ArticleInternational journal of physiology, pathophysiology and pharmacology2021
Application of weighted gene co-expression network analysis to identify the hub genes in H1N1.
Article in International journal of physiology, pathophysiology and pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Novel Core Gene Signature Associated with Inflammation-to-Metaplasia Transition in Influenza A Virus-Infected Lungs.International journal of molecular sciences · 2024Article
- Comprehensive immune landscape of lung-resident memory CD8Frontiers in microbiology · 2023Article
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Authors and funding
8 authors.
Funding
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Abstract
objectiveIdentifying the disease-associated interactions between different genes helps us to find novel therapeutic targets and predictive biomarkers.
methodsGene expression data GSE82050 from H1N1 and control human samples were acquired from the NCBI GEO database. Highly co-expressed genes were grouped into modules. Through Person's correlation coefficient calculation between the module and clinical phenotype, notable modules were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted, and the hub genes within the module of interest were identified. Also, gene expression data GSE27131 were acquired from the GEO database to verify differential key gene expression analysis. The CIBERSORT was used to evaluate the immune cells infiltration and the GSVA was performed to identify the differentially regulated pathways in H1N1. The receiver operating characteristic (ROC) curves were used to assess the diagnostic values of the hub genes.
resultThe black module was shown to have the highest correlation with the clinical phenotype, mainly functioning in the signaling pathways such as the mitochondrial inner membrane, DNA conformation change, DNA repair, and cell cycle phase transition. Through analysis of the black module, we found 5 genes that were highly correlated with the H1N1 phenotype. The H1N1 project from GSE27131 confirmed an increased expression of these genes.
conclusionBy using the WGCNA we analyzed and predicted the key genes in H1N1. BRCA1, CDC20, MAD2L1, MCM2, and UBE2C were found to be the most relevant genes, which may be therapeutic targets and predictive biomarkers for H1N1 therapy.
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