Construction of a prostate adenocarcinoma molecular classification: integrating spatial transcriptomics with retrospective cohort validation
Abstract Background Prostate adenocarcinoma (PRAD) is a biologically heterogeneous disease threatening the health of elderly males worldwide, and some PRAD subtypes with certain molecular landscape are always associated with poor prognosis. A more precise molecular classification system for PRAD is...
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| Language: | English |
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BMC
2025-07-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-025-06661-6 |
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| _version_ | 1849767672102780928 |
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| author | Bingnan Lu Yifan Liu Guo Ji Yuntao Yao Zhao Yang Bolin Zhu Lei Wang Keqin Dong Yuanan Li Jiaying Shi Junzhe He Runzhi Huang Wang Zhou Xinming Cui Xiuwu Pan Xingang Cui |
| author_facet | Bingnan Lu Yifan Liu Guo Ji Yuntao Yao Zhao Yang Bolin Zhu Lei Wang Keqin Dong Yuanan Li Jiaying Shi Junzhe He Runzhi Huang Wang Zhou Xinming Cui Xiuwu Pan Xingang Cui |
| author_sort | Bingnan Lu |
| collection | DOAJ |
| description | Abstract Background Prostate adenocarcinoma (PRAD) is a biologically heterogeneous disease threatening the health of elderly males worldwide, and some PRAD subtypes with certain molecular landscape are always associated with poor prognosis. A more precise molecular classification system for PRAD is urgently needed. Methods Through spatial transcriptome analysis, we identified different malignant cell/spot types in PRAD. Then, Monocle 2 analysis was applied to identify malignant cell fates and differentiation-related genes. Together with the prognosis-related genes identified through Kaplan–Meier analysis and univariate Cox regression in TCGA-PRAD cohort, we defined malignant cell differentiation-related prognostic genes (MDPGs). Based on MDPGs, we constructed a malignant cell differentiation-based PRAD classification (MDPC) using the ConsensusClusterPlus algorithm. Then, we explored multi-omics correlations of MDPC, and constructed the regulation networks of MDPC as well as the prognostic prediction model. Finally, we validated the prognostic prediction value of MDPC through immunohistochemical staining and follow-up of a retrospective cohort. Results Three malignant spot types were identified through spatial transcriptome analysis. Then, we defined 33 MDPGs and successfully constructed MDPC with three different subtypes (DPP4+MSMB+ MDPC, NHP2+NVL+ MDPC, COL1A1+MYLK+ MDPC). Apart from the correlations with tumor genomics, immunomics, MDPC also harbored convincing prognostic prediction value. In our cohort, COL1A1+MYLK+ MDPC served as an independent risk factor of OS (hazard ratio (HR) = 20.720, P-value = 0.0018) and PFS (HR = 117.00, P-value = 0.0036), and was closely correlated with Gleason grade, WHO/ISUP grade, radiotherapy, chemotherapy, endocrinotherapy, bone metastasis before treatment, and progression after treatment. Conclusion We successfully constructed MDPC with validated prognostic prediction value. This classification system provided clinicians with an effective tool to stratify PRAD patients, identifying high-risk individuals, recognizing patients prone to develop bone metastasis, and offering opportunities for early intervention to improve patients’ prognosis. |
| format | Article |
| id | doaj-art-32845bfbc0ab4784a5d88ac0695da8f0 |
| institution | DOAJ |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-32845bfbc0ab4784a5d88ac0695da8f02025-08-20T03:04:07ZengBMCJournal of Translational Medicine1479-58762025-07-0123112710.1186/s12967-025-06661-6Construction of a prostate adenocarcinoma molecular classification: integrating spatial transcriptomics with retrospective cohort validationBingnan Lu0Yifan Liu1Guo Ji2Yuntao Yao3Zhao Yang4Bolin Zhu5Lei Wang6Keqin Dong7Yuanan Li8Jiaying Shi9Junzhe He10Runzhi Huang11Wang Zhou12Xinming Cui13Xiuwu Pan14Xingang Cui15Department of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineDepartment of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineDepartment of Pathology, Shanghai Tenth People’s Hospital, Tongji University School of MedicineDepartment of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai General Hospital Affiliated to Shanghai Jiao Tong University School of MedicineCollege of Life Sciences, University of Chinese Academy of SciencesDepartment of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineDepartment of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineDepartment of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Jiao Tong University School of MedicineShanghai Jiao Tong University School of MedicineDepartment of Burn Surgery, the First Affiliated Hospital of Naval Medical UniversityDepartment of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineNangang Branch, Heilongjiang Provincial HospitalDepartment of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineDepartment of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineAbstract Background Prostate adenocarcinoma (PRAD) is a biologically heterogeneous disease threatening the health of elderly males worldwide, and some PRAD subtypes with certain molecular landscape are always associated with poor prognosis. A more precise molecular classification system for PRAD is urgently needed. Methods Through spatial transcriptome analysis, we identified different malignant cell/spot types in PRAD. Then, Monocle 2 analysis was applied to identify malignant cell fates and differentiation-related genes. Together with the prognosis-related genes identified through Kaplan–Meier analysis and univariate Cox regression in TCGA-PRAD cohort, we defined malignant cell differentiation-related prognostic genes (MDPGs). Based on MDPGs, we constructed a malignant cell differentiation-based PRAD classification (MDPC) using the ConsensusClusterPlus algorithm. Then, we explored multi-omics correlations of MDPC, and constructed the regulation networks of MDPC as well as the prognostic prediction model. Finally, we validated the prognostic prediction value of MDPC through immunohistochemical staining and follow-up of a retrospective cohort. Results Three malignant spot types were identified through spatial transcriptome analysis. Then, we defined 33 MDPGs and successfully constructed MDPC with three different subtypes (DPP4+MSMB+ MDPC, NHP2+NVL+ MDPC, COL1A1+MYLK+ MDPC). Apart from the correlations with tumor genomics, immunomics, MDPC also harbored convincing prognostic prediction value. In our cohort, COL1A1+MYLK+ MDPC served as an independent risk factor of OS (hazard ratio (HR) = 20.720, P-value = 0.0018) and PFS (HR = 117.00, P-value = 0.0036), and was closely correlated with Gleason grade, WHO/ISUP grade, radiotherapy, chemotherapy, endocrinotherapy, bone metastasis before treatment, and progression after treatment. Conclusion We successfully constructed MDPC with validated prognostic prediction value. This classification system provided clinicians with an effective tool to stratify PRAD patients, identifying high-risk individuals, recognizing patients prone to develop bone metastasis, and offering opportunities for early intervention to improve patients’ prognosis.https://doi.org/10.1186/s12967-025-06661-6Prostate adenocarcinomaMolecular classification systemCell differentiationSpatial transcriptomeImmunohistochemical stainingRetrospective clinical cohort validation |
| spellingShingle | Bingnan Lu Yifan Liu Guo Ji Yuntao Yao Zhao Yang Bolin Zhu Lei Wang Keqin Dong Yuanan Li Jiaying Shi Junzhe He Runzhi Huang Wang Zhou Xinming Cui Xiuwu Pan Xingang Cui Construction of a prostate adenocarcinoma molecular classification: integrating spatial transcriptomics with retrospective cohort validation Journal of Translational Medicine Prostate adenocarcinoma Molecular classification system Cell differentiation Spatial transcriptome Immunohistochemical staining Retrospective clinical cohort validation |
| title | Construction of a prostate adenocarcinoma molecular classification: integrating spatial transcriptomics with retrospective cohort validation |
| title_full | Construction of a prostate adenocarcinoma molecular classification: integrating spatial transcriptomics with retrospective cohort validation |
| title_fullStr | Construction of a prostate adenocarcinoma molecular classification: integrating spatial transcriptomics with retrospective cohort validation |
| title_full_unstemmed | Construction of a prostate adenocarcinoma molecular classification: integrating spatial transcriptomics with retrospective cohort validation |
| title_short | Construction of a prostate adenocarcinoma molecular classification: integrating spatial transcriptomics with retrospective cohort validation |
| title_sort | construction of a prostate adenocarcinoma molecular classification integrating spatial transcriptomics with retrospective cohort validation |
| topic | Prostate adenocarcinoma Molecular classification system Cell differentiation Spatial transcriptome Immunohistochemical staining Retrospective clinical cohort validation |
| url | https://doi.org/10.1186/s12967-025-06661-6 |
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