Bioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer

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dc.authoridKori, Medi/0000-0002-4589-930X
dc.authoridGOV, ESRA/0000-0002-5256-4778
dc.contributor.authorKori, Medi
dc.contributor.authorGov, Esra
dc.date.accessioned2025-01-06T17:38:06Z
dc.date.available2025-01-06T17:38:06Z
dc.date.issued2022
dc.description.abstractGastric cancer (GC) is one of the five most common cancers in the world and unfortunately has a high mortality rate. To date, the pathogenesis and disease genes of GC are unclear, so the need for new diagnostic and prognostic strategies for GC is undeniable. Despite particular findings in this regard, a holistic approach encompassing molecular data from different biological levels for GC has been lacking. To translate Big Data into system-level biomarkers, in this study, we integrated three different GC gene expression data with three different biological networks for the first time and captured biologically significant (i.e., reporter) transcripts, hub proteins, transcription factors, and receptor molecules of GC. We analyzed the revealed biomolecules with independent RNA-seq data for their diagnostic and prognostic capabilities. While this holistic approach uncovered biomolecules already associated with GC, it also revealed novel system biomarker candidates for GC. Classification performances of novel candidate biomarkers with machine learning approaches were investigated. With this study, AES, CEBPZ, GRK6, HPGDS, SKIL, and SP3 were identified for the first time as diagnostic and/or prognostic biomarker candidates for GC. Consequently, we have provided valuable data for further experimental and clinical efforts that may be useful for the diagnosis and/or prognosis of GC.
dc.identifier.doi10.3390/genes13122233
dc.identifier.issn2073-4425
dc.identifier.issue12
dc.identifier.pmid36553500
dc.identifier.scopus2-s2.0-85144524823
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/genes13122233
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2478
dc.identifier.volume13
dc.identifier.wosWOS:000901216800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofGenes
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectgastric cancer
dc.subjectdisease genes
dc.subjectdiagnostic genes
dc.subjectprognostic genes
dc.subjectmulti-omics
dc.subjectsystems biology
dc.titleBioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer
dc.typeArticle

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