Bioinformatics role of the WGCNA analysis and co-expression network identifies of prognostic marker in lung cancer

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dc.citation.issue5
dc.citation.volume29
dc.contributor.authorChengcheng L
dc.contributor.authorRaza SHA
dc.contributor.authorShengchen Y
dc.contributor.authorMohammedsaleh ZM
dc.contributor.authorShater AF
dc.contributor.authorSaleh FM
dc.contributor.authorAlamoudi MO
dc.contributor.authorAloufi BH
dc.contributor.authorMohajja Alshammari A
dc.contributor.authorSchreurs NM
dc.contributor.authorZan L
dc.coverage.spatialSaudi Arabia
dc.date.accessioned2024-12-03T19:34:30Z
dc.date.available2024-12-03T19:34:30Z
dc.date.issued2022-05-01
dc.description.abstractLung cancer is the most talked about cancer in the world. It is also one of the cancers that currently has a high mortality rate. The aim of our research is to find more effective therapeutic targets and prognostic markers for human lung cancer. First, we download gene expression data from the GEO database. We performed weighted co-expression network analysis on the selected genes, we then constructed scale-free networks and topological overlap matrices, and performed correlation modular analysis with the cancer group. We screened the 200 genes with the highest correlation in the cyan module for functional enrichment analysis and protein interaction network construction, found that most of them focused on cell division, tumor necrosis factor-mediated signaling pathways, cellular redox homeostasis, reactive oxygen species biosynthesis, and other processes, and were related to the cell cycle, apoptosis, HIF-1 signaling pathway, p53 signaling pathway, NF-κB signaling pathway, and several cancer disease pathways are involved. Finally, we used the GEPIA website data to perform survival analysis on some of the genes with GS > 0.6 in the cyan module. CBX3, AHCY, MRPL12, TPGB, TUBG1, KIF11, LRRC59, MRPL17, TMEM106B, ZWINT, TRIP13, and HMMR was identified as an important prognostic factor for lung cancer patients. In summary, we identified 12 mRNAs associated with lung cancer prognosis. Our study contributes to a deeper understanding of the molecular mechanisms of lung cancer and provides new insights into drug use and prognosis.
dc.description.confidentialfalse
dc.edition.editionMay 2022
dc.format.pagination3519-3527
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/35844396
dc.identifier.citationChengcheng L, Raza SHA, Shengchen Y, Mohammedsaleh ZM, Shater AF, Saleh FM, Alamoudi MO, Aloufi BH, Mohajja Alshammari A, Schreurs NM, Zan L. (2022). Bioinformatics role of the WGCNA analysis and co-expression network identifies of prognostic marker in lung cancer.. Saudi J Biol Sci. 29. 5. (pp. 3519-3527).
dc.identifier.doi10.1016/j.sjbs.2022.02.016
dc.identifier.eissn2213-7106
dc.identifier.elements-typejournal-article
dc.identifier.issn1319-562X
dc.identifier.piiS1319-562X(22)00092-4
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/72165
dc.languageeng
dc.publisherElsevier B.V. on behalf of King Saud University
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S1319562X22000924
dc.relation.isPartOfSaudi J Biol Sci
dc.rights(c) The author/sen
dc.rights.licenseCC BYen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectBioinformatics
dc.subjectGene Expression Omnibus
dc.subjectGene Expression Profiling Interactive Analysis (GEPIA)
dc.subjectLung Cancer
dc.subjectWeighted Correlation Network Analysis (WGCNA)
dc.titleBioinformatics role of the WGCNA analysis and co-expression network identifies of prognostic marker in lung cancer
dc.typeJournal article
pubs.elements-id452315
pubs.organisational-groupOther
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