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

Abstract

The biological functional network of tumor tissues is relatively stable for a period of time and under different conditions, so the impact of tumor heterogeneity is effectively avoided. Based on edge perturbation, functional gene interaction networks were used to reveal the pathological environment of patients with non-small cell carcinoma at the individual level, and to identify cancer subtypes with the same or similar status, and then a multi-dimensional and multi-omics comprehensive analysis was put into practice. Two edge perturbation subtypes were identified through the construction of the background interaction network and the edge-perturbation matrix (EPM). Further analyses revealed clear differences between those two clusters in terms of prognostic survival, stemness indices, immune cell infiltration, immune checkpoint molecular expression, copy number alterations, mutation load, homologous recombination defects (HRD), neoantigen load, and chromosomal instability. Additionally, a risk prediction model based on TCGA for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) was successfully constructed and validated using the independent data set (GSE50081).