Pathway-based cancer transcriptome deciphers a high-resolution intrinsic heterogeneity within bladder cancer classification
Abstract Background The heterogeneity of bladder cancer (BLCA) is affected by its inherent transcriptional properties and tumor microenvironment (TME). Stromal transcriptional components in the TME significantly influence the transcriptional classification of BLCA, and the intrinsic biological trans...
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| Language: | English |
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BMC
2025-06-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-025-06682-1 |
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| author | Zhan Wang Zhaokai Zhou Shuai Yang Zhengrui Li Run Shi Ruizhi Wang Kui Liu Xiaojuan Tang Qi Li |
| author_facet | Zhan Wang Zhaokai Zhou Shuai Yang Zhengrui Li Run Shi Ruizhi Wang Kui Liu Xiaojuan Tang Qi Li |
| author_sort | Zhan Wang |
| collection | DOAJ |
| description | Abstract Background The heterogeneity of bladder cancer (BLCA) is affected by its inherent transcriptional properties and tumor microenvironment (TME). Stromal transcriptional components in the TME significantly influence the transcriptional classification of BLCA, and the intrinsic biological transcriptional characteristics of cancer cells may be obscured by the dominant, lineage-dependent transcriptional components of stromal origin. This study aimed to explore the degree and mechanisms by which cancer-intrinsic gene expression profiles contribute to the classification and prognosis of BLCA patients. Materials and methods In this study, BLCA single-cell transcriptome data from GSE135337 were used to identify pure tumor cells in BLCA and explore the different intrinsic heterogeneous cell subgroups of BLCA through pathway-based cancer transcriptome classification. Additionally, BLCA intrinsic subtypes were uncovered in the TCGA BLCA dataset based on the characteristic genes of the subgroups. Lastly, various machine learning algorithms were applied to identify novel potential targets of BLCA, following which their pro-tumorigenic effects were experimentally verified. Results Four BLCA intrinsic subtypes with different molecular, functional and phenotypic characteristics were successfully identified. Specifically, MA and DP subtypes demonstrated malignant phenotypes, accompanied by unfavorable clinical prognoses, limited involvement in cell death pathways, marked cell proliferation, and diminished immune activation. Notably, MA subtype exhibited the most favorable response to immunotherapy, potentially attributable to its distinctive tumor immune microenvironment. DSM subtype represented an immune-rich subtype with the optimal prognosis, characterized by abundant immune cells, high levels of co-stimulatory, co-inhibitory, major histocompatibility complex molecules, and a potential for immunotherapy response. On the other hand, HM subtype was associated with a high level of autophagy and necrosis and an “immune-hot” TIME. Furthermore, BLCA intrinsic subtypes effectively classified independent sets of BLCAs, with limited overlap with existing transcriptional classifications and showcasing unprecedented predictive and prognostic value. Finally, the DP subtype, associated with the worst prognosis, was further analyzed, leading to the identification of three potential target genes (DAD1, CYP1B1, and REXO2) significantly associated with metabolic disorders, as well as BLCA stage and grade. Conclusion This study identified a promising platform for understanding intrinsic tumor heterogeneity, which could offer new insights into the intricate molecular mechanisms of BLCA. Targeted therapy against BEXO2 may improve the prognosis of BLCA patients by regulating mitochondria-related metabolic disorders. |
| format | Article |
| id | doaj-art-e3a1fd57652e4189acc6ca8e32d6acdf |
| institution | OA Journals |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-e3a1fd57652e4189acc6ca8e32d6acdf2025-08-20T02:10:35ZengBMCJournal of Translational Medicine1479-58762025-06-0123111910.1186/s12967-025-06682-1Pathway-based cancer transcriptome deciphers a high-resolution intrinsic heterogeneity within bladder cancer classificationZhan Wang0Zhaokai Zhou1Shuai Yang2Zhengrui Li3Run Shi4Ruizhi Wang5Kui Liu6Xiaojuan Tang7Qi Li8Department of Urology, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Urology, The Second Xiangya Hospital of Central South UniversityDepartment of Urology, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Urology, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Pediatric Surgery, The First Affiliated Hospital of Henan University of Science and TechnologyDepartment of Plastic and Reconstructive Surgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Pediatric Surgery, The First Affiliated Hospital of Zhengzhou UniversityAbstract Background The heterogeneity of bladder cancer (BLCA) is affected by its inherent transcriptional properties and tumor microenvironment (TME). Stromal transcriptional components in the TME significantly influence the transcriptional classification of BLCA, and the intrinsic biological transcriptional characteristics of cancer cells may be obscured by the dominant, lineage-dependent transcriptional components of stromal origin. This study aimed to explore the degree and mechanisms by which cancer-intrinsic gene expression profiles contribute to the classification and prognosis of BLCA patients. Materials and methods In this study, BLCA single-cell transcriptome data from GSE135337 were used to identify pure tumor cells in BLCA and explore the different intrinsic heterogeneous cell subgroups of BLCA through pathway-based cancer transcriptome classification. Additionally, BLCA intrinsic subtypes were uncovered in the TCGA BLCA dataset based on the characteristic genes of the subgroups. Lastly, various machine learning algorithms were applied to identify novel potential targets of BLCA, following which their pro-tumorigenic effects were experimentally verified. Results Four BLCA intrinsic subtypes with different molecular, functional and phenotypic characteristics were successfully identified. Specifically, MA and DP subtypes demonstrated malignant phenotypes, accompanied by unfavorable clinical prognoses, limited involvement in cell death pathways, marked cell proliferation, and diminished immune activation. Notably, MA subtype exhibited the most favorable response to immunotherapy, potentially attributable to its distinctive tumor immune microenvironment. DSM subtype represented an immune-rich subtype with the optimal prognosis, characterized by abundant immune cells, high levels of co-stimulatory, co-inhibitory, major histocompatibility complex molecules, and a potential for immunotherapy response. On the other hand, HM subtype was associated with a high level of autophagy and necrosis and an “immune-hot” TIME. Furthermore, BLCA intrinsic subtypes effectively classified independent sets of BLCAs, with limited overlap with existing transcriptional classifications and showcasing unprecedented predictive and prognostic value. Finally, the DP subtype, associated with the worst prognosis, was further analyzed, leading to the identification of three potential target genes (DAD1, CYP1B1, and REXO2) significantly associated with metabolic disorders, as well as BLCA stage and grade. Conclusion This study identified a promising platform for understanding intrinsic tumor heterogeneity, which could offer new insights into the intricate molecular mechanisms of BLCA. Targeted therapy against BEXO2 may improve the prognosis of BLCA patients by regulating mitochondria-related metabolic disorders.https://doi.org/10.1186/s12967-025-06682-1Bladder cancerSingle-cell RNA-seqIntrinsic heterogeneityMolecular subtypesMetabolic disorderImmune microenvironment |
| spellingShingle | Zhan Wang Zhaokai Zhou Shuai Yang Zhengrui Li Run Shi Ruizhi Wang Kui Liu Xiaojuan Tang Qi Li Pathway-based cancer transcriptome deciphers a high-resolution intrinsic heterogeneity within bladder cancer classification Journal of Translational Medicine Bladder cancer Single-cell RNA-seq Intrinsic heterogeneity Molecular subtypes Metabolic disorder Immune microenvironment |
| title | Pathway-based cancer transcriptome deciphers a high-resolution intrinsic heterogeneity within bladder cancer classification |
| title_full | Pathway-based cancer transcriptome deciphers a high-resolution intrinsic heterogeneity within bladder cancer classification |
| title_fullStr | Pathway-based cancer transcriptome deciphers a high-resolution intrinsic heterogeneity within bladder cancer classification |
| title_full_unstemmed | Pathway-based cancer transcriptome deciphers a high-resolution intrinsic heterogeneity within bladder cancer classification |
| title_short | Pathway-based cancer transcriptome deciphers a high-resolution intrinsic heterogeneity within bladder cancer classification |
| title_sort | pathway based cancer transcriptome deciphers a high resolution intrinsic heterogeneity within bladder cancer classification |
| topic | Bladder cancer Single-cell RNA-seq Intrinsic heterogeneity Molecular subtypes Metabolic disorder Immune microenvironment |
| url | https://doi.org/10.1186/s12967-025-06682-1 |
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