PO.CL01.05 · 临床研究
NME1、CXCL12、VDR、DNMT1、CAV1、IL27和IL33多态性与孟加拉国女性乳腺癌易感性的关联：一项病例对照与计算机模拟研究
Association of NME1, CXCL12, VDR, DNMT1, CAV1, IL27, and IL33 polymorphisms with breast cancer susceptibility in Bangladeshi women: A case-control and in silico study
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摘要 Abstract
中文摘要
目的：乳腺癌(BC)仍是最常见的癌症之一，也是全球女性死亡的主要原因。尽管治疗取得进展，识别影响易感性的遗传因素对于高风险人群仍至关重要。本研究旨在探讨NME1(rs16949649)、CXCL12(rs2839693, rs1801157)、VDR(rs7975232, rs731236, rs2228570)、DNMT1(rs16999593)、CAV1(rs3807987)、IL27(rs181206)和IL33(rs7044343)的多态性是否与孟加拉国女性BC相关，结合病例对照数据与计算机模拟分析。
方法：我们分析了250例经组织学确诊的BC病例和250例年龄匹配的健康对照。通过PCR-RFLP进行基因分型。计算了比值比(ORs)及其95%置信区间(CIs)，p<0.05被认为具有显著性。为补充这些发现，使用GEPIA、UALCAN、SIFT、PolyPhen-2、CADD、PredictSNP、Mutation Assessor、MuPro和I-Mutant进行了变异影响的计算机模拟预测。
结果：NME1 rs16949649显示与BC存在显著关联(显性模型：OR=2.24，p=0.040；等位基因模型：OR=2.44，p=0.045)。CXCL12 rs2839693也增加风险(显性模型：OR=1.69，p=0.017；等位基因模型：OR=1.67，p=0.008)。在VDR变异中，rs7975232和rs731236与BC呈正相关，而rs2228570具有保护作用(加性模型2：OR=0.36，p=0.009)。在病例对照分析中，DNMT1 rs16999593、CAV1 rs3807987、IL27 rs181206、IL33 rs7044343或CXCL12 rs1801157均未观察到有意义的关联。有趣的是，计算机模拟建模提示DNMT1 rs16999593(H97R)可能降低蛋白稳定性，部分预测工具指出其可能具有疾病相关性。对于IL33 rs7044343(C>T)，T等位基因被预测会增加BC易感性，反映了IL-33在肿瘤生物学中的情境依赖性作用。
结论：本研究凸显NME1 rs16949649、CXCL12 rs2839693和VDR变异(rs7975232、rs731236、rs2228570)作为孟加拉国女性BC风险的重要遗传标志物。虽然病例对照分析未证实DNMT1和IL33的显著效应，但计算预测提示它们可能影响蛋白功能，值得进一步探索。将遗传关联与计算机模拟分析相结合，可为深入认识代表性不足人群的乳腺癌易感性提供更深刻的见解。
查看英文原文 English abstract
Purpose: Breast cancer (BC) remains one of the most common cancers and a leading cause of death among women worldwide. Despite therapeutic advances, identifying genetic factors that influence susceptibility is crucial for populations at high risk. This study aimed to investigate whether polymorphisms in NME1 (rs16949649), CXCL12 (rs2839693, rs1801157), VDR (rs7975232, rs731236, rs2228570), DNMT1 (rs16999593), CAV1 (rs3807987), IL27 (rs181206), and IL33 (rs7044343) are linked to BC in Bangladeshi women, combining case-control data with in-silico analysis.
Methods: We analyzed 250 histologically confirmed BC cases and 250 age-matched healthy controls. Genotyping was carried out by PCR-RFLP. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated, and p<0.05 was considered significant. To complement these findings, in-silico predictions of variant impact were performed using GEPIA, UALCAN, SIFT, PolyPhen-2, CADD, PredictSNP, Mutation Assessor, MuPro, and I-Mutant.
Results: NME1 rs16949649 showed a significant association with BC (dominant model: OR=2.24, p=0.040; allele model: OR=2.44, p=0.045). CXCL12 rs2839693 also increased risk (dominant model: OR=1.69, p=0.017; allele model: OR=1.67, p=0.008). Among VDR variants, rs7975232 and rs731236 were positively associated with BC, whereas rs2228570 was protective (additive model 2: OR=0.36, p=0.009). No meaningful associations were observed for DNMT1 rs16999593, CAV1 rs3807987, IL27 rs181206, IL33 rs7044343, or CXCL12 rs1801157 in the case-control analysis. Interestingly, in-silico modeling suggested that DNMT1 rs16999593 (H97R) could reduce protein stability, with some predictors indicating possible disease relevance. For IL33 rs7044343 (C>T), the T allele was predicted to increase BC susceptibility, reflecting IL-33's context-dependent roles in tumor biology.
Conclusions: This study highlights NME1 rs16949649, CXCL12 rs2839693, and VDR variants (rs7975232, rs731236, rs2228570) as important genetic markers of BC risk in Bangladeshi women. While case-control analysis did not confirm significant effects for DNMT1 and IL33 , computational predictions suggest they may influence protein function and deserve further exploration. Integrating genetic association with in-silico analysis can provide deeper insights into breast cancer susceptibility in underrepresented populations.
利益披露 Disclosure
M. Islam, None..
M. Barek, None.