Identification of Novel Prognostic Signatures for Clear Cell Renal Cell Carcinoma Based on ceRNA Network Construction and Immune Infiltration Analysis
Objective. Clear cell renal cell carcinoma (ccRCC) carries significant morbidity and mortality globally and is often resistant to conventional radiotherapy and chemotherapy. Immune checkpoint blockade (ICB) has received attention in ccRCC patients as a promising anticancer treatment. Furthermore, co...
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| description | Objective. Clear cell renal cell carcinoma (ccRCC) carries significant morbidity and mortality globally and is often resistant to conventional radiotherapy and chemotherapy. Immune checkpoint blockade (ICB) has received attention in ccRCC patients as a promising anticancer treatment. Furthermore, competitive endogenous RNA (ceRNA) networks are crucial for the occurrence and progression of various tumors. This study was aimed at identifying reliable prognostic signatures and exploring potential mechanisms between ceRNA regulation and immune cell infiltration in ccRCC patients. Methods and Results. Gene expression profiling and clinical information of ccRCC samples were obtained from The Cancer Genome Atlas (TCGA) database. Through comprehensive bioinformatic analyses, differentially expressed mRNAs (DEmRNAs; n=131), lncRNAs (DElncRNAs; n=12), and miRNAs (DEmiRNAs; n=25) were identified to establish ceRNA networks. The CIBERSORT algorithm was applied to calculate the proportion of 22 types of tumor-infiltrating immune cells (TIICs) in ccRCC tissues. Subsequently, univariate Cox, Lasso, and multivariate Cox regression analyses were employed to construct ceRNA-related and TIIC-related prognostic signatures. In addition, we explored the relationship between the crucial genes and TIICs via coexpression analysis, which revealed that the interactions between MALAT1, miR-1271-5p, KIAA1324, and follicular helper T cells might be closely correlated with the progression of ccRCC. Ultimately, we preliminarily validated that the potential MALAT1/miR-1271-5p/KIAA1324 axis was consistent with the ceRNA theory by qRT-PCR in the ccRCC cell lines. Conclusion. On the basis of the ceRNA networks and TIICs, we constructed two prognostic signatures with excellent predictive value and explored possible molecular regulatory mechanisms, which might contribute to the improvement of prognosis and individualized treatment for ccRCC patients. |
| doi_str_mv | 10.1155/2022/4033583 |
| format | Article |
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Clear cell renal cell carcinoma (ccRCC) carries significant morbidity and mortality globally and is often resistant to conventional radiotherapy and chemotherapy. Immune checkpoint blockade (ICB) has received attention in ccRCC patients as a promising anticancer treatment. Furthermore, competitive endogenous RNA (ceRNA) networks are crucial for the occurrence and progression of various tumors. This study was aimed at identifying reliable prognostic signatures and exploring potential mechanisms between ceRNA regulation and immune cell infiltration in ccRCC patients. Methods and Results. Gene expression profiling and clinical information of ccRCC samples were obtained from The Cancer Genome Atlas (TCGA) database. Through comprehensive bioinformatic analyses, differentially expressed mRNAs (DEmRNAs; n=131), lncRNAs (DElncRNAs; n=12), and miRNAs (DEmiRNAs; n=25) were identified to establish ceRNA networks. The CIBERSORT algorithm was applied to calculate the proportion of 22 types of tumor-infiltrating immune cells (TIICs) in ccRCC tissues. Subsequently, univariate Cox, Lasso, and multivariate Cox regression analyses were employed to construct ceRNA-related and TIIC-related prognostic signatures. In addition, we explored the relationship between the crucial genes and TIICs via coexpression analysis, which revealed that the interactions between MALAT1, miR-1271-5p, KIAA1324, and follicular helper T cells might be closely correlated with the progression of ccRCC. Ultimately, we preliminarily validated that the potential MALAT1/miR-1271-5p/KIAA1324 axis was consistent with the ceRNA theory by qRT-PCR in the ccRCC cell lines. Conclusion. On the basis of the ceRNA networks and TIICs, we constructed two prognostic signatures with excellent predictive value and explored possible molecular regulatory mechanisms, which might contribute to the improvement of prognosis and individualized treatment for ccRCC patients.</description><identifier>ISSN: 0278-0240</identifier><identifier>EISSN: 1875-8630</identifier><identifier>DOI: 10.1155/2022/4033583</identifier><identifier>PMID: 35320950</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Algorithms ; Biomarkers, Tumor - analysis ; Carcinoma, Renal Cell - drug therapy ; Carcinoma, Renal Cell - immunology ; Chemotherapy ; Clear cell-type renal cell carcinoma ; Correlation analysis ; Disease Progression ; Gene expression ; Gene Expression Profiling ; Genomes ; Humans ; Immune checkpoint ; Immune Checkpoint Inhibitors - therapeutic use ; Immune system ; Immunotherapy ; Infiltration ; Kidney cancer ; Kidney Neoplasms - drug therapy ; Kidney Neoplasms - immunology ; Kinases ; Lymphocytes ; Lymphocytes T ; Medical prognosis ; Membrane Proteins - analysis ; Metastases ; MicroRNAs - analysis ; Morbidity ; Neoplasm Proteins - analysis ; Neoplastic Stem Cells - immunology ; Networks ; Patients ; Prognosis ; Radiation therapy ; Regression analysis ; Regulatory mechanisms (biology) ; RNA - analysis ; RNA, Long Noncoding - analysis ; RNA, Messenger - analysis ; Signal transduction ; Signatures ; Software ; Survival Analysis ; T Follicular Helper Cells - immunology ; Tumor-infiltrating lymphocytes ; Tumors</subject><ispartof>Disease markers, 2022, Vol.2022, p.4033583-28</ispartof><rights>Copyright © 2022 Lu Zhou et al.</rights><rights>Copyright © 2022 Lu Zhou et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2022 Lu Zhou et al. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c448t-1820d79461f0bb7905194b1e5eadb7f25c2bb86fadd55af449642dbf6554b5243</citedby><cites>FETCH-LOGICAL-c448t-1820d79461f0bb7905194b1e5eadb7f25c2bb86fadd55af449642dbf6554b5243</cites><orcidid>0000-0002-6704-2147</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8938059/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8938059/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,4010,27900,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35320950$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Hashmi, Atif Ali</contributor><creatorcontrib>Zhou, Lu</creatorcontrib><creatorcontrib>Ye, Juan</creatorcontrib><creatorcontrib>Wen, Fengyun</creatorcontrib><creatorcontrib>Yu, Hong</creatorcontrib><title>Identification of Novel Prognostic Signatures for Clear Cell Renal Cell Carcinoma Based on ceRNA Network Construction and Immune Infiltration Analysis</title><title>Disease markers</title><addtitle>Dis Markers</addtitle><description>Objective. Clear cell renal cell carcinoma (ccRCC) carries significant morbidity and mortality globally and is often resistant to conventional radiotherapy and chemotherapy. Immune checkpoint blockade (ICB) has received attention in ccRCC patients as a promising anticancer treatment. Furthermore, competitive endogenous RNA (ceRNA) networks are crucial for the occurrence and progression of various tumors. This study was aimed at identifying reliable prognostic signatures and exploring potential mechanisms between ceRNA regulation and immune cell infiltration in ccRCC patients. Methods and Results. Gene expression profiling and clinical information of ccRCC samples were obtained from The Cancer Genome Atlas (TCGA) database. Through comprehensive bioinformatic analyses, differentially expressed mRNAs (DEmRNAs; n=131), lncRNAs (DElncRNAs; n=12), and miRNAs (DEmiRNAs; n=25) were identified to establish ceRNA networks. The CIBERSORT algorithm was applied to calculate the proportion of 22 types of tumor-infiltrating immune cells (TIICs) in ccRCC tissues. Subsequently, univariate Cox, Lasso, and multivariate Cox regression analyses were employed to construct ceRNA-related and TIIC-related prognostic signatures. In addition, we explored the relationship between the crucial genes and TIICs via coexpression analysis, which revealed that the interactions between MALAT1, miR-1271-5p, KIAA1324, and follicular helper T cells might be closely correlated with the progression of ccRCC. Ultimately, we preliminarily validated that the potential MALAT1/miR-1271-5p/KIAA1324 axis was consistent with the ceRNA theory by qRT-PCR in the ccRCC cell lines. Conclusion. On the basis of the ceRNA networks and TIICs, we constructed two prognostic signatures with excellent predictive value and explored possible molecular regulatory mechanisms, which might contribute to the improvement of prognosis and individualized treatment for ccRCC patients.</description><subject>Algorithms</subject><subject>Biomarkers, Tumor - analysis</subject><subject>Carcinoma, Renal Cell - drug therapy</subject><subject>Carcinoma, Renal Cell - immunology</subject><subject>Chemotherapy</subject><subject>Clear cell-type renal cell carcinoma</subject><subject>Correlation analysis</subject><subject>Disease Progression</subject><subject>Gene expression</subject><subject>Gene Expression Profiling</subject><subject>Genomes</subject><subject>Humans</subject><subject>Immune checkpoint</subject><subject>Immune Checkpoint Inhibitors - therapeutic use</subject><subject>Immune system</subject><subject>Immunotherapy</subject><subject>Infiltration</subject><subject>Kidney cancer</subject><subject>Kidney Neoplasms - drug therapy</subject><subject>Kidney Neoplasms - immunology</subject><subject>Kinases</subject><subject>Lymphocytes</subject><subject>Lymphocytes T</subject><subject>Medical prognosis</subject><subject>Membrane Proteins - analysis</subject><subject>Metastases</subject><subject>MicroRNAs - analysis</subject><subject>Morbidity</subject><subject>Neoplasm Proteins - analysis</subject><subject>Neoplastic Stem Cells - immunology</subject><subject>Networks</subject><subject>Patients</subject><subject>Prognosis</subject><subject>Radiation therapy</subject><subject>Regression analysis</subject><subject>Regulatory mechanisms (biology)</subject><subject>RNA - analysis</subject><subject>RNA, Long Noncoding - analysis</subject><subject>RNA, Messenger - analysis</subject><subject>Signal transduction</subject><subject>Signatures</subject><subject>Software</subject><subject>Survival Analysis</subject><subject>T Follicular Helper Cells - immunology</subject><subject>Tumor-infiltrating lymphocytes</subject><subject>Tumors</subject><issn>0278-0240</issn><issn>1875-8630</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><recordid>eNp9kctu1DAYRiMEotPCjjWyxAaphPqa2BukIeIyUjWgAmvL8WXqktjFTlr1RXhe3GaogAUb25KPzn_5quoZgq8RYuwEQ4xPKCSEcfKgWiHespo3BD6sVhC3vIaYwoPqMOcLCBEWVDyuDggjGAoGV9XPjbFh8s5rNfkYQHRgG6_sAD6nuAsxT16DL34X1DQnm4GLCXSDVeW0wwDObFDD8uxU0j7EUYG3KlsDikvbs-0abO10HdN30MWQpzTruzIqGLAZxzlYsAnOD1Nayq-L7yb7_KR65NSQ7dP9fVR9e__ua_exPv30YdOtT2tNKZ9qxDE0raANcrDvWwEZErRHllll-tZhpnHf88YpYxhTjlLRUGx61zBGe4YpOareLN7LuR-t0WUXSQ3yMvlRpRsZlZd__wR_LnfxSnJBOGSiCF7uBSn-mG2e5OizLgtRwcY5S1wKci4QZQV98Q96EedUBr6jCMeIwKZQrxZKp5hzsu6-GQTlbeDyNnC5D7zgz_8c4B7-nXABjhfg3Aejrv3_db8AyqO02g</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Zhou, Lu</creator><creator>Ye, Juan</creator><creator>Wen, Fengyun</creator><creator>Yu, Hong</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</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>7QL</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-6704-2147</orcidid></search><sort><creationdate>2022</creationdate><title>Identification of Novel Prognostic Signatures for Clear Cell Renal Cell Carcinoma Based on ceRNA Network Construction and Immune Infiltration Analysis</title><author>Zhou, Lu ; Ye, Juan ; Wen, Fengyun ; Yu, Hong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c448t-1820d79461f0bb7905194b1e5eadb7f25c2bb86fadd55af449642dbf6554b5243</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Biomarkers, Tumor - analysis</topic><topic>Carcinoma, Renal Cell - drug therapy</topic><topic>Carcinoma, Renal Cell - immunology</topic><topic>Chemotherapy</topic><topic>Clear cell-type renal cell carcinoma</topic><topic>Correlation analysis</topic><topic>Disease Progression</topic><topic>Gene expression</topic><topic>Gene Expression Profiling</topic><topic>Genomes</topic><topic>Humans</topic><topic>Immune checkpoint</topic><topic>Immune Checkpoint Inhibitors - therapeutic use</topic><topic>Immune system</topic><topic>Immunotherapy</topic><topic>Infiltration</topic><topic>Kidney cancer</topic><topic>Kidney Neoplasms - drug therapy</topic><topic>Kidney Neoplasms - immunology</topic><topic>Kinases</topic><topic>Lymphocytes</topic><topic>Lymphocytes T</topic><topic>Medical prognosis</topic><topic>Membrane Proteins - analysis</topic><topic>Metastases</topic><topic>MicroRNAs - analysis</topic><topic>Morbidity</topic><topic>Neoplasm Proteins - analysis</topic><topic>Neoplastic Stem Cells - immunology</topic><topic>Networks</topic><topic>Patients</topic><topic>Prognosis</topic><topic>Radiation therapy</topic><topic>Regression analysis</topic><topic>Regulatory mechanisms (biology)</topic><topic>RNA - analysis</topic><topic>RNA, Long Noncoding - analysis</topic><topic>RNA, Messenger - analysis</topic><topic>Signal transduction</topic><topic>Signatures</topic><topic>Software</topic><topic>Survival Analysis</topic><topic>T Follicular Helper Cells - immunology</topic><topic>Tumor-infiltrating lymphocytes</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhou, Lu</creatorcontrib><creatorcontrib>Ye, Juan</creatorcontrib><creatorcontrib>Wen, Fengyun</creatorcontrib><creatorcontrib>Yu, Hong</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Disease markers</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhou, Lu</au><au>Ye, Juan</au><au>Wen, Fengyun</au><au>Yu, Hong</au><au>Hashmi, Atif Ali</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of Novel Prognostic Signatures for Clear Cell Renal Cell Carcinoma Based on ceRNA Network Construction and Immune Infiltration Analysis</atitle><jtitle>Disease markers</jtitle><addtitle>Dis Markers</addtitle><date>2022</date><risdate>2022</risdate><volume>2022</volume><spage>4033583</spage><epage>28</epage><pages>4033583-28</pages><issn>0278-0240</issn><eissn>1875-8630</eissn><abstract>Objective. Clear cell renal cell carcinoma (ccRCC) carries significant morbidity and mortality globally and is often resistant to conventional radiotherapy and chemotherapy. Immune checkpoint blockade (ICB) has received attention in ccRCC patients as a promising anticancer treatment. Furthermore, competitive endogenous RNA (ceRNA) networks are crucial for the occurrence and progression of various tumors. This study was aimed at identifying reliable prognostic signatures and exploring potential mechanisms between ceRNA regulation and immune cell infiltration in ccRCC patients. Methods and Results. Gene expression profiling and clinical information of ccRCC samples were obtained from The Cancer Genome Atlas (TCGA) database. Through comprehensive bioinformatic analyses, differentially expressed mRNAs (DEmRNAs; n=131), lncRNAs (DElncRNAs; n=12), and miRNAs (DEmiRNAs; n=25) were identified to establish ceRNA networks. The CIBERSORT algorithm was applied to calculate the proportion of 22 types of tumor-infiltrating immune cells (TIICs) in ccRCC tissues. Subsequently, univariate Cox, Lasso, and multivariate Cox regression analyses were employed to construct ceRNA-related and TIIC-related prognostic signatures. In addition, we explored the relationship between the crucial genes and TIICs via coexpression analysis, which revealed that the interactions between MALAT1, miR-1271-5p, KIAA1324, and follicular helper T cells might be closely correlated with the progression of ccRCC. Ultimately, we preliminarily validated that the potential MALAT1/miR-1271-5p/KIAA1324 axis was consistent with the ceRNA theory by qRT-PCR in the ccRCC cell lines. Conclusion. On the basis of the ceRNA networks and TIICs, we constructed two prognostic signatures with excellent predictive value and explored possible molecular regulatory mechanisms, which might contribute to the improvement of prognosis and individualized treatment for ccRCC patients.</abstract><cop>United States</cop><pub>Hindawi</pub><pmid>35320950</pmid><doi>10.1155/2022/4033583</doi><tpages>28</tpages><orcidid>https://orcid.org/0000-0002-6704-2147</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Biomarkers, Tumor - analysis Carcinoma, Renal Cell - drug therapy Carcinoma, Renal Cell - immunology Chemotherapy Clear cell-type renal cell carcinoma Correlation analysis Disease Progression Gene expression Gene Expression Profiling Genomes Humans Immune checkpoint Immune Checkpoint Inhibitors - therapeutic use Immune system Immunotherapy Infiltration Kidney cancer Kidney Neoplasms - drug therapy Kidney Neoplasms - immunology Kinases Lymphocytes Lymphocytes T Medical prognosis Membrane Proteins - analysis Metastases MicroRNAs - analysis Morbidity Neoplasm Proteins - analysis Neoplastic Stem Cells - immunology Networks Patients Prognosis Radiation therapy Regression analysis Regulatory mechanisms (biology) RNA - analysis RNA, Long Noncoding - analysis RNA, Messenger - analysis Signal transduction Signatures Software Survival Analysis T Follicular Helper Cells - immunology Tumor-infiltrating lymphocytes Tumors |
| title | Identification of Novel Prognostic Signatures for Clear Cell Renal Cell Carcinoma Based on ceRNA Network Construction and Immune Infiltration Analysis |
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