Upregulation of CCNB2 and a novel lncRNAs-related risk model predict prognosis in clear cell renal cell carcinoma
Background Clear cell renal cell carcinoma (ccRCC) is the main type of renal cell carcinoma. Cyclin B2 (CCNB2) is a subtype of B-type cyclin that is associated with the prognosis of several cancers. This study aimed to identify the relationship between CCNB2 and progression of ccRCC and construct a...
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| Veröffentlicht in: | Journal of cancer research and clinical oncology 2024-02, Vol.150 (2), p.64-64, Article 64 |
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| creator | Ren, Congzhe Wang, Qihua Xu, Zhunan Pan, Yang Wang, Shangren Liu, Xiaoqiang |
| description | Background
Clear cell renal cell carcinoma (ccRCC) is the main type of renal cell carcinoma. Cyclin B2 (CCNB2) is a subtype of B-type cyclin that is associated with the prognosis of several cancers. This study aimed to identify the relationship between CCNB2 and progression of ccRCC and construct a novel lncRNAs-related model to predict prognosis of ccRCC patients.
Methods
The data were obtained from public databases. We identified CCNB2 in ccRCC using Kaplan–Meier survival analysis, univariate and multivariate Cox regression, and Gene Ontology analysis. External validation was then performed. The risk model was constructed based on prognostic lncRNAs by the LASSO algorithm and multivariate Cox regression. Receiver operating characteristics (ROC) curves were used to evaluate the model. Consensus clustering analysis was performed to re-stratify the patients. Finally, we analyzed the tumor-immune microenvironment and performed screening of potential drugs.
Results
CCNB2 associated with late clinicopathological parameters and poor prognosis in ccRCC and was an independent predictor for disease-free survival. In addition, CCNB2 shared the same expression pattern with known suppressive immune checkpoints. A risk model dependent on the expression of three prognostic CCNB2-related lncRNAs (SNHG17, VPS9D1-AS1, and ZMIZ1-AS1) was constructed. The risk signature was an independent predictor of ccRCC. The area under the ROC (AUC) curve for overall survival at 1-, 3-, 5-, and 8-year was 0.704, 0.702, 0.741, and 0.763. The high-risk group and cluster 2 had stronger immunogenicity and were more sensitive to immunotherapy.
Conclusion
CCNB2 could be an important biomarker for predicting prognosis in ccRCC patients. Furthermore, we developed a novel lncRNAs-related risk model and identified two CCNB2-related molecular clusters. The risk model performed well in predicting overall survival and immunological microenvironment of ccRCC. |
| doi_str_mv | 10.1007/s00432-024-05611-x |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2921119281</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2921390925</sourcerecordid><originalsourceid>FETCH-LOGICAL-c403t-9db1f264383514465ecad19ecb1374f96abcf1fd1094c6e6dc7309509eaeab503</originalsourceid><addsrcrecordid>eNqFkUtv1TAQhS0EoqXwB1ggS2y6CZ3xI6mX5YqXVBUJ0bXl2JOrlMRu7RtU_n19SXmIRVnNyPOdmWMdxl4ivEGA7qQAKCkaEKoB3SI2t4_YIe6fUEr9-K_-gD0r5QoAO92Jp-xAnkoAKeGQ3VxeZ9ouk9uNKfI08M3m4q3gLgbueEzfaeJT9F8uzkqTqVIUeB7LNz6nUEdVG0a_qzVtYypj4WPkfiKXuadp4pmim9bWu-zHmGb3nD0Z3FToxX09Ypfv333dfGzOP3_4tDk7b7wCuWtM6HEQrapWNSrVavIuoCHfo-zUYFrX-wGHgGCUb6kNvpNgNBhy5HoN8ogdr3uruZuFys7OY9lbcZHSUqxELVtQnfg_KoxARCNOsaKv_0Gv0pLrL1dKGjBCV0qslM-plEyDvc7j7PIPi2D32dk1O1uzsz-zs7dV9Op-9dLPFH5LfoVVAbkCpY7ilvKf2w-svQOmiaO1</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2921390925</pqid></control><display><type>article</type><title>Upregulation of CCNB2 and a novel lncRNAs-related risk model predict prognosis in clear cell renal cell carcinoma</title><source>MEDLINE</source><source>Springer Nature - Complete Springer Journals</source><source>DOAJ Directory of Open Access Journals</source><source>Springer Nature OA Free Journals</source><creator>Ren, Congzhe ; Wang, Qihua ; Xu, Zhunan ; Pan, Yang ; Wang, Shangren ; Liu, Xiaoqiang</creator><creatorcontrib>Ren, Congzhe ; Wang, Qihua ; Xu, Zhunan ; Pan, Yang ; Wang, Shangren ; Liu, Xiaoqiang</creatorcontrib><description>Background
Clear cell renal cell carcinoma (ccRCC) is the main type of renal cell carcinoma. Cyclin B2 (CCNB2) is a subtype of B-type cyclin that is associated with the prognosis of several cancers. This study aimed to identify the relationship between CCNB2 and progression of ccRCC and construct a novel lncRNAs-related model to predict prognosis of ccRCC patients.
Methods
The data were obtained from public databases. We identified CCNB2 in ccRCC using Kaplan–Meier survival analysis, univariate and multivariate Cox regression, and Gene Ontology analysis. External validation was then performed. The risk model was constructed based on prognostic lncRNAs by the LASSO algorithm and multivariate Cox regression. Receiver operating characteristics (ROC) curves were used to evaluate the model. Consensus clustering analysis was performed to re-stratify the patients. Finally, we analyzed the tumor-immune microenvironment and performed screening of potential drugs.
Results
CCNB2 associated with late clinicopathological parameters and poor prognosis in ccRCC and was an independent predictor for disease-free survival. In addition, CCNB2 shared the same expression pattern with known suppressive immune checkpoints. A risk model dependent on the expression of three prognostic CCNB2-related lncRNAs (SNHG17, VPS9D1-AS1, and ZMIZ1-AS1) was constructed. The risk signature was an independent predictor of ccRCC. The area under the ROC (AUC) curve for overall survival at 1-, 3-, 5-, and 8-year was 0.704, 0.702, 0.741, and 0.763. The high-risk group and cluster 2 had stronger immunogenicity and were more sensitive to immunotherapy.
Conclusion
CCNB2 could be an important biomarker for predicting prognosis in ccRCC patients. Furthermore, we developed a novel lncRNAs-related risk model and identified two CCNB2-related molecular clusters. The risk model performed well in predicting overall survival and immunological microenvironment of ccRCC.</description><identifier>ISSN: 1432-1335</identifier><identifier>ISSN: 0171-5216</identifier><identifier>EISSN: 1432-1335</identifier><identifier>DOI: 10.1007/s00432-024-05611-x</identifier><identifier>PMID: 38300330</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>algorithms ; biomarkers ; Cancer Research ; Carcinoma ; Carcinoma, Renal Cell - genetics ; Clear cell-type renal cell carcinoma ; Cyclin B2 - genetics ; cyclins ; Drug screening ; gene ontology ; Hematology ; Humans ; Immune checkpoint ; Immunogenicity ; Immunosuppressive agents ; Immunotherapy ; Internal Medicine ; Kidney cancer ; Kidney Neoplasms - genetics ; Medical prognosis ; Medicine ; Medicine & Public Health ; Microenvironments ; Non-coding RNA ; Oncology ; Prognosis ; regression analysis ; renal cell carcinoma ; risk ; Risk groups ; RNA, Long Noncoding - genetics ; Survival analysis ; Tumor Microenvironment ; Up-Regulation</subject><ispartof>Journal of cancer research and clinical oncology, 2024-02, Vol.150 (2), p.64-64, Article 64</ispartof><rights>The Author(s) 2024</rights><rights>2024. The Author(s).</rights><rights>The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c403t-9db1f264383514465ecad19ecb1374f96abcf1fd1094c6e6dc7309509eaeab503</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/s00432-024-05611-x$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://doi.org/10.1007/s00432-024-05611-x$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,27901,27902,41096,41464,42165,42533,51294,51551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38300330$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ren, Congzhe</creatorcontrib><creatorcontrib>Wang, Qihua</creatorcontrib><creatorcontrib>Xu, Zhunan</creatorcontrib><creatorcontrib>Pan, Yang</creatorcontrib><creatorcontrib>Wang, Shangren</creatorcontrib><creatorcontrib>Liu, Xiaoqiang</creatorcontrib><title>Upregulation of CCNB2 and a novel lncRNAs-related risk model predict prognosis in clear cell renal cell carcinoma</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>Background
Clear cell renal cell carcinoma (ccRCC) is the main type of renal cell carcinoma. Cyclin B2 (CCNB2) is a subtype of B-type cyclin that is associated with the prognosis of several cancers. This study aimed to identify the relationship between CCNB2 and progression of ccRCC and construct a novel lncRNAs-related model to predict prognosis of ccRCC patients.
Methods
The data were obtained from public databases. We identified CCNB2 in ccRCC using Kaplan–Meier survival analysis, univariate and multivariate Cox regression, and Gene Ontology analysis. External validation was then performed. The risk model was constructed based on prognostic lncRNAs by the LASSO algorithm and multivariate Cox regression. Receiver operating characteristics (ROC) curves were used to evaluate the model. Consensus clustering analysis was performed to re-stratify the patients. Finally, we analyzed the tumor-immune microenvironment and performed screening of potential drugs.
Results
CCNB2 associated with late clinicopathological parameters and poor prognosis in ccRCC and was an independent predictor for disease-free survival. In addition, CCNB2 shared the same expression pattern with known suppressive immune checkpoints. A risk model dependent on the expression of three prognostic CCNB2-related lncRNAs (SNHG17, VPS9D1-AS1, and ZMIZ1-AS1) was constructed. The risk signature was an independent predictor of ccRCC. The area under the ROC (AUC) curve for overall survival at 1-, 3-, 5-, and 8-year was 0.704, 0.702, 0.741, and 0.763. The high-risk group and cluster 2 had stronger immunogenicity and were more sensitive to immunotherapy.
Conclusion
CCNB2 could be an important biomarker for predicting prognosis in ccRCC patients. Furthermore, we developed a novel lncRNAs-related risk model and identified two CCNB2-related molecular clusters. The risk model performed well in predicting overall survival and immunological microenvironment of ccRCC.</description><subject>algorithms</subject><subject>biomarkers</subject><subject>Cancer Research</subject><subject>Carcinoma</subject><subject>Carcinoma, Renal Cell - genetics</subject><subject>Clear cell-type renal cell carcinoma</subject><subject>Cyclin B2 - genetics</subject><subject>cyclins</subject><subject>Drug screening</subject><subject>gene ontology</subject><subject>Hematology</subject><subject>Humans</subject><subject>Immune checkpoint</subject><subject>Immunogenicity</subject><subject>Immunosuppressive agents</subject><subject>Immunotherapy</subject><subject>Internal Medicine</subject><subject>Kidney cancer</subject><subject>Kidney Neoplasms - genetics</subject><subject>Medical prognosis</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Microenvironments</subject><subject>Non-coding RNA</subject><subject>Oncology</subject><subject>Prognosis</subject><subject>regression analysis</subject><subject>renal cell carcinoma</subject><subject>risk</subject><subject>Risk groups</subject><subject>RNA, Long Noncoding - genetics</subject><subject>Survival analysis</subject><subject>Tumor Microenvironment</subject><subject>Up-Regulation</subject><issn>1432-1335</issn><issn>0171-5216</issn><issn>1432-1335</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><recordid>eNqFkUtv1TAQhS0EoqXwB1ggS2y6CZ3xI6mX5YqXVBUJ0bXl2JOrlMRu7RtU_n19SXmIRVnNyPOdmWMdxl4ivEGA7qQAKCkaEKoB3SI2t4_YIe6fUEr9-K_-gD0r5QoAO92Jp-xAnkoAKeGQ3VxeZ9ouk9uNKfI08M3m4q3gLgbueEzfaeJT9F8uzkqTqVIUeB7LNz6nUEdVG0a_qzVtYypj4WPkfiKXuadp4pmim9bWu-zHmGb3nD0Z3FToxX09Ypfv333dfGzOP3_4tDk7b7wCuWtM6HEQrapWNSrVavIuoCHfo-zUYFrX-wGHgGCUb6kNvpNgNBhy5HoN8ogdr3uruZuFys7OY9lbcZHSUqxELVtQnfg_KoxARCNOsaKv_0Gv0pLrL1dKGjBCV0qslM-plEyDvc7j7PIPi2D32dk1O1uzsz-zs7dV9Op-9dLPFH5LfoVVAbkCpY7ilvKf2w-svQOmiaO1</recordid><startdate>20240201</startdate><enddate>20240201</enddate><creator>Ren, Congzhe</creator><creator>Wang, Qihua</creator><creator>Xu, Zhunan</creator><creator>Pan, Yang</creator><creator>Wang, Shangren</creator><creator>Liu, Xiaoqiang</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>C6C</scope><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>7TO</scope><scope>H94</scope><scope>K9.</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope></search><sort><creationdate>20240201</creationdate><title>Upregulation of CCNB2 and a novel lncRNAs-related risk model predict prognosis in clear cell renal cell carcinoma</title><author>Ren, Congzhe ; Wang, Qihua ; Xu, Zhunan ; Pan, Yang ; Wang, Shangren ; Liu, Xiaoqiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c403t-9db1f264383514465ecad19ecb1374f96abcf1fd1094c6e6dc7309509eaeab503</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>algorithms</topic><topic>biomarkers</topic><topic>Cancer Research</topic><topic>Carcinoma</topic><topic>Carcinoma, Renal Cell - genetics</topic><topic>Clear cell-type renal cell carcinoma</topic><topic>Cyclin B2 - genetics</topic><topic>cyclins</topic><topic>Drug screening</topic><topic>gene ontology</topic><topic>Hematology</topic><topic>Humans</topic><topic>Immune checkpoint</topic><topic>Immunogenicity</topic><topic>Immunosuppressive agents</topic><topic>Immunotherapy</topic><topic>Internal Medicine</topic><topic>Kidney cancer</topic><topic>Kidney Neoplasms - genetics</topic><topic>Medical prognosis</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Microenvironments</topic><topic>Non-coding RNA</topic><topic>Oncology</topic><topic>Prognosis</topic><topic>regression analysis</topic><topic>renal cell carcinoma</topic><topic>risk</topic><topic>Risk groups</topic><topic>RNA, Long Noncoding - genetics</topic><topic>Survival analysis</topic><topic>Tumor Microenvironment</topic><topic>Up-Regulation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ren, Congzhe</creatorcontrib><creatorcontrib>Wang, Qihua</creatorcontrib><creatorcontrib>Xu, Zhunan</creatorcontrib><creatorcontrib>Pan, Yang</creatorcontrib><creatorcontrib>Wang, Shangren</creatorcontrib><creatorcontrib>Liu, Xiaoqiang</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Journal of cancer research and clinical oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ren, Congzhe</au><au>Wang, Qihua</au><au>Xu, Zhunan</au><au>Pan, Yang</au><au>Wang, Shangren</au><au>Liu, Xiaoqiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Upregulation of CCNB2 and a novel lncRNAs-related risk model predict prognosis in clear cell renal cell carcinoma</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>2024-02-01</date><risdate>2024</risdate><volume>150</volume><issue>2</issue><spage>64</spage><epage>64</epage><pages>64-64</pages><artnum>64</artnum><issn>1432-1335</issn><issn>0171-5216</issn><eissn>1432-1335</eissn><abstract>Background
Clear cell renal cell carcinoma (ccRCC) is the main type of renal cell carcinoma. Cyclin B2 (CCNB2) is a subtype of B-type cyclin that is associated with the prognosis of several cancers. This study aimed to identify the relationship between CCNB2 and progression of ccRCC and construct a novel lncRNAs-related model to predict prognosis of ccRCC patients.
Methods
The data were obtained from public databases. We identified CCNB2 in ccRCC using Kaplan–Meier survival analysis, univariate and multivariate Cox regression, and Gene Ontology analysis. External validation was then performed. The risk model was constructed based on prognostic lncRNAs by the LASSO algorithm and multivariate Cox regression. Receiver operating characteristics (ROC) curves were used to evaluate the model. Consensus clustering analysis was performed to re-stratify the patients. Finally, we analyzed the tumor-immune microenvironment and performed screening of potential drugs.
Results
CCNB2 associated with late clinicopathological parameters and poor prognosis in ccRCC and was an independent predictor for disease-free survival. In addition, CCNB2 shared the same expression pattern with known suppressive immune checkpoints. A risk model dependent on the expression of three prognostic CCNB2-related lncRNAs (SNHG17, VPS9D1-AS1, and ZMIZ1-AS1) was constructed. The risk signature was an independent predictor of ccRCC. The area under the ROC (AUC) curve for overall survival at 1-, 3-, 5-, and 8-year was 0.704, 0.702, 0.741, and 0.763. The high-risk group and cluster 2 had stronger immunogenicity and were more sensitive to immunotherapy.
Conclusion
CCNB2 could be an important biomarker for predicting prognosis in ccRCC patients. Furthermore, we developed a novel lncRNAs-related risk model and identified two CCNB2-related molecular clusters. The risk model performed well in predicting overall survival and immunological microenvironment of ccRCC.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>38300330</pmid><doi>10.1007/s00432-024-05611-x</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | algorithms biomarkers Cancer Research Carcinoma Carcinoma, Renal Cell - genetics Clear cell-type renal cell carcinoma Cyclin B2 - genetics cyclins Drug screening gene ontology Hematology Humans Immune checkpoint Immunogenicity Immunosuppressive agents Immunotherapy Internal Medicine Kidney cancer Kidney Neoplasms - genetics Medical prognosis Medicine Medicine & Public Health Microenvironments Non-coding RNA Oncology Prognosis regression analysis renal cell carcinoma risk Risk groups RNA, Long Noncoding - genetics Survival analysis Tumor Microenvironment Up-Regulation |
| title | Upregulation of CCNB2 and a novel lncRNAs-related risk model predict prognosis in clear cell renal cell carcinoma |
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