Construction of regulatory T cells specific genes predictive models of prostate cancer patients based on machine learning: a computational analysis and in vitro experiments
Abstract Background Diseases are often caused by multiple factors, regulatory T cells specific genes (RTSGs) have been shown to be associated with cancer, however, their role in prostate cancer (PRAD) has not been fully explored. Methods RTSGs associated with PRAD prognosis were identified using Cox...
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Springer
2025-02-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-01862-3 |
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| author | Zhengrong Zhou Chaozhao Liang |
| author_facet | Zhengrong Zhou Chaozhao Liang |
| author_sort | Zhengrong Zhou |
| collection | DOAJ |
| description | Abstract Background Diseases are often caused by multiple factors, regulatory T cells specific genes (RTSGs) have been shown to be associated with cancer, however, their role in prostate cancer (PRAD) has not been fully explored. Methods RTSGs associated with PRAD prognosis were identified using Cox regression analysis and LASSO analysis. Furthermore, a prognostic model was constructed in PRAD based on the 4 RTSGs, and its biological function were analyzed. We evaluated the differences in tumor immune microenvironment based on prognostic signature. Finally, cell experiments confirmed the function of synaptonemal complex protein-2 (SYCP2) in PRAD cells. Results The prognostic value of RTSGs in PRAD patients has been comprehensively analyzed for the first time and identified four RTSGs with prognostic values. A prognosis risk model was constructed based on four RTSGs and its prognostic value was validated on an independent external PRAD dataset. In PRAD patients, this prognostic feature is an independent risk factor and was significantly correlated with clinical feature information of PRAD patients. This feature is also related to the immune microenvironment of PRAD. Cell experiments have confirmed that SYCP2 regulates the apoptosis and cycle progression of PRAD cells significantly. Therefore, SYCP2 may become an important regulatory factor in the progression of PRAD by participating in intracellular functional regulation. Conclusions This research provides a fundamental theoretical basis for improving the diagnosis and treatment of PRAD in clinical practice. |
| format | Article |
| id | doaj-art-ab893422557942d5a6702a6738f7a17c |
| institution | DOAJ |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-ab893422557942d5a6702a6738f7a17c2025-08-20T02:43:13ZengSpringerDiscover Oncology2730-60112025-02-0116111410.1007/s12672-025-01862-3Construction of regulatory T cells specific genes predictive models of prostate cancer patients based on machine learning: a computational analysis and in vitro experimentsZhengrong Zhou0Chaozhao Liang1Department of Urology, The First Affiliated Hospital of Anhui Medical UniversityDepartment of Urology, The First Affiliated Hospital of Anhui Medical UniversityAbstract Background Diseases are often caused by multiple factors, regulatory T cells specific genes (RTSGs) have been shown to be associated with cancer, however, their role in prostate cancer (PRAD) has not been fully explored. Methods RTSGs associated with PRAD prognosis were identified using Cox regression analysis and LASSO analysis. Furthermore, a prognostic model was constructed in PRAD based on the 4 RTSGs, and its biological function were analyzed. We evaluated the differences in tumor immune microenvironment based on prognostic signature. Finally, cell experiments confirmed the function of synaptonemal complex protein-2 (SYCP2) in PRAD cells. Results The prognostic value of RTSGs in PRAD patients has been comprehensively analyzed for the first time and identified four RTSGs with prognostic values. A prognosis risk model was constructed based on four RTSGs and its prognostic value was validated on an independent external PRAD dataset. In PRAD patients, this prognostic feature is an independent risk factor and was significantly correlated with clinical feature information of PRAD patients. This feature is also related to the immune microenvironment of PRAD. Cell experiments have confirmed that SYCP2 regulates the apoptosis and cycle progression of PRAD cells significantly. Therefore, SYCP2 may become an important regulatory factor in the progression of PRAD by participating in intracellular functional regulation. Conclusions This research provides a fundamental theoretical basis for improving the diagnosis and treatment of PRAD in clinical practice.https://doi.org/10.1007/s12672-025-01862-3Prostate cancerRegulatory T cellsPrognosisC4-2 cellsImmune |
| spellingShingle | Zhengrong Zhou Chaozhao Liang Construction of regulatory T cells specific genes predictive models of prostate cancer patients based on machine learning: a computational analysis and in vitro experiments Discover Oncology Prostate cancer Regulatory T cells Prognosis C4-2 cells Immune |
| title | Construction of regulatory T cells specific genes predictive models of prostate cancer patients based on machine learning: a computational analysis and in vitro experiments |
| title_full | Construction of regulatory T cells specific genes predictive models of prostate cancer patients based on machine learning: a computational analysis and in vitro experiments |
| title_fullStr | Construction of regulatory T cells specific genes predictive models of prostate cancer patients based on machine learning: a computational analysis and in vitro experiments |
| title_full_unstemmed | Construction of regulatory T cells specific genes predictive models of prostate cancer patients based on machine learning: a computational analysis and in vitro experiments |
| title_short | Construction of regulatory T cells specific genes predictive models of prostate cancer patients based on machine learning: a computational analysis and in vitro experiments |
| title_sort | construction of regulatory t cells specific genes predictive models of prostate cancer patients based on machine learning a computational analysis and in vitro experiments |
| topic | Prostate cancer Regulatory T cells Prognosis C4-2 cells Immune |
| url | https://doi.org/10.1007/s12672-025-01862-3 |
| work_keys_str_mv | AT zhengrongzhou constructionofregulatorytcellsspecificgenespredictivemodelsofprostatecancerpatientsbasedonmachinelearningacomputationalanalysisandinvitroexperiments AT chaozhaoliang constructionofregulatorytcellsspecificgenespredictivemodelsofprostatecancerpatientsbasedonmachinelearningacomputationalanalysisandinvitroexperiments |