In silico analysis unveiling potential biomarkers in gallbladder carcinogenesis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38914609", "kind": "science-record", "revision": 4, "content_hash": "sha256:fe0f57e4547165deaa0a192eca4c0abd42869cb966eb1bf7382254880d0bca8c", "created_at": "2026-11-01T20:28:59Z", "updated_at": "2026-11-01T22:54:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "In silico analysis unveiling potential biomarkers in gallbladder carcinogenesis.", "doi": "10.1038/s41598-024-61762-4", "pmid": "38914609", "authors": ["Raviranjan Kumar Gupta", "Ravi Bhushan", "Saket Kumar", "Shyam Babu Prasad"], "journal": "Scientific reports", "year": 2024, "abstract": "Gallbladder cancer (GBC) is a rare but very aggressive most common digestive tract cancer with a high mortality rate due to delayed diagnosis at the advanced stage. Moreover, GBC progression shows asymptomatic characteristics making it impossible to detect at an early stage. In these circumstances, conventional therapy like surgery, chemotherapy, and radiotherapy becomes refractive. However, few studies reported some molecular markers like KRAS (Kirsten Rat Sarcoma) mutation, upregulation of HER2/neu, EGFR (Epidermal Growth Factor Receptor), and microRNAs in GBC. However, the absence of some specific early diagnostic and prognostic markers is the biggest hurdle for the therapy of GBC to date. The present study has been designed to identify some specific molecular markers for precise diagnosis, and prognosis, for successful treatment of the GBC. By In Silico a network-centric analysis of two microarray datasets; (GSE202479) and (GSE13222) from the Gene Expression Omnibus (GEO) database, shows 50 differentially expressed genes (DEGs) associated with GBC. Further network analysis revealed that 12 genes are highly interconnected based on the highest MCODE (Molecular Complex Detection) value, among all three genes; TRIP13 (Thyroid Receptor Interacting Protein), NEK2 (Never in Mitosis gene-A related Kinase 2), and TPX2 (Targeting Protein for Xklp2) having highest network interaction with transcription factors and miRNA suggesting critically associated with GBC. Further survival analysis data corroborate the association of these genes; TRIP13, NEK2, and TPX2 with GBC. Thus, TRIP13, NEK2, and TPX2 genes are significantly correlated with a greater risk of mortality, transforming them from mere biomarkers of the GBC for early detections and may emerge as prognostic markers for treatment.", "claims": [{"id": "claim-method", "statement": "The tools GEOquery and limma were used to perform differential expression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38914609", "locator": "sections[4] \"DEGs identification\" paragraph 2", "quote": "The tools GEOquery and limma were used to perform differential expression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The detailed datasets analyzed during the current study are available with the corresponding author. In the future, it will be made available on reasonable request. Few data were used under license for the current study. Therefore, it is not publicly available. 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