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http://hdl.handle.net/11452/34478
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DC Field | Value | Language |
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dc.date.accessioned | 2023-10-20T06:31:04Z | - |
dc.date.available | 2023-10-20T06:31:04Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Pirim, D. (2020). "Integrative analyses of molecular pathways and key candidate biomarkers associated with colorectal cancer". Cancer Biomarkers, 27(4), 555-568. | en_US |
dc.identifier.issn | 1574-0153 | - |
dc.identifier.issn | 1875-8592 | - |
dc.identifier.uri | https://doi.org/10.3233/CBM-191263 | - |
dc.identifier.uri | https://pubmed.ncbi.nlm.nih.gov/32176635/ | - |
dc.identifier.uri | http://hdl.handle.net/11452/34478 | - |
dc.description.abstract | BACKGROUND: Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths and mining the molecular factors underlying CRC pathogenesis is imperative for alleviating the disease burden. OBJECTIVE: To highlight key molecular pathways, prioritize hub genes and their regulators related to CRC. METHODS: Data sets of TCGA-COAD and GTEx were used to identify differentially expressed genes (DEGs) and their functional enrichments in pathways and biological processes were analyzed using bioinformatics tools. Protein-protein interaction network was constructed and hub genes were identified using Cytoscape. Ingenuity Pathway Analysis was used to analyze the relations of the hub genes with diseases and canonical pathways. Key regulators targeting the hub genes such as TFs, miRNAs and their interactions were identified using in silico tools. RESULTS: AURKA, CDK1, MYC, CDH1, CCNB1, CDC20, UBE2C, PLK1, KIF11, and CCNA2 were prioritized as hub genes based on their topological properties. Enrichment analyses emphasized the roles of DEGs and hub genes in the cell cycle process. Interactions of the hub genes with TFs and miRNAs suggested TP53, EZH2 and KLF4 as being promising candidate biomarkers for CRC. CONCLUSIONS: Our results provide in silico evidence for candidate biomolecules that may have strong biomarker potential for CRC-related translational strategies. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IOS Press | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Oncology | en_US |
dc.subject | Colorectal cancer | en_US |
dc.subject | Microrna | en_US |
dc.subject | Transcription factors | en_US |
dc.subject | In silico tools | en_US |
dc.subject | Bioinformatics | en_US |
dc.subject | Ingenuity pathway analysis | en_US |
dc.subject | Cell-proliferation | en_US |
dc.subject | Gene-expression | en_US |
dc.subject | Tumor-growth | en_US |
dc.subject | Mechanism | en_US |
dc.subject | Database | en_US |
dc.subject.mesh | Biomarkers, tumor | tr_TR |
dc.subject.mesh | Cell cycle | en_US |
dc.subject.mesh | Colorectal neoplasms | en_US |
dc.subject.mesh | Computational biology | en_US |
dc.subject.mesh | Databases, genetic | en_US |
dc.subject.mesh | Gene expression profiling | en_US |
dc.subject.mesh | Humans | en_US |
dc.subject.mesh | MicroRNAs | en_US |
dc.subject.mesh | Protein Interaction Maps | en_US |
dc.subject.mesh | Signal transduction | en_US |
dc.title | Integrative analyses of molecular pathways and key candidate biomarkers associated with colorectal cancer | en_US |
dc.type | Article | en_US |
dc.identifier.wos | 000526817400013 | tr_TR |
dc.identifier.scopus | 2-s2.0-85083304469 | tr_TR |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi | tr_TR |
dc.contributor.department | Uludağ Üniversitesi/Fen-Edebiyat Fakültesi/Moleküler Biyoloji ve Genetik Bölümü. | tr_TR |
dc.contributor.orcid | 0000-0002-0522-9432 | tr_TR |
dc.identifier.startpage | 555 | tr_TR |
dc.identifier.endpage | 568 | tr_TR |
dc.identifier.volume | 27 | tr_TR |
dc.identifier.issue | 4 | tr_TR |
dc.relation.journal | Cancer Biomarkers | en_US |
dc.contributor.buuauthor | Pirim, Dilek | - |
dc.contributor.researcherid | ABA-4957-2020 | tr_TR |
dc.identifier.pubmed | 32176635 | tr_TR |
dc.subject.wos | Oncology | tr_TR |
dc.indexed.wos | SCIE | en_US |
dc.indexed.scopus | Scopus | en_US |
dc.indexed.pubmed | PubMed | en_US |
dc.wos.quartile | Q2 | en_US |
dc.contributor.scopusid | 55978575700 | tr_TR |
dc.subject.scopus | Hepatocellular carcinoma; Tumor microenvironment; Prognosis | en_US |
dc.subject.emtree | Kruppel like factor 4 | en_US |
dc.subject.emtree | Microrna | en_US |
dc.subject.emtree | Protein p53 | en_US |
dc.subject.emtree | Transcription factor ezh2 | en_US |
dc.subject.emtree | Tumor marker | en_US |
dc.subject.emtree | Microrna | en_US |
dc.subject.emtree | Tumor marker | en_US |
dc.subject.emtree | Article | en_US |
dc.subject.emtree | Bioinformatics | en_US |
dc.subject.emtree | Colorectal cancer | en_US |
dc.subject.emtree | Computer model | en_US |
dc.subject.emtree | Gene expression | en_US |
dc.subject.emtree | Gene function | en_US |
dc.subject.emtree | Gene identification | en_US |
dc.subject.emtree | Gene interaction | en_US |
dc.subject.emtree | Human | en_US |
dc.subject.emtree | Oncogene | en_US |
dc.subject.emtree | Priority journal | en_US |
dc.subject.emtree | Protein protein interaction | en_US |
dc.subject.emtree | Biology | en_US |
dc.subject.emtree | Cell cycle | en_US |
dc.subject.emtree | Colorectal tumor | en_US |
dc.subject.emtree | Gene expression profiling | en_US |
dc.subject.emtree | Genetic database | en_US |
dc.subject.emtree | Genetics | en_US |
dc.subject.emtree | Metabolism | en_US |
dc.subject.emtree | Pathology | en_US |
dc.subject.emtree | Procedures | en_US |
dc.subject.emtree | Protein analysis | en_US |
dc.subject.emtree | Signal transduction | en_US |
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