Wu Hui-Chen, Liao Yuyan, Lai Yunjia, Lin Po-Han, Santella Regina M, Miller Gary W, Terry Mary Beth
Department of Environmental Health Sciences, Mailman School of Public Health of Columbia University, New York, NY, 10032, USA.
Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA.
Breast Cancer Res. 2025 Sep 1;27(1):157. doi: 10.1186/s13058-025-02110-w.
Plasma proteins may serve as biomarkers for breast cancer. This study aimed to characterize the blood proteomic signatures of women with a higher risk of breast cancer due to their family history.
We conducted a nested case-control study (median followup: 9.8 years) within the New York site of the Breast Cancer Family Registry (BCFR) (n = 39 cases and 48 age-matched controls). We measured the expression levels as Normalized Protein Expression (NPX) of 92 proteins using the Olink Oncology panel. We then utilized an integrative network analysis of statistically significant protein markers and metabolomic profiles to better understand the potential molecular pathways involved in breast cancer.
We found four proteins were positively associated with breast cancer risk; the adjusted odds ratios (ORs) (95% confidence interval (CI) per 1-standard deviation (SD) increase in NPX were 1.87 (95% CI: 1.07, 3.28) for folate receptor (FR)-alpha, 2.72 (1.36, 5.44) for C-X-C motif chemokine 13 (CXCL13), 2.63 (1.32, 5.23) for amphiregulin (AREG), and 3.59 (95% CI: 1.58, 8.19) for mesothelin (MSLN). These results were no longer statistically significant after adjusting for multiple comparisons. Results from integrative network analysis using xMWAS suggested that the candidate protein markers were associated with distinct subsets of metabolites, forming single-protein-multiple metabolite clusters (|r|>0.3, p < 0.05).
While our results should be interpreted with caution, if replicated in larger prospective cohorts, these findings will have translational significance, attesting to the power of high-throughput profiling of circulating protein markers in identifying breast cancer biomarkers and important pathways involved in cancer development.
血浆蛋白可能作为乳腺癌的生物标志物。本研究旨在表征因家族病史而患乳腺癌风险较高的女性的血液蛋白质组特征。
我们在乳腺癌家族登记处(BCFR)的纽约站点开展了一项巢式病例对照研究(中位随访时间:9.8年)(n = 39例病例和48例年龄匹配的对照)。我们使用Olink肿瘤学检测板测量了92种蛋白质的标准化蛋白表达(NPX)水平。然后,我们对具有统计学意义的蛋白质标志物和代谢组学谱进行综合网络分析,以更好地了解参与乳腺癌的潜在分子途径。
我们发现四种蛋白质与乳腺癌风险呈正相关;NPX每增加1个标准差(SD),叶酸受体(FR)-α的校正比值比(OR)(95%置信区间(CI))为1.87(95%CI:1.07,3.28),C-X-C基序趋化因子13(CXCL13)为2.72(1.36,5.44),双调蛋白(AREG)为2.63(1.32,5.23),间皮素(MSLN)为3.59(95%CI:1.58,8.19)。在进行多重比较校正后,这些结果不再具有统计学意义。使用xMWAS进行的综合网络分析结果表明,候选蛋白质标志物与不同的代谢物亚组相关,形成单蛋白-多代谢物簇(|r|>0.3,p < 0.05)。
虽然我们的结果应谨慎解读,但如果在更大的前瞻性队列中得到验证,这些发现将具有转化意义,证明循环蛋白标志物的高通量分析在识别乳腺癌生物标志物和癌症发展中涉及的重要途径方面的作用。