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Title: Identification of Genetic Polymorphism of Centrosomal Protein 290 to Assess Its Role as A Novel Diagnostic and Prognostic Biomarker for Ovarian Cancer
Authors: Ambreen Tauseef, Sana Batool, Zaima Ali, Uzma Zargham
Journal: Pakistan Journal of Health Sciences (PJHS)
Publisher: Lahore Medical Research Center
Country: Pakistan
Year: 2025
Volume: 6
Issue: 5
Language: en
Keywords: Genetic variationPolymorphismovarian cancerCentrosomal Protein 290
Ovarian cancer (OC) is one of the most lethal gynecological malignancies, with the dilemma of diagnosing at an advanced stage due to the lack of sensitive early detection markers. Centrosomal protein 290 (CEP290) is important for various functions within cellular processes, and its genetic changes may contribute to tumor development and progression. Identifying polymorphisms in the CEP290 gene could provide valuable insights into its potential as a novel diagnostic and prognostic biomarker for OC. Objective: To identify genetic polymorphism of CEP 290 in ovarian cancer and to determine its role as a novel prognostic and diagnostic biomarker for OC. Methods: A genetic profiling methodology was utilized to detect single-nucleotide polymorphisms (SNPs) within the CEP290 gene in patients diagnosed with ovarian cancer. Techniques such as polymerase chain reaction (PCR) and DNA sequencing were implemented to ascertain these genetic variations, followed by statistical analysis to evaluate their association with disease susceptibility and prognosis. Results: Two potential SNPs, CXCR2 C+785T and VEGF C+936T, were identified in the CEP290 gene. These polymorphisms may be linked to ovarian cancer pathogenesis, influencing tumor growth, angiogenesis, and immune response mechanisms. Conclusions: It was concluded that this study highlights CXCR2 C+785T and VEGF C+936T as potential SNPs within the CEP290 gene, suggesting their role in ovarian cancer diagnosis and prognosis. Further research with larger cohorts is necessary to validate these findings and discover their clinical utility as predictive biomarkers.
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