Construction and validation of a novel cuproptosis-related long noncoding RNA signature for predicting the outcome of prostate cancer
Background: Prostate cancer (PCa) is one of the most common tumors of the urinary system. Cuproptosis is a novel mode of controlled cell death that is related to the development of various tumor types. However, the functions of cuproptosis-related long noncoding RNAs (CRLs) in PCa have not yet been...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.976850/full |
| _version_ | 1811179234988654592 |
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| author | Shaoqin Jiang Shaoqin Jiang Zhihao Li Ruiling Dou Zequn Lin Jili Zhang Wenhui Zhang Zeyu Chen Xianqi Shen Jin Ji Min Qu Yan Wang Mengqiang Li Xu Gao |
| author_facet | Shaoqin Jiang Shaoqin Jiang Zhihao Li Ruiling Dou Zequn Lin Jili Zhang Wenhui Zhang Zeyu Chen Xianqi Shen Jin Ji Min Qu Yan Wang Mengqiang Li Xu Gao |
| author_sort | Shaoqin Jiang |
| collection | DOAJ |
| description | Background: Prostate cancer (PCa) is one of the most common tumors of the urinary system. Cuproptosis is a novel mode of controlled cell death that is related to the development of various tumor types. However, the functions of cuproptosis-related long noncoding RNAs (CRLs) in PCa have not yet been well studied.Methods: In this study, data of PCa patients were obtained from The Cancer Genome Atlas (TCGA) and from the Changhai Hospital. Univariate and multivariate Cox regression analyses and LASSO regression analysis were conducted to screen CRLs linked to the prognosis of PCa patients. A risk score model was constructed on the basis of CRLs to predict prognosis. PCa patients were categorized into high- and low-risk cohorts. The predictive value of the risk score was evaluated by Kaplan–Meier survival analysis, receiver operating characteristic curves, and nomograms. In addition, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore possible pathways involving CRLs in PCa. Immune function analysis confirmed the correlation between CRLs and immunity in PCa. Finally, we explored the tumor mutational burden and drug response in the high- and low-risk cohorts.Results: First, we identified seven CRLs (C1orf229, C9orf139, LIPE-AS1, MCPH1-AS1, PRR26, SGMS1-AS1, and SNHG1) that were closely related to prognosis in PCa. The risk score model based on the selected CRLs could accurately predict the prognosis of PCa patients. The high-risk cohort was associated with worse disease-free survival (DFS) time in PCa patients (p < 0.001). ROC curve analysis was performed to confirm the validity of the signature (area under the curve (AUC) at 1 year: 0.703). Nomograms were constructed based on the risk score and clinicopathological features and also exhibited great predictive efficiency for PCa. GO and KEGG analyses showed that the CRLs were mainly enriched in metabolism-related biological pathways in PCa. In addition, immune function analysis showed that patients in the high-risk cohort had higher TMB and were more sensitive to conventional chemotherapy and targeted drugs including doxorubicin, epothilone B, etoposide, and mitomycin C.Conclusion: We constructed a novel CRL-related risk score model to accurately predict the prognosis of PCa patients. Our results indicate that CRLs are potential targets for drug therapy in PCa and provide a possible new direction for personalized treatment of PCa patients. |
| first_indexed | 2024-04-11T06:32:28Z |
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| id | doaj.art-30ff4af0ec6a463c92521e07855aff54 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-11T06:32:28Z |
| publishDate | 2022-12-01 |
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| spelling | doaj.art-30ff4af0ec6a463c92521e07855aff542022-12-22T04:39:59ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-12-011310.3389/fgene.2022.976850976850Construction and validation of a novel cuproptosis-related long noncoding RNA signature for predicting the outcome of prostate cancerShaoqin Jiang0Shaoqin Jiang1Zhihao Li2Ruiling Dou3Zequn Lin4Jili Zhang5Wenhui Zhang6Zeyu Chen7Xianqi Shen8Jin Ji9Min Qu10Yan Wang11Mengqiang Li12Xu Gao13Department of Urology, Changhai Hospital, Second Military Medical University, Shanghai, ChinaDepartment of Urology, Fujian Union Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Urology, Fujian Union Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Urology, Fujian Union Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Urology, Fujian Union Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Urology, Changhai Hospital, Second Military Medical University, Shanghai, ChinaDepartment of Urology, Changhai Hospital, Second Military Medical University, Shanghai, ChinaDepartment of Urology, Changhai Hospital, Second Military Medical University, Shanghai, ChinaDepartment of Urology, Changhai Hospital, Second Military Medical University, Shanghai, ChinaDepartment of Urology, Changhai Hospital, Second Military Medical University, Shanghai, ChinaDepartment of Urology, Changhai Hospital, Second Military Medical University, Shanghai, ChinaDepartment of Urology, Changhai Hospital, Second Military Medical University, Shanghai, ChinaDepartment of Urology, Fujian Union Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Urology, Changhai Hospital, Second Military Medical University, Shanghai, ChinaBackground: Prostate cancer (PCa) is one of the most common tumors of the urinary system. Cuproptosis is a novel mode of controlled cell death that is related to the development of various tumor types. However, the functions of cuproptosis-related long noncoding RNAs (CRLs) in PCa have not yet been well studied.Methods: In this study, data of PCa patients were obtained from The Cancer Genome Atlas (TCGA) and from the Changhai Hospital. Univariate and multivariate Cox regression analyses and LASSO regression analysis were conducted to screen CRLs linked to the prognosis of PCa patients. A risk score model was constructed on the basis of CRLs to predict prognosis. PCa patients were categorized into high- and low-risk cohorts. The predictive value of the risk score was evaluated by Kaplan–Meier survival analysis, receiver operating characteristic curves, and nomograms. In addition, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore possible pathways involving CRLs in PCa. Immune function analysis confirmed the correlation between CRLs and immunity in PCa. Finally, we explored the tumor mutational burden and drug response in the high- and low-risk cohorts.Results: First, we identified seven CRLs (C1orf229, C9orf139, LIPE-AS1, MCPH1-AS1, PRR26, SGMS1-AS1, and SNHG1) that were closely related to prognosis in PCa. The risk score model based on the selected CRLs could accurately predict the prognosis of PCa patients. The high-risk cohort was associated with worse disease-free survival (DFS) time in PCa patients (p < 0.001). ROC curve analysis was performed to confirm the validity of the signature (area under the curve (AUC) at 1 year: 0.703). Nomograms were constructed based on the risk score and clinicopathological features and also exhibited great predictive efficiency for PCa. GO and KEGG analyses showed that the CRLs were mainly enriched in metabolism-related biological pathways in PCa. In addition, immune function analysis showed that patients in the high-risk cohort had higher TMB and were more sensitive to conventional chemotherapy and targeted drugs including doxorubicin, epothilone B, etoposide, and mitomycin C.Conclusion: We constructed a novel CRL-related risk score model to accurately predict the prognosis of PCa patients. Our results indicate that CRLs are potential targets for drug therapy in PCa and provide a possible new direction for personalized treatment of PCa patients.https://www.frontiersin.org/articles/10.3389/fgene.2022.976850/fullcuproptosisprostate cancerlong noncoding RNA signatureprognosis modelnomogram |
| spellingShingle | Shaoqin Jiang Shaoqin Jiang Zhihao Li Ruiling Dou Zequn Lin Jili Zhang Wenhui Zhang Zeyu Chen Xianqi Shen Jin Ji Min Qu Yan Wang Mengqiang Li Xu Gao Construction and validation of a novel cuproptosis-related long noncoding RNA signature for predicting the outcome of prostate cancer Frontiers in Genetics cuproptosis prostate cancer long noncoding RNA signature prognosis model nomogram |
| title | Construction and validation of a novel cuproptosis-related long noncoding RNA signature for predicting the outcome of prostate cancer |
| title_full | Construction and validation of a novel cuproptosis-related long noncoding RNA signature for predicting the outcome of prostate cancer |
| title_fullStr | Construction and validation of a novel cuproptosis-related long noncoding RNA signature for predicting the outcome of prostate cancer |
| title_full_unstemmed | Construction and validation of a novel cuproptosis-related long noncoding RNA signature for predicting the outcome of prostate cancer |
| title_short | Construction and validation of a novel cuproptosis-related long noncoding RNA signature for predicting the outcome of prostate cancer |
| title_sort | construction and validation of a novel cuproptosis related long noncoding rna signature for predicting the outcome of prostate cancer |
| topic | cuproptosis prostate cancer long noncoding RNA signature prognosis model nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.976850/full |
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