A novel classification method for NSCLC based on the background interaction network and the edge-perturbation matrix.

Area of Science:

• Oncology
• Bioinformatics
• Systems Biology

Background:

• Tumor biological functional networks offer a stable window into pathological environments, mitigating tumor heterogeneity concerns.
• Understanding individual patient pathological environments is crucial for precise cancer subtyping and treatment.

Purpose of the Study:

• To leverage edge perturbation in functional gene interaction networks to define non-small cell carcinoma subtypes.
• To conduct a comprehensive, multi-dimensional, multi-omics analysis of identified subtypes.
• To develop and validate a risk prediction model for lung adenocarcinoma and lung squamous cell carcinoma.

Main Methods:

• Construction of background interaction networks and an edge-perturbation matrix (EPM).
• Identification of distinct edge perturbation subtypes.
• Comparative analysis of subtypes across prognostic survival, stemness, immune infiltration, immune checkpoints, genomic alterations, and mutational landscape.
• Development and validation of a risk prediction model using TCGA and GSE50081 datasets.

Main Results:

• Two distinct edge perturbation subtypes were identified in non-small cell carcinoma patients.
• Significant differences were observed between subtypes in survival, stemness, immune profiles, copy number alterations, mutation load, HRD, neoantigen load, and chromosomal instability.
• A robust risk prediction model for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) was successfully developed and validated.

Conclusions:

• Edge perturbation analysis effectively distinguishes non-small cell carcinoma subtypes with distinct biological and clinical characteristics.
• These findings facilitate a deeper understanding of tumor pathology at an individual level, paving the way for personalized oncology.
• The validated risk model offers a valuable tool for predicting patient outcomes in LUAD and LUSC.