Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework
BackgroundBladder cancer (BLCA) is the most common malignancy of the urinary tract. On the other hand, disulfidptosis, a mechanism of disulfide stress-induced cell death, is closely associated with tumorigenesis and progression. Here, we investigated the impact of disulfidptosis-related genes (DRGs)...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2023.1180404/full |
| _version_ | 1797844266866704384 |
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| author | Songyun Zhao Songyun Zhao Lanyu Wang Wei Ding Bicheng Ye Chao Cheng Jianfeng Shao Jinhui Liu Hongyi Zhou |
| author_facet | Songyun Zhao Songyun Zhao Lanyu Wang Wei Ding Bicheng Ye Chao Cheng Jianfeng Shao Jinhui Liu Hongyi Zhou |
| author_sort | Songyun Zhao |
| collection | DOAJ |
| description | BackgroundBladder cancer (BLCA) is the most common malignancy of the urinary tract. On the other hand, disulfidptosis, a mechanism of disulfide stress-induced cell death, is closely associated with tumorigenesis and progression. Here, we investigated the impact of disulfidptosis-related genes (DRGs) on the prognosis of BLCA, identified various DRG clusters, and developed a risk model to assess patient prognosis, immunological profile, and treatment response.MethodsThe expression and mutational characteristics of four DRGs were first analyzed in bulk RNA-Seq and single-cell RNA sequencing data, IHC staining identified the role of DRGs in BLCA progression, and two DRG clusters were identified by consensus clustering. Using the differentially expressed genes (DEGs) from these two clusters, we transformed ten machine learning algorithms into more than 80 combinations and finally selected the best algorithm to construct a disulfidptosis-related prognostic signature (DRPS). We based this selection on the mean C-index of three BLCA cohorts. Furthermore, we explored the differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between high and low-risk groups. To visually depict the clinical value of DRPS, we employed nomograms. Additionally, we verified whether DRPS predicts response to immunotherapy in BLCA patients by utilizing the Tumour Immune Dysfunction and Rejection (TIDE) and IMvigor 210 cohorts.ResultsIn the integrated cohort, we identified several DRG clusters and DRG gene clusters that differed significantly in overall survival (OS) and tumor microenvironment. After the integration of clinicopathological features, DRPS showed robust predictive power. Based on the median risk score associated with disulfidptosis, BLCA patients were divided into low-risk (LR) and high-risk (HR) groups, with patients in the LR group having a better prognosis, a higher tumor mutational load and being more sensitive to immunotherapy and chemotherapy.ConclusionOur study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of BLCA patients, offering new insights into individualized treatment. |
| first_indexed | 2024-04-09T17:19:30Z |
| format | Article |
| id | doaj.art-7fabebd82ec3423892eafa165016fb40 |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-04-09T17:19:30Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-7fabebd82ec3423892eafa165016fb402023-04-19T05:05:13ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922023-04-011410.3389/fendo.2023.11804041180404Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival frameworkSongyun Zhao0Songyun Zhao1Lanyu Wang2Wei Ding3Bicheng Ye4Chao Cheng5Jianfeng Shao6Jinhui Liu7Hongyi Zhou8Department of Urology, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, ChinaDepartment of Neurosurgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, ChinaDepartment of Urology, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, ChinaDepartment of Nuclear Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaSchool of Clinical Medicine, Yangzhou Polytechnic College, Yangzhou, ChinaDepartment of Neurosurgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, ChinaDepartment of Urology, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, ChinaDepartment of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Urology, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, ChinaBackgroundBladder cancer (BLCA) is the most common malignancy of the urinary tract. On the other hand, disulfidptosis, a mechanism of disulfide stress-induced cell death, is closely associated with tumorigenesis and progression. Here, we investigated the impact of disulfidptosis-related genes (DRGs) on the prognosis of BLCA, identified various DRG clusters, and developed a risk model to assess patient prognosis, immunological profile, and treatment response.MethodsThe expression and mutational characteristics of four DRGs were first analyzed in bulk RNA-Seq and single-cell RNA sequencing data, IHC staining identified the role of DRGs in BLCA progression, and two DRG clusters were identified by consensus clustering. Using the differentially expressed genes (DEGs) from these two clusters, we transformed ten machine learning algorithms into more than 80 combinations and finally selected the best algorithm to construct a disulfidptosis-related prognostic signature (DRPS). We based this selection on the mean C-index of three BLCA cohorts. Furthermore, we explored the differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between high and low-risk groups. To visually depict the clinical value of DRPS, we employed nomograms. Additionally, we verified whether DRPS predicts response to immunotherapy in BLCA patients by utilizing the Tumour Immune Dysfunction and Rejection (TIDE) and IMvigor 210 cohorts.ResultsIn the integrated cohort, we identified several DRG clusters and DRG gene clusters that differed significantly in overall survival (OS) and tumor microenvironment. After the integration of clinicopathological features, DRPS showed robust predictive power. Based on the median risk score associated with disulfidptosis, BLCA patients were divided into low-risk (LR) and high-risk (HR) groups, with patients in the LR group having a better prognosis, a higher tumor mutational load and being more sensitive to immunotherapy and chemotherapy.ConclusionOur study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of BLCA patients, offering new insights into individualized treatment.https://www.frontiersin.org/articles/10.3389/fendo.2023.1180404/fulldisulfidptosisBLCAmachine learningtumor microenvironmentimmunotherapyrisk score signature |
| spellingShingle | Songyun Zhao Songyun Zhao Lanyu Wang Wei Ding Bicheng Ye Chao Cheng Jianfeng Shao Jinhui Liu Hongyi Zhou Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework Frontiers in Endocrinology disulfidptosis BLCA machine learning tumor microenvironment immunotherapy risk score signature |
| title | Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework |
| title_full | Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework |
| title_fullStr | Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework |
| title_full_unstemmed | Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework |
| title_short | Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework |
| title_sort | crosstalk of disulfidptosis related subtypes establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework |
| topic | disulfidptosis BLCA machine learning tumor microenvironment immunotherapy risk score signature |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2023.1180404/full |
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