Hub Genes in Non-Small Cell Lung Cancer Regulatory Networks

• Published in: Biomolecules (Basel, Switzerland) Vol. 12; no. 12; p. 1782
• Main Authors: Ye, Qing, Guo, Nancy Lan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.11.2022; MDPI
• Subjects: Algorithms; Annotations; Artificial intelligence; Biomarkers; Biomarkers, Tumor - genetics; Boolean; Cancer; Cancer therapies; Carcinogenesis; Carcinoma, Non-Small-Cell Lung - pathology; CD27 antigen; Chemotherapy; Clinical trials; Copy number; CRISPR; CRISPR-Cas9; CTLA-4 protein; Development and progression; DNA Copy Number Variations; Gene expression; Gene Regulatory Networks; Genetic aspects; Genomes; hub genes; Humans; Immunotherapy; Lung cancer; Lung cancer, Non-small cell; Lung Neoplasms - pathology; Medical prognosis; Metastasis; Methods; mRNA; multi-omics networks; non-small cell lung cancer; Non-small cell lung carcinoma; Patient outcomes; Patients; PD-1 protein; proliferation; Protein expression; Proteins; RNA-mediated interference; RNAi; Small cell lung carcinoma; Therapeutic applications; Therapeutic targets; Tumorigenesis; Tumors; United States; non-small cell lung cancer; RNAi; patient survival; proliferation; CRISPR-Cas9; biomarkers; multi-omics networks; therapeutic targets; hub genes
• ISSN: 2218-273X; 2218-273X
• DOI: 10.3390/biom12121782

There are currently no accurate biomarkers for optimal treatment selection in early-stage non-small cell lung cancer (NSCLC). Novel therapeutic targets are needed to improve NSCLC survival outcomes. This study systematically evaluated the association between genome-scale regulatory network centralities and NSCLC tumorigenesis, proliferation, and survival in early-stage NSCLC patients. Boolean implication networks were used to construct multimodal networks using patient DNA copy number variation, mRNA, and protein expression profiles. statistics of differential gene/protein expression in tumors versus non-cancerous adjacent tissues, dependency scores in in vitro CRISPR-Cas9/RNA interference (RNAi) screening of human NSCLC cell lines, and hazard ratios in univariate Cox modeling of the Cancer Genome Atlas (TCGA) NSCLC patients were correlated with graph theory centrality metrics. Hub genes in multi-omics networks involving gene/protein expression were associated with oncogenic, proliferative potentials and poor patient survival outcomes ( < 0.05, Pearson's correlation). Immunotherapy targets , and were ranked as top hub genes within the 10th percentile in most constructed multi-omics networks. , and were discovered as important hub genes in NSCLC proliferation with oncogenic potential. These results support the importance of hub genes in NSCLC tumorigenesis, proliferation, and prognosis, with implications in prioritizing therapeutic targets to improve patient survival outcomes.