Feng Jia, Qi Xing, Chen Chen, Li Baolin, Wang Min, Xie Xuelong, Yang Kailan, Liu Xuan, Chen Rui Min, Guo Tongtong, Liu Jinbo
Department of Laboratory Medicine, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
Department of Clinical Laboratory Medicine, Ziyang Central Hospital, Ziyang, 641300, Sichuan, China.
Sci Rep. 2025 Feb 25;15(1):6791. doi: 10.1038/s41598-025-90447-9.
Breast cancer (BC) is one of the most common malignancies among women globally, characterized by complex pathogenesis involving various biomarkers present in blood and urine. To enhance understanding of the genetic associations between biomarkers and BC via multidimensional, cross ethnic investigations. Based on GWAS data of 35 blood and urine biomarkers from European populations, we adopted multiple analysis strategies including univariable Mendelian randomization (MR) analysis, reverse MR analysis, sensitivity analysis and multivariate MR to identify potential biomarkers associated with BC risk and survival. Our initial analysis included 122,977 BC and 105,974 controls of European ancestry. Building upon these findings, we conducted cross ethnic validation by applying the same analyses to East Asian populations using data from the IEU GWAS database, which included 5,552 BC and 89,731 controls. This step allowed us to investigate the universality and heterogeneity of our identified biomarkers across different ancestries. Subsequently, utilizing clinical laboratory detection data from multiple regions in China, we performed differential analyses and survival assessments on these potential biomarkers to evaluate their clinical relevance and utility. Notably, we leveraged Luzhou's clinical data to integrate HDL-C with conventional tumor markers (CEA, CA125, CA153) into a machine learning model, comparing its diagnostic efficacy against tumor marker combination. Our study validated associations of ALP, HDL-C, TG, SHBG, and IGF-1 with BC risk, reinforcing the reliability of these findings. Moreover, notable interethnic disparities emerged in the association between HDL-C and BC risk, where in HDL-C demonstrates a contrasting role: acting as a genetic protective agent against BC and suggesting promise as an auxiliary diagnostic marker in East Asian populations, yet inversely, it serves as a genetic dangerous predictor in European populations. Analyzing BC subtypes, we identified associations of HDL-C, TG, SHBG, and CRP with ERBC, while ERBC showed associations with GLU, urinary creatinine and microalbuminuria, underscoring subtype-specific genetic characteristics critical for personalized prevention and treatment strategies. Overall, this comprehensive study, by traversing the intricate landscape of genetic associations across ethnic boundaries and employing advanced analytical methodologies, not only uncovers the complex interplay between key biomarkers and BC susceptibility but also highlights the significance of ethnic-specific differences in the role of HDL-C. By enhancing the diagnostic power of a tailored biomarker panel through machine learning, this study contributes to the advancement of precision medicine in BC, offering strategies tailored to the unique genetic profiles and biomarker patterns across diverse populations.
乳腺癌（BC）是全球女性中最常见的恶性肿瘤之一，其发病机制复杂，涉及血液和尿液中的多种生物标志物。为了通过多维度、跨种族研究加深对生物标志物与乳腺癌之间遗传关联的理解。基于欧洲人群35种血液和尿液生物标志物的全基因组关联研究（GWAS）数据，我们采用了多种分析策略，包括单变量孟德尔随机化（MR）分析、反向MR分析、敏感性分析和多变量MR，以确定与乳腺癌风险和生存相关的潜在生物标志物。我们的初始分析纳入了122,977名具有欧洲血统的乳腺癌患者和105,974名对照。基于这些发现，我们通过使用IEU GWAS数据库的数据对东亚人群进行相同的分析来进行跨种族验证，该数据库包括5,552名乳腺癌患者和89,731名对照。这一步骤使我们能够研究已识别生物标志物在不同血统人群中的普遍性和异质性。随后，利用来自中国多个地区的临床实验室检测数据，我们对这些潜在生物标志物进行了差异分析和生存评估，以评估它们的临床相关性和实用性。值得注意的是，我们利用泸州的临床数据将高密度脂蛋白胆固醇（HDL-C）与传统肿瘤标志物（癌胚抗原、CA125、CA153）整合到一个机器学习模型中，比较其与肿瘤标志物组合的诊断效能。我们的研究验证了碱性磷酸酶（ALP）、HDL-C、甘油三酯（TG）、性激素结合球蛋白（SHBG）和胰岛素样生长因子-1（IGF-1）与乳腺癌风险的关联，增强了这些发现的可靠性。此外，HDL-C与乳腺癌风险之间的关联存在显著的种族差异，其中HDL-C表现出相反的作用：在东亚人群中它作为预防乳腺癌的遗传保护因子，有望作为辅助诊断标志物；而在欧洲人群中，它却作为遗传危险因素的预测指标。通过分析乳腺癌亚型，我们确定了HDL-C、TG、SHBG和C反应蛋白（CRP）与雌激素受体阳性乳腺癌（ERBC）的关联，而ERBC与葡萄糖、尿肌酐和微量白蛋白尿有关，突出了亚型特异性遗传特征对个性化预防和治疗策略的重要性。总体而言,这项全面的研究跨越种族界限，深入探究复杂的遗传关联格局，并运用先进的分析方法，不仅揭示了关键生物标志物与乳腺癌易感性之间的复杂相互作用，还凸显了HDL-C作用中种族特异性差异的重要性。通过机器学习提高定制生物标志物组合的诊断能力，本研究为乳腺癌精准医学的发展做出了贡献，为不同人群独特的基因谱和生物标志物模式提供了量身定制的策略。
