A novel coiled-coil domain containing-related gene signature for predicting prognosis and treatment effect of breast cancer

Purpose Breast cancer (BRCA) is a prevalent tumor worldwide. The association between the coiled-coil domain-containing (CCDC) protein family and different tumors has been established. However, the prognostic significance of this protein family in breast cancer remains uncertain. Methods Gene express...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of cancer research and clinical oncology 2023-11, Vol.149 (15), p.14205-14225
Hauptverfasser: Wang, Yufei, Wang, Yanmei, Zhou, Jia, Ying, Pingting, Wang, Zhuo, Wu, Yan, Hao, Minyan, Qiu, Shuying, Jin, Hongchuan, Wang, Xian
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 14225
container_issue 15
container_start_page 14205
container_title Journal of cancer research and clinical oncology
container_volume 149
creator Wang, Yufei
Wang, Yanmei
Zhou, Jia
Ying, Pingting
Wang, Zhuo
Wu, Yan
Hao, Minyan
Qiu, Shuying
Jin, Hongchuan
Wang, Xian
description Purpose Breast cancer (BRCA) is a prevalent tumor worldwide. The association between the coiled-coil domain-containing (CCDC) protein family and different tumors has been established. However, the prognostic significance of this protein family in breast cancer remains uncertain. Methods Gene expression and clinical data were obtained from the TCGA, METABRIC, and GEO databases. Prognosis genes were identified using univariate Cox and LASSO Cox regression, leading to the establishment of a prognostic signature. Subsequently, the risk model was conducted based on survival and clinical feature analyses, and a nomogram for prognosis prediction was developed. Furthermore, analyses of biological function, immune characteristics, and drug sensitivity were performed. Finally, single-cell sequencing data were utilized to uncover the expression patterns of genes in the risk model. Results Five genes were identified and utilized for risk modeling. The model demonstrated excellent prognostic value as indicated by ROC and Kaplan–Meier analysis. The high-risk group exhibited shorter survival time and higher likelihood of recurrence. Functional annotation indicated a correlation between the risk score and immune pathways. Conversely, the low-risk group displayed a greater enrichment in immune pathways and exhibited more active immune microenvironment characteristics. Additionally, drug sensitivity analysis using both public and our sequencing data revealed that the risk model possessed a broad range of predictive values. Conclusions We have developed a gene signature and have verified that patients with low-risk are more likely to have better prognosis and respond positively to therapy. This finding offers a valuable point of reference for BRCA individualized treatment.
doi_str_mv 10.1007/s00432-023-05222-y
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2848845611</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2879630450</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-22059f661448288f8d71d0ae7d9ee077bc9cb8cb52b752a1e04e4ba8665f6c5e3</originalsourceid><addsrcrecordid>eNp9kU9rHSEUxSU0JK9JvkAWReimGxt1xj9vGUKaBgLdtGtx9DpMmNFUncKjX76-viSFLrI6evzd64GD0CWjnxml6qpQ2necUN4RKjjnZHeENmxvsa4T79CGMsWI4EyeovelPNJ2F4qfoNNOCaGVlBv0-xrH9Atm7NI0gyd7wT4tdorNirXpFEeSYbYVPB4hAi7TGG1dM-CQMn7K4CdXG9WOaYypTAXb6HHNYOsCsWIIAVzFKeCheaViZ6ODfI6Og50LXDzrGfrx5fb7zVfy8O3u_ub6gbiWsxLOqdgGKVnfa6510F4xTy0ovwWgSg1u6wbtBsEHJbhlQHvoB6ulFEE6Ad0Z-nTY2_L9XKFUs0zFwTzbCGkthute615Ixhr68T_0Ma05tnSNUlvZ0V7QRvED5XIqJUMwT3labN4ZRs2-GnOoxrRqzN9qzK4NfXhevQ4L-NeRly4a0B2A0p7iCPnf32-s_QMb15tU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2879630450</pqid></control><display><type>article</type><title>A novel coiled-coil domain containing-related gene signature for predicting prognosis and treatment effect of breast cancer</title><source>Springer Online Journals</source><creator>Wang, Yufei ; Wang, Yanmei ; Zhou, Jia ; Ying, Pingting ; Wang, Zhuo ; Wu, Yan ; Hao, Minyan ; Qiu, Shuying ; Jin, Hongchuan ; Wang, Xian</creator><creatorcontrib>Wang, Yufei ; Wang, Yanmei ; Zhou, Jia ; Ying, Pingting ; Wang, Zhuo ; Wu, Yan ; Hao, Minyan ; Qiu, Shuying ; Jin, Hongchuan ; Wang, Xian</creatorcontrib><description>Purpose Breast cancer (BRCA) is a prevalent tumor worldwide. The association between the coiled-coil domain-containing (CCDC) protein family and different tumors has been established. However, the prognostic significance of this protein family in breast cancer remains uncertain. Methods Gene expression and clinical data were obtained from the TCGA, METABRIC, and GEO databases. Prognosis genes were identified using univariate Cox and LASSO Cox regression, leading to the establishment of a prognostic signature. Subsequently, the risk model was conducted based on survival and clinical feature analyses, and a nomogram for prognosis prediction was developed. Furthermore, analyses of biological function, immune characteristics, and drug sensitivity were performed. Finally, single-cell sequencing data were utilized to uncover the expression patterns of genes in the risk model. Results Five genes were identified and utilized for risk modeling. The model demonstrated excellent prognostic value as indicated by ROC and Kaplan–Meier analysis. The high-risk group exhibited shorter survival time and higher likelihood of recurrence. Functional annotation indicated a correlation between the risk score and immune pathways. Conversely, the low-risk group displayed a greater enrichment in immune pathways and exhibited more active immune microenvironment characteristics. Additionally, drug sensitivity analysis using both public and our sequencing data revealed that the risk model possessed a broad range of predictive values. Conclusions We have developed a gene signature and have verified that patients with low-risk are more likely to have better prognosis and respond positively to therapy. This finding offers a valuable point of reference for BRCA individualized treatment.</description><identifier>ISSN: 0171-5216</identifier><identifier>EISSN: 1432-1335</identifier><identifier>DOI: 10.1007/s00432-023-05222-y</identifier><identifier>PMID: 37558766</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Breast cancer ; Cancer Research ; Gene expression ; Genes ; Hematology ; Internal Medicine ; Medical prognosis ; Medicine ; Medicine &amp; Public Health ; Microenvironments ; Nomograms ; Oncology ; Prognosis ; Risk groups ; Sensitivity analysis</subject><ispartof>Journal of cancer research and clinical oncology, 2023-11, Vol.149 (15), p.14205-14225</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-22059f661448288f8d71d0ae7d9ee077bc9cb8cb52b752a1e04e4ba8665f6c5e3</citedby><cites>FETCH-LOGICAL-c375t-22059f661448288f8d71d0ae7d9ee077bc9cb8cb52b752a1e04e4ba8665f6c5e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00432-023-05222-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00432-023-05222-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37558766$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Yufei</creatorcontrib><creatorcontrib>Wang, Yanmei</creatorcontrib><creatorcontrib>Zhou, Jia</creatorcontrib><creatorcontrib>Ying, Pingting</creatorcontrib><creatorcontrib>Wang, Zhuo</creatorcontrib><creatorcontrib>Wu, Yan</creatorcontrib><creatorcontrib>Hao, Minyan</creatorcontrib><creatorcontrib>Qiu, Shuying</creatorcontrib><creatorcontrib>Jin, Hongchuan</creatorcontrib><creatorcontrib>Wang, Xian</creatorcontrib><title>A novel coiled-coil domain containing-related gene signature for predicting prognosis and treatment effect of breast cancer</title><title>Journal of cancer research and clinical oncology</title><addtitle>J Cancer Res Clin Oncol</addtitle><addtitle>J Cancer Res Clin Oncol</addtitle><description>Purpose Breast cancer (BRCA) is a prevalent tumor worldwide. The association between the coiled-coil domain-containing (CCDC) protein family and different tumors has been established. However, the prognostic significance of this protein family in breast cancer remains uncertain. Methods Gene expression and clinical data were obtained from the TCGA, METABRIC, and GEO databases. Prognosis genes were identified using univariate Cox and LASSO Cox regression, leading to the establishment of a prognostic signature. Subsequently, the risk model was conducted based on survival and clinical feature analyses, and a nomogram for prognosis prediction was developed. Furthermore, analyses of biological function, immune characteristics, and drug sensitivity were performed. Finally, single-cell sequencing data were utilized to uncover the expression patterns of genes in the risk model. Results Five genes were identified and utilized for risk modeling. The model demonstrated excellent prognostic value as indicated by ROC and Kaplan–Meier analysis. The high-risk group exhibited shorter survival time and higher likelihood of recurrence. Functional annotation indicated a correlation between the risk score and immune pathways. Conversely, the low-risk group displayed a greater enrichment in immune pathways and exhibited more active immune microenvironment characteristics. Additionally, drug sensitivity analysis using both public and our sequencing data revealed that the risk model possessed a broad range of predictive values. Conclusions We have developed a gene signature and have verified that patients with low-risk are more likely to have better prognosis and respond positively to therapy. This finding offers a valuable point of reference for BRCA individualized treatment.</description><subject>Breast cancer</subject><subject>Cancer Research</subject><subject>Gene expression</subject><subject>Genes</subject><subject>Hematology</subject><subject>Internal Medicine</subject><subject>Medical prognosis</subject><subject>Medicine</subject><subject>Medicine &amp; Public Health</subject><subject>Microenvironments</subject><subject>Nomograms</subject><subject>Oncology</subject><subject>Prognosis</subject><subject>Risk groups</subject><subject>Sensitivity analysis</subject><issn>0171-5216</issn><issn>1432-1335</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kU9rHSEUxSU0JK9JvkAWReimGxt1xj9vGUKaBgLdtGtx9DpMmNFUncKjX76-viSFLrI6evzd64GD0CWjnxml6qpQ2necUN4RKjjnZHeENmxvsa4T79CGMsWI4EyeovelPNJ2F4qfoNNOCaGVlBv0-xrH9Atm7NI0gyd7wT4tdorNirXpFEeSYbYVPB4hAi7TGG1dM-CQMn7K4CdXG9WOaYypTAXb6HHNYOsCsWIIAVzFKeCheaViZ6ODfI6Og50LXDzrGfrx5fb7zVfy8O3u_ub6gbiWsxLOqdgGKVnfa6510F4xTy0ovwWgSg1u6wbtBsEHJbhlQHvoB6ulFEE6Ad0Z-nTY2_L9XKFUs0zFwTzbCGkthute615Ixhr68T_0Ma05tnSNUlvZ0V7QRvED5XIqJUMwT3labN4ZRs2-GnOoxrRqzN9qzK4NfXhevQ4L-NeRly4a0B2A0p7iCPnf32-s_QMb15tU</recordid><startdate>20231101</startdate><enddate>20231101</enddate><creator>Wang, Yufei</creator><creator>Wang, Yanmei</creator><creator>Zhou, Jia</creator><creator>Ying, Pingting</creator><creator>Wang, Zhuo</creator><creator>Wu, Yan</creator><creator>Hao, Minyan</creator><creator>Qiu, Shuying</creator><creator>Jin, Hongchuan</creator><creator>Wang, Xian</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TO</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20231101</creationdate><title>A novel coiled-coil domain containing-related gene signature for predicting prognosis and treatment effect of breast cancer</title><author>Wang, Yufei ; Wang, Yanmei ; Zhou, Jia ; Ying, Pingting ; Wang, Zhuo ; Wu, Yan ; Hao, Minyan ; Qiu, Shuying ; Jin, Hongchuan ; Wang, Xian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-22059f661448288f8d71d0ae7d9ee077bc9cb8cb52b752a1e04e4ba8665f6c5e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Breast cancer</topic><topic>Cancer Research</topic><topic>Gene expression</topic><topic>Genes</topic><topic>Hematology</topic><topic>Internal Medicine</topic><topic>Medical prognosis</topic><topic>Medicine</topic><topic>Medicine &amp; Public Health</topic><topic>Microenvironments</topic><topic>Nomograms</topic><topic>Oncology</topic><topic>Prognosis</topic><topic>Risk groups</topic><topic>Sensitivity analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Yufei</creatorcontrib><creatorcontrib>Wang, Yanmei</creatorcontrib><creatorcontrib>Zhou, Jia</creatorcontrib><creatorcontrib>Ying, Pingting</creatorcontrib><creatorcontrib>Wang, Zhuo</creatorcontrib><creatorcontrib>Wu, Yan</creatorcontrib><creatorcontrib>Hao, Minyan</creatorcontrib><creatorcontrib>Qiu, Shuying</creatorcontrib><creatorcontrib>Jin, Hongchuan</creatorcontrib><creatorcontrib>Wang, Xian</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>ProQuest Health and Medical</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest Research Library</collection><collection>Research Library (Corporate)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of cancer research and clinical oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Yufei</au><au>Wang, Yanmei</au><au>Zhou, Jia</au><au>Ying, Pingting</au><au>Wang, Zhuo</au><au>Wu, Yan</au><au>Hao, Minyan</au><au>Qiu, Shuying</au><au>Jin, Hongchuan</au><au>Wang, Xian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A novel coiled-coil domain containing-related gene signature for predicting prognosis and treatment effect of breast cancer</atitle><jtitle>Journal of cancer research and clinical oncology</jtitle><stitle>J Cancer Res Clin Oncol</stitle><addtitle>J Cancer Res Clin Oncol</addtitle><date>2023-11-01</date><risdate>2023</risdate><volume>149</volume><issue>15</issue><spage>14205</spage><epage>14225</epage><pages>14205-14225</pages><issn>0171-5216</issn><eissn>1432-1335</eissn><abstract>Purpose Breast cancer (BRCA) is a prevalent tumor worldwide. The association between the coiled-coil domain-containing (CCDC) protein family and different tumors has been established. However, the prognostic significance of this protein family in breast cancer remains uncertain. Methods Gene expression and clinical data were obtained from the TCGA, METABRIC, and GEO databases. Prognosis genes were identified using univariate Cox and LASSO Cox regression, leading to the establishment of a prognostic signature. Subsequently, the risk model was conducted based on survival and clinical feature analyses, and a nomogram for prognosis prediction was developed. Furthermore, analyses of biological function, immune characteristics, and drug sensitivity were performed. Finally, single-cell sequencing data were utilized to uncover the expression patterns of genes in the risk model. Results Five genes were identified and utilized for risk modeling. The model demonstrated excellent prognostic value as indicated by ROC and Kaplan–Meier analysis. The high-risk group exhibited shorter survival time and higher likelihood of recurrence. Functional annotation indicated a correlation between the risk score and immune pathways. Conversely, the low-risk group displayed a greater enrichment in immune pathways and exhibited more active immune microenvironment characteristics. Additionally, drug sensitivity analysis using both public and our sequencing data revealed that the risk model possessed a broad range of predictive values. Conclusions We have developed a gene signature and have verified that patients with low-risk are more likely to have better prognosis and respond positively to therapy. This finding offers a valuable point of reference for BRCA individualized treatment.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>37558766</pmid><doi>10.1007/s00432-023-05222-y</doi><tpages>21</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0171-5216
ispartof Journal of cancer research and clinical oncology, 2023-11, Vol.149 (15), p.14205-14225
issn 0171-5216
1432-1335
language eng
recordid cdi_proquest_miscellaneous_2848845611
source Springer Online Journals
subjects Breast cancer
Cancer Research
Gene expression
Genes
Hematology
Internal Medicine
Medical prognosis
Medicine
Medicine & Public Health
Microenvironments
Nomograms
Oncology
Prognosis
Risk groups
Sensitivity analysis
title A novel coiled-coil domain containing-related gene signature for predicting prognosis and treatment effect of breast cancer
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T12%3A49%3A43IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20novel%20coiled-coil%20domain%20containing-related%20gene%20signature%20for%20predicting%20prognosis%20and%20treatment%20effect%20of%20breast%20cancer&rft.jtitle=Journal%20of%20cancer%20research%20and%20clinical%20oncology&rft.au=Wang,%20Yufei&rft.date=2023-11-01&rft.volume=149&rft.issue=15&rft.spage=14205&rft.epage=14225&rft.pages=14205-14225&rft.issn=0171-5216&rft.eissn=1432-1335&rft_id=info:doi/10.1007/s00432-023-05222-y&rft_dat=%3Cproquest_cross%3E2879630450%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2879630450&rft_id=info:pmid/37558766&rfr_iscdi=true