Prostate cancer and metabolic syndrome: exploring shared signature genes through integrative analysis of bioinformatics and clinical data
Abstract The incidence of both prostate cancer (PCa) and metabolic syndrome (MS) has been steadily increasing due to changes in population structure and lifestyle. These two conditions frequently co-occur, yet their shared pathogenic mechanisms remain unclear. In this study, we utilized bioinformati...
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-02561-9 |
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| author | Maomao Guo Sudong Liang Zhenghui Guan Jingcheng Mao Zhibin Xu Wenchao Zhao Hao Bian Jianfeng Zhu Jiangping Wang Xin Jin Yuan Xia |
| author_facet | Maomao Guo Sudong Liang Zhenghui Guan Jingcheng Mao Zhibin Xu Wenchao Zhao Hao Bian Jianfeng Zhu Jiangping Wang Xin Jin Yuan Xia |
| author_sort | Maomao Guo |
| collection | DOAJ |
| description | Abstract The incidence of both prostate cancer (PCa) and metabolic syndrome (MS) has been steadily increasing due to changes in population structure and lifestyle. These two conditions frequently co-occur, yet their shared pathogenic mechanisms remain unclear. In this study, we utilized bioinformatics and machine learning techniques to analyze public datasets and validated our findings using clinical specimens from our center to identify common signature genes between PCa and MS. We began by screening differentially expressed genes (DEGs) and module genes through Linear models for microarray analysis (Limma) and Weighted Gene Co-expression Network Analysis (WGCNA) of four microarray datasets from the GEO database (PCa: GSE8511, GSE32571, and GSE104749; MS: GSE98895). Comprehensively bioinformatics analyses, including functional enrichment, LASSO, and random forest algorithms, coupled with receiver operating characteristic (ROC) and precision recall curve (PRC) analyses were conducted. We identified 423 DEGs in the PCa dataset and 2481 differentially modular genes in the MS dataset. Among these, 52 intersection genes enriched in immunomodulatory pathways were found. Three common signature genes, namely GPD1L, ACY1, and C12orf75, were identified through LASSO and random forest analyses. Subsequent validation using clinical specimens confirmed differential expression of these genes in PCa, with survival analysis indicating that elevated expression of ACY1 is associated with adverse prognosis in PCa patients. Additionally, immunoinfiltration analysis revealed higher levels of macrophage M0 and activated dendritic cells in PCa tissues. In summary, our study identifies three shared signature genes between PCa and MS, with ACY1 demonstrating adverse prognostic significance in PCa. Our findings provide a foundation for elucidating the pathogenic mechanisms and interplay between PCa and MS, offering novel insights for identifying potential therapeutic targets in PCa. |
| format | Article |
| id | doaj-art-2ad9aadd6c6a4527a5281b06300971f6 |
| institution | DOAJ |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-2ad9aadd6c6a4527a5281b06300971f62025-08-20T03:09:20ZengSpringerDiscover Oncology2730-60112025-05-0116111310.1007/s12672-025-02561-9Prostate cancer and metabolic syndrome: exploring shared signature genes through integrative analysis of bioinformatics and clinical dataMaomao Guo0Sudong Liang1Zhenghui Guan2Jingcheng Mao3Zhibin Xu4Wenchao Zhao5Hao Bian6Jianfeng Zhu7Jiangping Wang8Xin Jin9Yuan Xia10Department of Urology, The Affiliated Taizhou People’s Hospital of Nanjing Medical UniversityDepartment of Urology, The Affiliated Taizhou People’s Hospital of Nanjing Medical UniversityDepartment of Urology, The Affiliated Taizhou People’s Hospital of Nanjing Medical UniversityDepartment of Hematology, The Affiliated Taizhou People’s Hospital of Nanjing Medical UniversityDepartment of Urology, The Affiliated Taizhou People’s Hospital of Nanjing Medical UniversityDepartment of Urology, The Affiliated Taizhou People’s Hospital of Nanjing Medical UniversityDepartment of Urology, The Affiliated Taizhou People’s Hospital of Nanjing Medical UniversityDepartment of Hematology, The Affiliated Taizhou People’s Hospital of Nanjing Medical UniversityDepartment of Urology, The Affiliated Taizhou People’s Hospital of Nanjing Medical UniversityDepartment of Urology, The Affiliated Taizhou People’s Hospital of Nanjing Medical UniversityDepartment of Hematology, The Affiliated Taizhou People’s Hospital of Nanjing Medical UniversityAbstract The incidence of both prostate cancer (PCa) and metabolic syndrome (MS) has been steadily increasing due to changes in population structure and lifestyle. These two conditions frequently co-occur, yet their shared pathogenic mechanisms remain unclear. In this study, we utilized bioinformatics and machine learning techniques to analyze public datasets and validated our findings using clinical specimens from our center to identify common signature genes between PCa and MS. We began by screening differentially expressed genes (DEGs) and module genes through Linear models for microarray analysis (Limma) and Weighted Gene Co-expression Network Analysis (WGCNA) of four microarray datasets from the GEO database (PCa: GSE8511, GSE32571, and GSE104749; MS: GSE98895). Comprehensively bioinformatics analyses, including functional enrichment, LASSO, and random forest algorithms, coupled with receiver operating characteristic (ROC) and precision recall curve (PRC) analyses were conducted. We identified 423 DEGs in the PCa dataset and 2481 differentially modular genes in the MS dataset. Among these, 52 intersection genes enriched in immunomodulatory pathways were found. Three common signature genes, namely GPD1L, ACY1, and C12orf75, were identified through LASSO and random forest analyses. Subsequent validation using clinical specimens confirmed differential expression of these genes in PCa, with survival analysis indicating that elevated expression of ACY1 is associated with adverse prognosis in PCa patients. Additionally, immunoinfiltration analysis revealed higher levels of macrophage M0 and activated dendritic cells in PCa tissues. In summary, our study identifies three shared signature genes between PCa and MS, with ACY1 demonstrating adverse prognostic significance in PCa. Our findings provide a foundation for elucidating the pathogenic mechanisms and interplay between PCa and MS, offering novel insights for identifying potential therapeutic targets in PCa.https://doi.org/10.1007/s12672-025-02561-9Prostate cancerMetabolic syndromeBioinformatics analysisImmune infiltration |
| spellingShingle | Maomao Guo Sudong Liang Zhenghui Guan Jingcheng Mao Zhibin Xu Wenchao Zhao Hao Bian Jianfeng Zhu Jiangping Wang Xin Jin Yuan Xia Prostate cancer and metabolic syndrome: exploring shared signature genes through integrative analysis of bioinformatics and clinical data Discover Oncology Prostate cancer Metabolic syndrome Bioinformatics analysis Immune infiltration |
| title | Prostate cancer and metabolic syndrome: exploring shared signature genes through integrative analysis of bioinformatics and clinical data |
| title_full | Prostate cancer and metabolic syndrome: exploring shared signature genes through integrative analysis of bioinformatics and clinical data |
| title_fullStr | Prostate cancer and metabolic syndrome: exploring shared signature genes through integrative analysis of bioinformatics and clinical data |
| title_full_unstemmed | Prostate cancer and metabolic syndrome: exploring shared signature genes through integrative analysis of bioinformatics and clinical data |
| title_short | Prostate cancer and metabolic syndrome: exploring shared signature genes through integrative analysis of bioinformatics and clinical data |
| title_sort | prostate cancer and metabolic syndrome exploring shared signature genes through integrative analysis of bioinformatics and clinical data |
| topic | Prostate cancer Metabolic syndrome Bioinformatics analysis Immune infiltration |
| url | https://doi.org/10.1007/s12672-025-02561-9 |
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