Discovery of primary prostate cancer biomarkers using cross cancer learning

Abstract Prostate cancer (PCa), the second leading cause of cancer death in American men, is a relatively slow-growing malignancy with multiple early treatment options. Yet, a significant number of low-risk PCa patients are over-diagnosed and over-treated with significant and long-term quality of li...

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Autores principales:	Kaiyue Zhou, Suzan Arslanturk, Douglas B. Craig, Elisabeth Heath, Sorin Draghici
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ce495cea89524f4a9f69c416fc8a182a
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spelling	oai:doaj.org-article:ce495cea89524f4a9f69c416fc8a182a2021-12-02T15:53:10ZDiscovery of primary prostate cancer biomarkers using cross cancer learning10.1038/s41598-021-89789-x2045-2322https://doaj.org/article/ce495cea89524f4a9f69c416fc8a182a2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89789-xhttps://doaj.org/toc/2045-2322Abstract Prostate cancer (PCa), the second leading cause of cancer death in American men, is a relatively slow-growing malignancy with multiple early treatment options. Yet, a significant number of low-risk PCa patients are over-diagnosed and over-treated with significant and long-term quality of life effects. Further, there is ever increasing evidence of metastasis and higher mortality when hormone-sensitive or castration-resistant PCa tumors are treated indistinctively. Hence, the critical need is to discover clinically-relevant and actionable PCa biomarkers by better understanding the biology of PCa. In this paper, we have discovered novel biomarkers of PCa tumors through cross-cancer learning by leveraging the pathological and molecular similarities in the DNA repair pathways of ovarian, prostate, and breast cancer tumors. Cross-cancer disease learning enriches the study population and identifies genetic/phenotypic commonalities that are important across diseases with pathological and molecular similarities. Our results show that ADIRF, SLC2A5, C3orf86, HSPA1B are among the most significant PCa biomarkers, while MTRNR2L1, EEPD1, TEPP and VN1R2 are jointly important biomarkers across prostate, breast and ovarian cancers. Our validation results have further shown that the discovered biomarkers can predict the disease state better than any randomly selected subset of differentially expressed prostate cancer genes.Kaiyue ZhouSuzan ArslanturkDouglas B. CraigElisabeth HeathSorin DraghiciNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kaiyue Zhou Suzan Arslanturk Douglas B. Craig Elisabeth Heath Sorin Draghici Discovery of primary prostate cancer biomarkers using cross cancer learning
description	Abstract Prostate cancer (PCa), the second leading cause of cancer death in American men, is a relatively slow-growing malignancy with multiple early treatment options. Yet, a significant number of low-risk PCa patients are over-diagnosed and over-treated with significant and long-term quality of life effects. Further, there is ever increasing evidence of metastasis and higher mortality when hormone-sensitive or castration-resistant PCa tumors are treated indistinctively. Hence, the critical need is to discover clinically-relevant and actionable PCa biomarkers by better understanding the biology of PCa. In this paper, we have discovered novel biomarkers of PCa tumors through cross-cancer learning by leveraging the pathological and molecular similarities in the DNA repair pathways of ovarian, prostate, and breast cancer tumors. Cross-cancer disease learning enriches the study population and identifies genetic/phenotypic commonalities that are important across diseases with pathological and molecular similarities. Our results show that ADIRF, SLC2A5, C3orf86, HSPA1B are among the most significant PCa biomarkers, while MTRNR2L1, EEPD1, TEPP and VN1R2 are jointly important biomarkers across prostate, breast and ovarian cancers. Our validation results have further shown that the discovered biomarkers can predict the disease state better than any randomly selected subset of differentially expressed prostate cancer genes.
format	article
author	Kaiyue Zhou Suzan Arslanturk Douglas B. Craig Elisabeth Heath Sorin Draghici
author_facet	Kaiyue Zhou Suzan Arslanturk Douglas B. Craig Elisabeth Heath Sorin Draghici
author_sort	Kaiyue Zhou
title	Discovery of primary prostate cancer biomarkers using cross cancer learning
title_short	Discovery of primary prostate cancer biomarkers using cross cancer learning
title_full	Discovery of primary prostate cancer biomarkers using cross cancer learning
title_fullStr	Discovery of primary prostate cancer biomarkers using cross cancer learning
title_full_unstemmed	Discovery of primary prostate cancer biomarkers using cross cancer learning
title_sort	discovery of primary prostate cancer biomarkers using cross cancer learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ce495cea89524f4a9f69c416fc8a182a
work_keys_str_mv	AT kaiyuezhou discoveryofprimaryprostatecancerbiomarkersusingcrosscancerlearning AT suzanarslanturk discoveryofprimaryprostatecancerbiomarkersusingcrosscancerlearning AT douglasbcraig discoveryofprimaryprostatecancerbiomarkersusingcrosscancerlearning AT elisabethheath discoveryofprimaryprostatecancerbiomarkersusingcrosscancerlearning AT sorindraghici discoveryofprimaryprostatecancerbiomarkersusingcrosscancerlearning
_version_	1718385538568814592

Discovery of primary prostate cancer biomarkers using cross cancer learning

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