Key genes and pathways in the molecular landscape of pancreatic ductal adenocarcinoma: A bioinformatics and machine learning study

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dc.authoridEYUBOGLU, Sinan/0009-0000-2673-3737
dc.authoridCoskuner Weber, Orkid/0000-0002-0772-9350
dc.contributor.authorEyuboglu, Sinan
dc.contributor.authorAlpsoy, Semih
dc.contributor.authorUversky, Vladimir N.
dc.contributor.authorCoskuner-Weber, Orkid
dc.date.accessioned2025-02-20T08:42:19Z
dc.date.available2025-02-20T08:42:19Z
dc.date.issued2024
dc.departmentTürk-Alman Üniversitesien_US
dc.description.abstractPancreatic ductal adenocarcinoma (PDAC) is recognized for its aggressive nature, dismal prognosis, and a notably low five-year survival rate, underscoring the critical need for early detection methods and more effective therapeutic approaches. This research rigorously investigates the molecular mechanisms underlying PDAC, with a focus on the identification of pivotal genes and pathways that may hold therapeutic relevance and prognostic value. Through the construction of a protein-protein interaction (PPI) network and the examination of differentially expressed genes (DEGs), the study uncovers key hub genes such as CDK1, KIF11, and BUB1, demonstrating their substantial role in the pathogenesis of PDAC. Notably, the dysregulation of these genes is consistent across a spectrum of cancers, positing them as potential targets for wide-ranging cancer therapeutics. This study also brings to the fore significant genes encoding intrinsically disordered proteins, in particular GPRC5A and KRT7, unveiling promising new pathways for therapeutic intervention. Advanced machine learning techniques were harnessed to classify PDAC patients with high accuracy, utilizing the key genetic markers as a dataset. The Support Vector Machine (SVM) model leveraged the hub genes to achieve a sensitivity of 91 % and a specificity of 85 %, while the RandomForest model notched a sensitivity of 91 % and specificity of 92.5 %. Crucially, when the identified genes were cross-referenced with TCGA-PAAD clinical datasets, a tangible correlation with patient survival rates was discovered, reinforcing the potential of these genes as prognostic biomarkers and their viability as targets for therapeutic intervention. This study's findings serve as a potent testament to the value of molecular analysis in enhancing the understanding of PDAC and in advancing the pursuit for more effective diagnostic and treatment strategies.
dc.identifier.doi10.1016/j.compbiolchem.2024.108268
dc.identifier.issn1476-9271
dc.identifier.issn1476-928X
dc.identifier.pmid39467488
dc.identifier.scopus2-s2.0-85207595928
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.compbiolchem.2024.108268
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1648
dc.identifier.volume113en_US
dc.identifier.wosWOS:001347538300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofComputational Biology and Chemistry
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250220
dc.subjectPancreatic canceren_US
dc.subjectBiomarkersen_US
dc.subjectHub genesen_US
dc.subjectSurvival analysisen_US
dc.subjectMachine learning modelsen_US
dc.subjectEarly diagnosisen_US
dc.titleKey genes and pathways in the molecular landscape of pancreatic ductal adenocarcinoma: A bioinformatics and machine learning study
dc.typeArticle

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