ArticleMolecular medicine reports2018
Identification of key genes and associated pathways in KIT/PDGFRA wild‑type gastrointestinal stromal tumors through bioinformatics analysis.
Article in Molecular medicine reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- The Mechanisms of Imatinib Resistance in Gastrointestinal Stromal Tumours: Theoretical Basis and Therapeutic Aspect.Journal of cellular and molecular medicine · 2025Review
- c-Kit Receptors as a Therapeutic Target in Cancer: Current Insights.OncoTargets and therapy · 2023Review
- Update on Molecular Genetics of Gastrointestinal Stromal Tumors.Diagnostics (Basel, Switzerland) · 2021Review
- Gastrointestinal Stromal Tumors (GISTs): Novel Therapeutic Strategies with Immunotherapy and Small Molecules.International journal of molecular sciences · 2021Review
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastrointestinal stromal tumors (GISTs) are the most common type of mesenchymal tumor in the gastrointestinal tract. The present study aimed to identify the potential candidate biomarkers that may be involved in the pathogenesis and progression of v‑kit Hardy‑Zuckerman 4 feline sarcoma viral oncogene homolog (KIT)/platelet‑derived growth factor receptor α (PDGFRA) wild‑type GISTs. A joint bioinformatics analysis was performed to identify the differentially expressed genes (DEGs) in wild‑type GIST samples compared with KIT/PDGFRA mutant GIST samples. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs was conducted using Database for Annotation, Visualization and Integrated Discovery and KEGG Orthology‑Based Annotation System (KOBAS) online tools, respectively. Protein‑protein interaction (PPI) networks of the DEGs were constructed using Search Tool for the Retrieval of Interacting Genes online tool and Cytoscape, and divided into sub‑networks using the Molecular Complex Detection (MCODE) plug‑in. Furthermore, enrichment analysis of DEGs in the modules was analyzed with KOBAS. In total, 546 DEGs were identified, including 238 upregulated genes primarily enriched in 'cell adhesion', 'biological adhesion', 'cell‑cell signaling', 'PI3K‑Akt signaling pathway' and 'ECM‑receptor interaction', while the 308 downregulated genes were predominantly involved in 'inflammatory response', 'sterol metabolic process' and 'fatty acid metabolic process', 'small GTPase mediated signal transduction', 'cAMP signaling pathway' and 'proteoglycans in cancer'. A total of 25 hub genes were obtained and four modules were mined from the PPI network, and sub‑networks also revealed these genes were primarily involved in significant pathways, including 'PI3K‑Akt signaling pathway', 'proteoglycans in cancer', 'pathways in cancer', 'Rap1 signaling pathway', 'ECM‑receptor interaction', 'phospholipase D signaling pathway', 'ras signaling pathway' and 'cGMP‑PKG signaling pathway'. These results suggested that several key hub DEGs may serve as potential candidate biomarkers for wild‑type GISTs, including phosphatidylinositol‑4,5‑bisphosphate 3‑kinase, catalytic subunit γ, insulin like growth factor 1 receptor, hepatocyte growth factor, thrombospondin 1, Erb‑B2 receptor tyrosine kinase 2 and matrix metallopeptidase 2. However, further experiments are required to confirm these results.
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