BackgroundLung cancer is a leading cause of cancer-related death worldwide and is the most commonly diagnosed cancer. Like other cancers, it is a complex and highly heterogeneous disease involving multiple signaling pathways. Identifying potential therapeutic targets is critical for the development of effective treatment strategies.MethodsWe used a systems biology approach to identify potential key regulatory factors in smoking-induced lung cancer. We first identified genes that were differentially expressed between smokers with normal lungs and those with cancerous lungs, then integrated these differentially expressed genes (DEGs) with data from a protein-protein interaction database to build a network model with functional modules for pathway analysis. We also carried out a gene set enrichment analysis of DEG lists using the Kinase Enrichment Analysis (KEA), Protein-Protein Interaction (PPI) hubs, and KEGG (Kyoto Encyclopedia of Genes and Genomes) databases.ResultsTwelve transcription factors were identified as having potential significance in lung cancer (CREB1, NUCKS1, HOXB4, MYCN, MYC, PHF8, TRIM28, WT1, CUX1, CRX, GABP, and TCF3); three of these (CRX, GABP, and TCF) have not been previously implicated in lung carcinogenesis. In addition, 11 kinases were found to be potentially related to lung cancer (MAPK1, IGF1R, RPS6KA1, ATR, MAPK14, MAPK3, MAPK4, MAPK8, PRKCZ, and INSR, and PRKAA1). However, PRKAA1 is reported here for the first time. MEPCE, CDK1, PRKCA, COPS5, GSK3B, BRCA1, EP300, and PIN1 were identified as potential hubs in lung cancer-associated signaling. In addition, we found 18 pathways that were potentially related to lung carcinogenesis, of which 12 (mitogen-activated protein kinase, gonadotropin-releasing hormone, Toll-like receptor, ErbB, and insulin signaling; purine and ether lipid metabolism; adherens junctions; regulation of autophagy; snare interactions in vesicular transport; and cell cycle) have been previously identified.ConclusionOur systems-based approach identified potential key molecules in lung carcinogenesis and provides a basis for investigations of tumor development as well as novel drug targets for lung cancer treatment.

No abstract

Due to the complexity and heterogeneity of cancer, it is inevitable to investigate carcinogenesis from the systems perspective 1,2 . This was the driver of our study 3 . In the next sections, we will highlight some aspects regarding to the methodology of the study and the most important findings.

The molecular mechanisms underlying the pathogenesis of COVID-19 has not been fully discovered. This study aims to decipher potentially hidden parts of the pathogenesis of COVID-19, potential novel drug targets, and to identify potential drug candidates. Two gene expression profiles (GSE147507-GSE153970) were analyzed and overlapping differentially expressed genes (DEGs) were selected for which top enriched transcription factors and kinases were identified and pathway analysis was performed. Protein-protein interaction (PPI) of DEGs was constructed, hub genes were identified and module analysis was also performed. DGIdb database was used to identify drugs for the potential targets (hub genes and the most enriched transcription factors and kinases for DEGs). A drug-potential target network was constructed and drugs are ranked according to the degree. L1000FDW web-based utility was used to identify drugs that can reverse transcriptional profiles of COVID-19. We identified drugs currently in clinical trials and novel potential 8 drugs. Besides the well-known pathogenic pathways, It was found that axon guidance is a potential pathogenic pathway. Sema7A, which may exacerbate hypercytokinemia, is considered a potential novel drug target. Another potential novel pathway is related to TINF2 overexpression which may induce potential telomere dysfunction and hence DNA damage that may exacerbate lung fibrosis.