Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learning
BackgroundThe etiology of interstitial cystitis/painful bladder syndrome (IC/BPS) remains elusive, presenting significant challenges in both diagnosis and treatment. To address these challenges, we employed a comprehensive approach aimed at identifying diagnostic biomarkers that could facilitate the...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1511529/full |
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| author | Tao Zhou Can Zhu Wei Zhang Qiongfang Wu Mingqiang Deng Zhiwei Jiang Longfei Peng Hao Geng Zhouting Tuo Zhouting Tuo Ci Zou |
| author_facet | Tao Zhou Can Zhu Wei Zhang Qiongfang Wu Mingqiang Deng Zhiwei Jiang Longfei Peng Hao Geng Zhouting Tuo Zhouting Tuo Ci Zou |
| author_sort | Tao Zhou |
| collection | DOAJ |
| description | BackgroundThe etiology of interstitial cystitis/painful bladder syndrome (IC/BPS) remains elusive, presenting significant challenges in both diagnosis and treatment. To address these challenges, we employed a comprehensive approach aimed at identifying diagnostic biomarkers that could facilitate the assessment of immune status in individuals with IC/BPS.MethodsTranscriptome data from IC/BPS patients were sourced from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) crucial for gene set enrichment analysis. Key genes within the module were revealed using weighted gene co-expression network analysis (WGCNA). Hub genes in IC/BPS patients were identified through the application of three distinct machine-learning algorithms. Additionally, the inflammatory status and immune landscape of IC/BPS patients were evaluated using the ssGSEA algorithm. The expression and biological functions of key genes in IC/BPS were further validated through in vitro experiments.ResultsA total of 87 DEGs were identified, comprising 43 up-regulated and 44 down-regulated genes. The integration of predictions from the three machine-learning algorithms highlighted three pivotal genes: PLAC8 (AUC: 0.887), S100A8 (AUC: 0.818), and PPBP (AUC: 0.871). Analysis of IC/BPS tissue samples confirmed elevated PLAC8 expression and the presence of immune cell markers in the validation cohorts. Moreover, PLAC8 overexpression was found to promote the proliferation of urothelial cells without affecting their migratory ability by inhibiting the Akt/mTOR/PI3K signaling pathway.ConclusionsOur study identifies potential diagnostic candidate genes and reveals the complex immune landscape associated with IC/BPS. Among them, PLAC8 is a promising diagnostic biomarker that modulates the immune response in patients with IC/BPS, which provides new insights into the future diagnosis of IC/BPS. |
| format | Article |
| id | doaj-art-ffee48ef80eb4d258806c69859a97d5b |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-ffee48ef80eb4d258806c69859a97d5b2025-01-23T06:56:33ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011610.3389/fimmu.2025.15115291511529Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learningTao Zhou0Can Zhu1Wei Zhang2Qiongfang Wu3Mingqiang Deng4Zhiwei Jiang5Longfei Peng6Hao Geng7Zhouting Tuo8Zhouting Tuo9Ci Zou10Department of Urology, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Urology, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Urology, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaCenter for Cell Lineage and Development, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences Guangzhou, Guangzhou, ChinaCenter for Cell Lineage and Development, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences Guangzhou, Guangzhou, ChinaDepartment of Urology, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Urology, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Urology, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Urology, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Urological Surgery, Daping Hospital, Army Medical Center of PLA, Army Medical University, Chongqing, ChinaDepartment of Urology, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaBackgroundThe etiology of interstitial cystitis/painful bladder syndrome (IC/BPS) remains elusive, presenting significant challenges in both diagnosis and treatment. To address these challenges, we employed a comprehensive approach aimed at identifying diagnostic biomarkers that could facilitate the assessment of immune status in individuals with IC/BPS.MethodsTranscriptome data from IC/BPS patients were sourced from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) crucial for gene set enrichment analysis. Key genes within the module were revealed using weighted gene co-expression network analysis (WGCNA). Hub genes in IC/BPS patients were identified through the application of three distinct machine-learning algorithms. Additionally, the inflammatory status and immune landscape of IC/BPS patients were evaluated using the ssGSEA algorithm. The expression and biological functions of key genes in IC/BPS were further validated through in vitro experiments.ResultsA total of 87 DEGs were identified, comprising 43 up-regulated and 44 down-regulated genes. The integration of predictions from the three machine-learning algorithms highlighted three pivotal genes: PLAC8 (AUC: 0.887), S100A8 (AUC: 0.818), and PPBP (AUC: 0.871). Analysis of IC/BPS tissue samples confirmed elevated PLAC8 expression and the presence of immune cell markers in the validation cohorts. Moreover, PLAC8 overexpression was found to promote the proliferation of urothelial cells without affecting their migratory ability by inhibiting the Akt/mTOR/PI3K signaling pathway.ConclusionsOur study identifies potential diagnostic candidate genes and reveals the complex immune landscape associated with IC/BPS. Among them, PLAC8 is a promising diagnostic biomarker that modulates the immune response in patients with IC/BPS, which provides new insights into the future diagnosis of IC/BPS.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1511529/fullIC/BPSbioinformaticsmachine-learningPLAC8immune cell landscape |
| spellingShingle | Tao Zhou Can Zhu Wei Zhang Qiongfang Wu Mingqiang Deng Zhiwei Jiang Longfei Peng Hao Geng Zhouting Tuo Zhouting Tuo Ci Zou Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learning Frontiers in Immunology IC/BPS bioinformatics machine-learning PLAC8 immune cell landscape |
| title | Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learning |
| title_full | Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learning |
| title_fullStr | Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learning |
| title_full_unstemmed | Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learning |
| title_short | Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learning |
| title_sort | identification and validation of immune and diagnostic biomarkers for interstitial cystitis painful bladder syndrome by integrating bioinformatics and machine learning |
| topic | IC/BPS bioinformatics machine-learning PLAC8 immune cell landscape |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1511529/full |
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