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Identification of novel biomarkers correlated with prostate cancer progression by an integrated bioinformatic analysis
Prostate cancer (PCa) is a highly aggressive malignant tumor and the biological mechanisms underlying its progression remain unclear.We performed weighted gene co-expression network analysis in PCa dataset from the Cancer Genome Atlas database to identify the key module and key genes related to the...
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Published in: | Medicine (Baltimore) 2020-07, Vol.99 (28), p.e21158-e21158 |
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description | Prostate cancer (PCa) is a highly aggressive malignant tumor and the biological mechanisms underlying its progression remain unclear.We performed weighted gene co-expression network analysis in PCa dataset from the Cancer Genome Atlas database to identify the key module and key genes related to the progression of PCa. Furthermore, another independent datasets were used to validate our findings.A total of 744 differentially expressed genes were screened out and 5 modules were identified for PCa samples from the Cancer Genome Atlas database. We found the brown module was the key module and related to tumor grade (R2 = 0.52) and tumor invasion depth (R2 = 0.39). Besides, 24 candidate hub genes were screened out and 2 genes (BIRC5 and DEPDC1B) were identified and validated as real hub genes that associated with the progression and prognosis of PCa. Moreover, the biological roles of BIRC5 were related to G-protein coupled receptor signal pathway, and the functions of DEPDC1B were related to the G-protein coupled receptor signal pathway and retinol metabolism in PCa.Taken together, we identified 1 module, 24 candidate hub genes and 2 real hub genes, which were prominently associated with PCa progression. With more experiments and clinical trials, these genes may provide a promising future for PCa treatment. |
doi_str_mv | 10.1097/MD.0000000000021158 |
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Furthermore, another independent datasets were used to validate our findings.A total of 744 differentially expressed genes were screened out and 5 modules were identified for PCa samples from the Cancer Genome Atlas database. We found the brown module was the key module and related to tumor grade (R2 = 0.52) and tumor invasion depth (R2 = 0.39). Besides, 24 candidate hub genes were screened out and 2 genes (BIRC5 and DEPDC1B) were identified and validated as real hub genes that associated with the progression and prognosis of PCa. Moreover, the biological roles of BIRC5 were related to G-protein coupled receptor signal pathway, and the functions of DEPDC1B were related to the G-protein coupled receptor signal pathway and retinol metabolism in PCa.Taken together, we identified 1 module, 24 candidate hub genes and 2 real hub genes, which were prominently associated with PCa progression. With more experiments and clinical trials, these genes may provide a promising future for PCa treatment.</description><identifier>ISSN: 0025-7974</identifier><identifier>EISSN: 1536-5964</identifier><identifier>DOI: 10.1097/MD.0000000000021158</identifier><identifier>PMID: 32664150</identifier><language>eng</language><publisher>United States: the Author(s). Published by Wolters Kluwer Health, Inc</publisher><subject>Biomarkers, Tumor - biosynthesis ; Biomarkers, Tumor - genetics ; Computational Biology - methods ; Disease Progression ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Humans ; Male ; Neoplasm Grading ; Observational Study ; Prognosis ; Prostatic Neoplasms - diagnosis ; Prostatic Neoplasms - genetics ; Prostatic Neoplasms - metabolism ; Transcriptome - genetics</subject><ispartof>Medicine (Baltimore), 2020-07, Vol.99 (28), p.e21158-e21158</ispartof><rights>the Author(s). Published by Wolters Kluwer Health, Inc.</rights><rights>Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4500-64ebc2fca895435ba6a44fbc184ec34cf073452280eee126a07142796c27a5663</citedby><cites>FETCH-LOGICAL-c4500-64ebc2fca895435ba6a44fbc184ec34cf073452280eee126a07142796c27a5663</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7360283/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7360283/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32664150$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ma, Zhifang</creatorcontrib><creatorcontrib>Wang, Jianming</creatorcontrib><creatorcontrib>Ding, Lingyan</creatorcontrib><creatorcontrib>Chen, Yujun</creatorcontrib><title>Identification of novel biomarkers correlated with prostate cancer progression by an integrated bioinformatic analysis</title><title>Medicine (Baltimore)</title><addtitle>Medicine (Baltimore)</addtitle><description>Prostate cancer (PCa) is a highly aggressive malignant tumor and the biological mechanisms underlying its progression remain unclear.We performed weighted gene co-expression network analysis in PCa dataset from the Cancer Genome Atlas database to identify the key module and key genes related to the progression of PCa. Furthermore, another independent datasets were used to validate our findings.A total of 744 differentially expressed genes were screened out and 5 modules were identified for PCa samples from the Cancer Genome Atlas database. We found the brown module was the key module and related to tumor grade (R2 = 0.52) and tumor invasion depth (R2 = 0.39). Besides, 24 candidate hub genes were screened out and 2 genes (BIRC5 and DEPDC1B) were identified and validated as real hub genes that associated with the progression and prognosis of PCa. Moreover, the biological roles of BIRC5 were related to G-protein coupled receptor signal pathway, and the functions of DEPDC1B were related to the G-protein coupled receptor signal pathway and retinol metabolism in PCa.Taken together, we identified 1 module, 24 candidate hub genes and 2 real hub genes, which were prominently associated with PCa progression. With more experiments and clinical trials, these genes may provide a promising future for PCa treatment.</description><subject>Biomarkers, Tumor - biosynthesis</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Computational Biology - methods</subject><subject>Disease Progression</subject><subject>Gene Expression Profiling</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Humans</subject><subject>Male</subject><subject>Neoplasm Grading</subject><subject>Observational Study</subject><subject>Prognosis</subject><subject>Prostatic Neoplasms - diagnosis</subject><subject>Prostatic Neoplasms - genetics</subject><subject>Prostatic Neoplasms - metabolism</subject><subject>Transcriptome - genetics</subject><issn>0025-7974</issn><issn>1536-5964</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNpdUU1vEzEQtRCIhsIvQEJ75LJl_L17QUItH5Va9VLOlteZTUwdO9ibRPn3OE0pLb5Y43nvjd88Qt5TOKPQ60_XF2fw7zBKZfeCzKjkqpW9Ei_JrL7KVvdanJA3pfwCoFwz8ZqccKaUoBJmZHs5xzj50Ts7-RSbNDYxbTE0g08rm-8wl8alnDHYCefNzk_LZp1TmWrZOBsd5kO9yFjKgT_sGxsbHydc5HtG1fFxTHlV9V3t2bAvvrwlr0YbCr57uE_Jz29fb89_tFc33y_Pv1y1TkiAVgkcHBud7XopuBysskKMg6OdQMeFG0FzIRnrABEpUxY0FUz3yjFtpVL8lHw-6q43wwrnrnrNNph19tXc3iTrzfNO9EuzSFujuQLW8Srw8UEgp98bLJNZ-eIwBBsxbYphggnoFVVQofwIdXU_JeP4OIaCOSRmri_M_4lV1oenP3zk_I2oAsQRsEthqnHchc0Os1miDdPyXk_qnrUMGFT3dWk1ZgD-B-5OpCE</recordid><startdate>20200710</startdate><enddate>20200710</enddate><creator>Ma, Zhifang</creator><creator>Wang, Jianming</creator><creator>Ding, Lingyan</creator><creator>Chen, Yujun</creator><general>the Author(s). Published by Wolters Kluwer Health, Inc</general><general>Wolters Kluwer Health</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20200710</creationdate><title>Identification of novel biomarkers correlated with prostate cancer progression by an integrated bioinformatic analysis</title><author>Ma, Zhifang ; Wang, Jianming ; Ding, Lingyan ; Chen, Yujun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4500-64ebc2fca895435ba6a44fbc184ec34cf073452280eee126a07142796c27a5663</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Biomarkers, Tumor - biosynthesis</topic><topic>Biomarkers, Tumor - genetics</topic><topic>Computational Biology - methods</topic><topic>Disease Progression</topic><topic>Gene Expression Profiling</topic><topic>Gene Expression Regulation, Neoplastic</topic><topic>Humans</topic><topic>Male</topic><topic>Neoplasm Grading</topic><topic>Observational Study</topic><topic>Prognosis</topic><topic>Prostatic Neoplasms - diagnosis</topic><topic>Prostatic Neoplasms - genetics</topic><topic>Prostatic Neoplasms - metabolism</topic><topic>Transcriptome - genetics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ma, Zhifang</creatorcontrib><creatorcontrib>Wang, Jianming</creatorcontrib><creatorcontrib>Ding, Lingyan</creatorcontrib><creatorcontrib>Chen, Yujun</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Medicine (Baltimore)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ma, Zhifang</au><au>Wang, Jianming</au><au>Ding, Lingyan</au><au>Chen, Yujun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of novel biomarkers correlated with prostate cancer progression by an integrated bioinformatic analysis</atitle><jtitle>Medicine (Baltimore)</jtitle><addtitle>Medicine (Baltimore)</addtitle><date>2020-07-10</date><risdate>2020</risdate><volume>99</volume><issue>28</issue><spage>e21158</spage><epage>e21158</epage><pages>e21158-e21158</pages><issn>0025-7974</issn><eissn>1536-5964</eissn><abstract>Prostate cancer (PCa) is a highly aggressive malignant tumor and the biological mechanisms underlying its progression remain unclear.We performed weighted gene co-expression network analysis in PCa dataset from the Cancer Genome Atlas database to identify the key module and key genes related to the progression of PCa. Furthermore, another independent datasets were used to validate our findings.A total of 744 differentially expressed genes were screened out and 5 modules were identified for PCa samples from the Cancer Genome Atlas database. We found the brown module was the key module and related to tumor grade (R2 = 0.52) and tumor invasion depth (R2 = 0.39). Besides, 24 candidate hub genes were screened out and 2 genes (BIRC5 and DEPDC1B) were identified and validated as real hub genes that associated with the progression and prognosis of PCa. Moreover, the biological roles of BIRC5 were related to G-protein coupled receptor signal pathway, and the functions of DEPDC1B were related to the G-protein coupled receptor signal pathway and retinol metabolism in PCa.Taken together, we identified 1 module, 24 candidate hub genes and 2 real hub genes, which were prominently associated with PCa progression. With more experiments and clinical trials, these genes may provide a promising future for PCa treatment.</abstract><cop>United States</cop><pub>the Author(s). Published by Wolters Kluwer Health, Inc</pub><pmid>32664150</pmid><doi>10.1097/MD.0000000000021158</doi><oa>free_for_read</oa></addata></record> |
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subjects | Biomarkers, Tumor - biosynthesis Biomarkers, Tumor - genetics Computational Biology - methods Disease Progression Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Male Neoplasm Grading Observational Study Prognosis Prostatic Neoplasms - diagnosis Prostatic Neoplasms - genetics Prostatic Neoplasms - metabolism Transcriptome - genetics |
title | Identification of novel biomarkers correlated with prostate cancer progression by an integrated bioinformatic analysis |
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