Jia Guochong, Ping Jie, Tao Ran, Long Jirong, Liu Lili, Xu Shuai, Munro Heather M, Ambs Stefan, Barnard Mollie E, Chen Yu, Choi Ji-Yeob, Gao Yu-Tang, Garcia-Closas Montserrat, Gu Jian, Hu Jennifer J, Iwasaki Motoki, John Esther M, Kweon Sun-Seog, Matsuda Koichi, Matsuo Keitaro, Nathanson Katherine, Nemesure Barbara, Olopade Olufunmilayo I, Pal Tuya, Park Sue K, Park Boyoung, Press Michael F, Sanderson Maureen, Sandler Dale P, Yao Song, Zheng Ying, Adejumo Prisca O, Ahearn Thomas, Brewster Abenaa M, Hennis Anselm J M, Ito Hidemi, Kubo Michiaki, Lee Eun-Sook, Low Siew-Kee, Makumbi Timothy, Ndom Paul, Noh Dong-Young, O'Brien Katie M, Olshan Andrew F, Oluwasanu Mojisola M, Park Min-Ho, Reid Sonya, Yamaji Taiki, Zirpoli Gary, Butler Ebonee N, Huang Maosheng, Ntekim Atara, Weinberg Clarice R, Li Bingshan, Huo Dezheng, Kang Daehee, Ambrosone Christine, Troester Melissa A, Haiman Christopher A, Shu Xiao-Ou, Palmer Julie R, Guo Xingyi, Zheng Wei
Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Int J Cancer. 2025 Jul 14. doi: 10.1002/ijc.70041.
Genome-wide association studies (GWAS) have identified more than 200 risk loci for breast cancer. However, target genes and their encoded proteins in these loci remain largely unknown. In this study, we utilized genetic prediction models for 1349 circulating proteins derived from individuals of African (n = 1871) and European (n = 7213) ancestry to investigate genetically predicted protein levels in association with breast cancer risk among females of African (n = 40,138), Asian (n = 137,677), and European (n = 247,173) ancestry. We identified 51 blood protein biomarkers associated with breast cancer risk, overall or by subtypes, at a false discovery rate (FDR) < 0.05, including 27 proteins encoded by genes located at least 1 Mb away from any of the known risk loci identified in GWAS. Of them, 32 proteins showed significant associations with breast cancer risk at the Bonferroni-corrected significance level (p < 2.45 × 10). Of the 24 proteins located at GWAS-identified risk loci, associations for 14 proteins were significantly attenuated after adjustment for the index risk variant of each respective locus, suggesting that these proteins may be target proteins for the risk loci. Encoding gene expression levels in normal breast tissue could be genetically predicted for 23 of the 51 identified proteins, and 13 encoding genes were associated with breast cancer risk in the same direction (p < .05). Our study identified potential protein targets of GWAS risk loci and biomarkers for breast cancer risk and provided additional insights into breast cancer genetics and etiology.
全基因组关联研究（GWAS）已确定了200多个乳腺癌风险位点。然而，这些位点中的靶基因及其编码蛋白在很大程度上仍不为人知。在本研究中，我们利用针对来自非洲（n = 1871）和欧洲（n = 7213）血统个体的1349种循环蛋白的遗传预测模型，来研究非洲（n = 40138）、亚洲（n = 137677）和欧洲（n = 247173）血统女性中与乳腺癌风险相关的遗传预测蛋白水平。我们在错误发现率（FDR）< 0.05的情况下，确定了51种与乳腺癌风险总体或按亚型相关的血液蛋白生物标志物，其中包括27种由距离GWAS中确定的任何已知风险位点至少1 Mb的基因编码的蛋白。其中，32种蛋白在Bonferroni校正的显著性水平（p < 2.45×10）下与乳腺癌风险存在显著关联。在位于GWAS确定的风险位点的24种蛋白中，对14种蛋白的关联在对每个相应位点的索引风险变异进行调整后显著减弱，这表明这些蛋白可能是风险位点的靶蛋白。对于51种已确定蛋白中的23种，可以通过基因预测正常乳腺组织中的编码基因表达水平，并且13种编码基因与乳腺癌风险呈相同方向的关联（p < 0.05）。我们的研究确定了GWAS风险位点的潜在蛋白靶点以及乳腺癌风险的生物标志物，并为乳腺癌遗传学和病因学提供了更多见解。
