Guochong Jia, Jie Ping, Ran Tao, Jirong Long, Lili Liu, Shuai Xu, Heather M. Munro, Stefan Ambs, Mollie E. Barnard, Yu Chen, Ji-Yeob Choi, Yu-Tang Gao, Montserrat Garcia-Closas, Jian Gu, Jennifer J. Hu, Motoki Iwasaki, Esther M. John, Sun-Seog Kweon, Koichi Matsuda, Keitaro Matsuo, Katherine Nathanson, Barbara Nemesure, Olufunmilayo I. Olopade, Tuya Pal, Sue K. Park, Boyoung Park, Michael F. Press, Maureen Sanderson, Dale P. Sandler, Song Yao, Ying Zheng, Prisca O. Adejumo, Thomas Ahearn, Abenaa M. Brewster, Anselm J. M. Hennis, Hidemi Ito, Michiaki Kubo, Eun-Sook Lee, Siew-Kee Low, Timothy Makumbi, Paul Ndom, Dong-Young Noh, Katie M. O'Brien, Andrew F. Olshan, Mojisola M. Oluwasanu, Min-Ho Park, Sonya Reid, Taiki Yamaji, Gary Zirpoli, Ebonee N. Butler, Maosheng Huang, Atara Ntekim, Clarice R. Weinberg, Bingshan Li, Dezheng Huo, Daehee Kang, Christine Ambrosone, Melissa A. Troester, Christopher A. Haiman, Xiao-Ou Shu, Julie R. Palmer, Xingyi Guo, Wei Zheng
{"title":"整合多祖先基因组和蛋白质组学数据,识别乳腺癌遗传风险位点的血液风险生物标志物和靶蛋白。","authors":"Guochong Jia, Jie Ping, Ran Tao, Jirong Long, Lili Liu, Shuai Xu, Heather M. Munro, Stefan Ambs, Mollie E. Barnard, Yu Chen, Ji-Yeob Choi, Yu-Tang Gao, Montserrat Garcia-Closas, Jian Gu, Jennifer J. Hu, Motoki Iwasaki, Esther M. John, Sun-Seog Kweon, Koichi Matsuda, Keitaro Matsuo, Katherine Nathanson, Barbara Nemesure, Olufunmilayo I. Olopade, Tuya Pal, Sue K. Park, Boyoung Park, Michael F. Press, Maureen Sanderson, Dale P. Sandler, Song Yao, Ying Zheng, Prisca O. Adejumo, Thomas Ahearn, Abenaa M. Brewster, Anselm J. M. Hennis, Hidemi Ito, Michiaki Kubo, Eun-Sook Lee, Siew-Kee Low, Timothy Makumbi, Paul Ndom, Dong-Young Noh, Katie M. O'Brien, Andrew F. Olshan, Mojisola M. Oluwasanu, Min-Ho Park, Sonya Reid, Taiki Yamaji, Gary Zirpoli, Ebonee N. Butler, Maosheng Huang, Atara Ntekim, Clarice R. Weinberg, Bingshan Li, Dezheng Huo, Daehee Kang, Christine Ambrosone, Melissa A. Troester, Christopher A. Haiman, Xiao-Ou Shu, Julie R. Palmer, Xingyi Guo, Wei Zheng","doi":"10.1002/ijc.70041","DOIUrl":null,"url":null,"abstract":"<p>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 (<i>n</i> = 1871) and European (<i>n</i> = 7213) ancestry to investigate genetically predicted protein levels in association with breast cancer risk among females of African (<i>n</i> = 40,138), Asian (<i>n</i> = 137,677), and European (<i>n</i> = 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 (<i>p</i> < 2.45 × 10<sup>−4</sup>). 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 (<i>p</i> < .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.</p>","PeriodicalId":180,"journal":{"name":"International Journal of Cancer","volume":"157 10","pages":"2071-2080"},"PeriodicalIF":4.7000,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrating multi-ancestry genomic and proteomic data to identify blood risk biomarkers and target proteins for breast cancer genetic risk loci\",\"authors\":\"Guochong Jia, Jie Ping, Ran Tao, Jirong Long, Lili Liu, Shuai Xu, Heather M. Munro, Stefan Ambs, Mollie E. Barnard, Yu Chen, Ji-Yeob Choi, Yu-Tang Gao, Montserrat Garcia-Closas, Jian Gu, Jennifer J. Hu, Motoki Iwasaki, Esther M. John, Sun-Seog Kweon, Koichi Matsuda, Keitaro Matsuo, Katherine Nathanson, Barbara Nemesure, Olufunmilayo I. Olopade, Tuya Pal, Sue K. Park, Boyoung Park, Michael F. Press, Maureen Sanderson, Dale P. Sandler, Song Yao, Ying Zheng, Prisca O. Adejumo, Thomas Ahearn, Abenaa M. Brewster, Anselm J. M. Hennis, Hidemi Ito, Michiaki Kubo, Eun-Sook Lee, Siew-Kee Low, Timothy Makumbi, Paul Ndom, Dong-Young Noh, Katie M. O'Brien, Andrew F. Olshan, Mojisola M. Oluwasanu, Min-Ho Park, Sonya Reid, Taiki Yamaji, Gary Zirpoli, Ebonee N. Butler, Maosheng Huang, Atara Ntekim, Clarice R. Weinberg, Bingshan Li, Dezheng Huo, Daehee Kang, Christine Ambrosone, Melissa A. Troester, Christopher A. Haiman, Xiao-Ou Shu, Julie R. Palmer, Xingyi Guo, Wei Zheng\",\"doi\":\"10.1002/ijc.70041\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 (<i>n</i> = 1871) and European (<i>n</i> = 7213) ancestry to investigate genetically predicted protein levels in association with breast cancer risk among females of African (<i>n</i> = 40,138), Asian (<i>n</i> = 137,677), and European (<i>n</i> = 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 (<i>p</i> < 2.45 × 10<sup>−4</sup>). 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 (<i>p</i> < .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.</p>\",\"PeriodicalId\":180,\"journal\":{\"name\":\"International Journal of Cancer\",\"volume\":\"157 10\",\"pages\":\"2071-2080\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2025-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/ijc.70041\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Cancer","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ijc.70041","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
Integrating multi-ancestry genomic and proteomic data to identify blood risk biomarkers and target proteins for breast cancer genetic risk loci
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 × 10−4). 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.
期刊介绍:
The International Journal of Cancer (IJC) is the official journal of the Union for International Cancer Control—UICC; it appears twice a month. IJC invites submission of manuscripts under a broad scope of topics relevant to experimental and clinical cancer research and publishes original Research Articles and Short Reports under the following categories:
-Cancer Epidemiology-
Cancer Genetics and Epigenetics-
Infectious Causes of Cancer-
Innovative Tools and Methods-
Molecular Cancer Biology-
Tumor Immunology and Microenvironment-
Tumor Markers and Signatures-
Cancer Therapy and Prevention