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<p>To assess the potential association between <i>LRP1 (rs715948)</i> and <i>PAI1 (rs2227631</i>, <i>rs1799889)</i> gene variation and ovarian cancer (OC) susceptibility. This study evaluated the genotypic and allelic distributions of <i>LRP1</i> gene and <i>PAI1</i> gene variants using Restriction Fragment Length Polymorphism (RFLP) analysis in 134&#xa0;°C patients and 134 healthy controls. <i>LRP1 (rs715948)</i> showed a significant association with OC risk. The TC genotype was (OR = 3.7823, 95% CI: 2.1732–6.5825, <i>p</i> &lt; 0.0001<b>)</b>, and the CC genotype has (OR = 2.1613, 95% CI: 1.0054–4.6459, <i>p</i> = 0.0484). The C allele was significantly more frequent in cases (46%) than controls (32%) (OR = 1.7684, 95% CI: 1.2443–2.5133, <i>p</i> = 0.0015). For <i>PAI1 (rs2227631)</i>, AG and GG genotypes showed no significant association (<i>p</i> = 0.3519 and <i>p</i> = 0.1165, respectively). <i>PAI1 (rs1799889)</i> AG genotype was (OR = 5.855, 95% CI: 2.4663–13.9027, <i>p</i> &lt; 0.0001), while GG genotype showed no significance (<i>p</i> = 0.1025). The dominant model of <i>LRP1</i>, (TC + CC) and C alleles, were significantly more frequent in OC cases, indicating a potential risk factor. In contrast, the dominant models (AG + GG) and G alleles of <i>PAI1 (rs2227631</i>,<i> rs1799889</i>) showed no significance with OC susceptibility. Genetic variation in <i>LRP1 (rs715948)</i> significantly associated with increased OC risk, particularly the TC and CC genotypes and C allele. The C allele of this gene is key markers linked to higher OC susceptibility. Whereas in <i>PAI1 (rs2227631</i>,<i> rs1799889)</i>, dominant models (AG + GG) show no significance, association suggesting a less prominent role in OC susceptibility. These findings highlight <i>LRP1</i> as a potential genetic biomarker for OC risk assessment, while the role of <i>PAI1</i> variants warrants further investigation in larger sample size.</p> Graphical Abstract <p></p>

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Unravelling Ovarian Cancer: an analysis of the Influence of LRP1 and PAI1 Genetic Variations

  • 期刊论文

  • 发表日期：

    2026年2月23日

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  • R. B. Devi Krishna,
  • Sneha Grace Mathews,
  • Preet Agarwal,
  • C. Elizabeth Rani,
  • Nandini Krishnamurthy,
  • Sanjana Murali,
  • A. Lohitha Rani,
  • Leena Dennis Joseph,
  • Banukeerthana Rajasekaran,
  • F. Andrea Mary

摘要

To assess the potential association between LRP1 (rs715948) and PAI1 (rs2227631, rs1799889) gene variation and ovarian cancer (OC) susceptibility. This study evaluated the genotypic and allelic distributions of LRP1 gene and PAI1 gene variants using Restriction Fragment Length Polymorphism (RFLP) analysis in 134 °C patients and 134 healthy controls. LRP1 (rs715948) showed a significant association with OC risk. The TC genotype was (OR = 3.7823, 95% CI: 2.1732–6.5825, p < 0.0001), and the CC genotype has (OR = 2.1613, 95% CI: 1.0054–4.6459, p = 0.0484). The C allele was significantly more frequent in cases (46%) than controls (32%) (OR = 1.7684, 95% CI: 1.2443–2.5133, p = 0.0015). For PAI1 (rs2227631), AG and GG genotypes showed no significant association (p = 0.3519 and p = 0.1165, respectively). PAI1 (rs1799889) AG genotype was (OR = 5.855, 95% CI: 2.4663–13.9027, p < 0.0001), while GG genotype showed no significance (p = 0.1025). The dominant model of LRP1, (TC + CC) and C alleles, were significantly more frequent in OC cases, indicating a potential risk factor. In contrast, the dominant models (AG + GG) and G alleles of PAI1 (rs2227631, rs1799889) showed no significance with OC susceptibility. Genetic variation in LRP1 (rs715948) significantly associated with increased OC risk, particularly the TC and CC genotypes and C allele. The C allele of this gene is key markers linked to higher OC susceptibility. Whereas in PAI1 (rs2227631, rs1799889), dominant models (AG + GG) show no significance, association suggesting a less prominent role in OC susceptibility. These findings highlight LRP1 as a potential genetic biomarker for OC risk assessment, while the role of PAI1 variants warrants further investigation in larger sample size.

Graphical Abstract