Prognostic stratification in non-small cell lung cancer using a TIDE-informed transcriptomic signature: model...

Area of Science:

• Oncology
• Immunology
• Genomics

Background:

• Non-small cell lung cancer (NSCLC) is a leading cause of cancer mortality.
• The Tumor Immune Dysfunction and Exclusion (TIDE) score's prognostic value in NSCLC outside of immunotherapy is not well-established.
• This research investigates the broader prognostic relevance of TIDE in NSCLC.

Purpose of the Study:

• To determine if TIDE-stratified NSCLC patients have prognostic information beyond immunotherapy response prediction.
• To develop and validate an immune gene expression-based prognostic signature using differentially expressed genes (DEGs) between TIDE strata.
• To explore the potential for personalized risk stratification in NSCLC.

Main Methods:

• Utilized The Cancer Genome Atlas (TCGA) dataset (n=1,153) for NSCLC gene expression and clinical data.
• Calculated TIDE scores for patient stratification and employed LASSO and Cox regression to identify prognostic immune DEGs.
• Validated the developed prognostic model using an external dataset (GSE50081, n=127) and analyzed immune cell infiltration and drug sensitivity.

Main Results:

• A prognostic model based on 24 immune-related DEGs significantly stratified NSCLC patients into high- and low-risk groups (P<0.001).
• Identified key signaling pathways (IL-17, p53, TNF) associated with immune genes like SLC7A5, PLAU, and MMP12.
• External validation confirmed the model's reproducible prognostic performance, with potential implications for drug response.

Conclusions:

• The TIDE algorithm provides prognostic insights for NSCLC patients irrespective of immunotherapy treatment.
• The developed TIDE-informed gene signature demonstrates robust prognostic stratification across different cohorts.
• This signature holds promise for broader application in personalized risk assessment and management of NSCLC.