dysfunction and Exclusion (TIDE) score is widely used to estimate immune-checkpoint blockade response, but its broader prognostic relevance in unselected NSCLC populations is unclear. This study aimed to determine whether TIDE-informed strata carry prognostic information beyond immunotherapy settings, and to develop and externally validate an immune gene expression-based prognostic signature derived from differentially expressed genes (DEGs) between these strata.
Methods:
Gene expression data and clinical information for NSCLC patients (n=1,153) were obtained from The Cancer Genome Atlas (TCGA). TIDE scores were calculated to stratify patients, and least absolute shrinkage and selection operator (LASSO) and Cox regressions were used to identify prognosis-related immune DEGs. A prognostic model was developed and validated using an external dataset (GSE50081, n=127). Immune cell infiltration was assessed using CIBERSORT, while drug sensitivity predictions were made based on the Genomics of Drug Sensitivity in Cancer (GDSC) database. Pathway enrichment analyses, including gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA), were conducted to explore key molecular mechanisms.
Results:
The prognostic model, based on 24 immune-related DEGs, effectively stratified NSCLC patients into high- and low-risk groups, with significant differences in survival outcomes (P<0.001). Key signaling pathways, including interleukin (IL)-17, p53, and tumor necrosis factor (TNF), were found to be associated with immune-related genes such as SLC7A5, PLAU, ANLN, MMP12, SCGB3A1, AHNAK2, and GJB3. Exploratory drug-response modeling with pRRophetic suggested lower estimated half-maximal inhibitory concentration (IC50) values for agents including MS-275 (entinostat), PF-4708671, and roscovitine in the high-risk group. External validation confirmed the model's reproducible prognostic performance.
Conclusions:
The TIDE algorithm carries prognostic information in NSCLC beyond immunotherapy settings. The proposed TIDE-informed gene signature reproduced prognostic stratification across cohorts, suggesting potential applicability to a broader NSCLC population and supporting future personalized risk stratification.
