Research Paper Volume 14, Issue 7 pp 3155—3174
A novel classification method for NSCLC based on the background interaction network and the edge-perturbation matrix
- 1 Somatic Radiotherapy Department, Shandong Second Provincial General Hospital, Shandong Provincial ENT Hospital, Jinan, Shandong 250023, PR China
- 2 Department of Respiratory and Critical Care Medicine, Shandong Second Provincial General Hospital, Shandong Provincial ENT Hospital, Shandong University, Jinan, Shandong 250023, PR China
- 3 Department of Blood Transfusion, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
- 4 Department of Oncology, Jinan Central Hospital, The Hospital Affiliated with Shandong First Medical University, Jinan, Shandong 250013, PR China
- 5 Respiratory Department, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250014, PR China
- 6 Nursing Department, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong 250014, PR China
- 7 Phase I Clinical Trial Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250012, PR China
Received: January 19, 2022 Accepted: March 28, 2022 Published: April 9, 2022
https://doi.org/10.18632/aging.204004How to Cite
Copyright: © 2022 Tian et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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).
Abbreviations
UCSC: University of California Santa Cruz; EPM: Edge-Perturbation Matrix; NA: No Available; TCGA: The Cancer Genome Atlas; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; TIDE: Tumour Immune Dysfunction and Exclusion; CNV: Copy Number Variation; LUAD: Lung Adenocarcinoma; LUSC: Lung squamous cell carcinoma; LASSO: Least Absolute Shrinkage and Selection Operator; TML: Tumour Mutation Load; FCGR: Fcgamma Receptor; FDR: False Discovery Rate; ROC curve: Receiver Operating Characteristic curve; AUC: Area Under Curve; GTEx: Genotype-Tissue Expression Program; ICI: Immune Checkpoint Inhibitor; HRD: Homologous Recombination Deficiency; IGP: Intra-Group Proportion; FCERI: Fc epsilon receptor; FCGR: Fcgamma receptor.