Multi-dimensional characterization of cellular states reveals clinically relevant immunological subtypes and therapeutic vulnerabilities in ovarian cancer

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Опубликовано в::Journal of Translational Medicine vol. 23, no. 1 (Dec 2025), p. 519
Главный автор: Zhang, Can
Другие авторы: Li, Si, Guo, Jiyu, Pan, Tao, Zhang, Ya, Gao, Yueying, Pan, Jiwei, Liu, Meng, Yang, Qingyi, Yu, Jinyang, Xu, Juan, Li, Yongsheng, Li, Xia
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Springer Nature B.V.
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024 7 |a 10.1186/s12967-025-06521-3  |2 doi 
035 |a 3290946158 
045 2 |b d20251201  |b d20251231 
084 |a 67196  |2 nlm 
100 1 |a Zhang, Can  |u Hainan Medical University, College of Biomedical Information and Engineering, Haikou, China (GRID:grid.443397.e) (ISNI:0000 0004 0368 7493) 
245 1 |a Multi-dimensional characterization of cellular states reveals clinically relevant immunological subtypes and therapeutic vulnerabilities in ovarian cancer 
260 |b Springer Nature B.V.  |c Dec 2025 
513 |a Journal Article 
520 3 |a BackgroundDiverse cell types and cellular states in the tumor microenvironment (TME) are drivers of biological and therapeutic heterogeneity in ovarian cancer (OV). Characterization of the diverse malignant and immunology cellular states that make up the TME and their associations with clinical outcomes are critical for cancer therapy. However, we are still lack of knowledge about the cellular states and their clinical relevance in OV.MethodsWe manually collected the comprehensive transcriptomes of OV samples and characterized the cellular states and ecotypes based on a machine-learning framework. The robustness of the cellular states was validated in independent cohorts and single-cell transcriptomes. The functions and regulators of cellular states were investigated. Meanwhile, we thoroughly examined the associations between cellular states and various clinical factors, including clinical prognosis and drug responses.ResultsWe depicted and characterized an immunophenotypic landscape of 3,099 OV samples and 80,044 cells based on a machine learning framework. We identified and validated 32 distinct transcriptionally defined cellular states from 12 cell types and three cellular communities or ecotypes, extending the current immunological subtypes in OV. Functional enrichment and upstream transcriptional regulator analyses revealed cancer hallmark-related pathways and potential immunological biomarkers. We further investigated the spatial patterns of identified cellular states by integrating the spatially resolved transcriptomes. Moreover, prognostic landscape and drug sensitivity analysis exhibited clinically relevant immunological subtypes and therapeutic vulnerabilities.ConclusionOur comprehensive analysis of TME helps leveraging various immunological subtypes to highlight new directions and targets for the treatment of cancer. 
653 |a Ovarian cancer 
653 |a Transcriptomes 
653 |a Gene expression 
653 |a Datasets 
653 |a Medical prognosis 
653 |a Ecotypes 
653 |a Cancer therapies 
653 |a Immunology 
653 |a Sensitivity analysis 
653 |a Lymphocytes 
653 |a Tumor microenvironment 
653 |a Machine learning 
653 |a Standard scores 
653 |a Tumors 
653 |a Dimensional analysis 
653 |a Cell cycle 
653 |a Chemotherapy 
653 |a Learning algorithms 
700 1 |a Li, Si  |u Harbin Medical University, School of Interdisciplinary Medicine and Engineering, Harbin, China (GRID:grid.410736.7) (ISNI:0000 0001 2204 9268) 
700 1 |a Guo, Jiyu  |u Harbin Medical University, School of Interdisciplinary Medicine and Engineering, Harbin, China (GRID:grid.410736.7) (ISNI:0000 0001 2204 9268) 
700 1 |a Pan, Tao  |u Hainan Medical University, College of Biomedical Information and Engineering, Haikou, China (GRID:grid.443397.e) (ISNI:0000 0004 0368 7493) 
700 1 |a Zhang, Ya  |u Hainan Medical University, College of Biomedical Information and Engineering, Haikou, China (GRID:grid.443397.e) (ISNI:0000 0004 0368 7493) 
700 1 |a Gao, Yueying  |u Harbin Medical University, School of Interdisciplinary Medicine and Engineering, Harbin, China (GRID:grid.410736.7) (ISNI:0000 0001 2204 9268) 
700 1 |a Pan, Jiwei  |u Hainan Medical University, College of Biomedical Information and Engineering, Haikou, China (GRID:grid.443397.e) (ISNI:0000 0004 0368 7493) 
700 1 |a Liu, Meng  |u Hainan Medical University, College of Biomedical Information and Engineering, Haikou, China (GRID:grid.443397.e) (ISNI:0000 0004 0368 7493) 
700 1 |a Yang, Qingyi  |u Harbin Medical University, School of Interdisciplinary Medicine and Engineering, Harbin, China (GRID:grid.410736.7) (ISNI:0000 0001 2204 9268) 
700 1 |a Yu, Jinyang  |u Hainan Medical University, College of Biomedical Information and Engineering, Haikou, China (GRID:grid.443397.e) (ISNI:0000 0004 0368 7493) 
700 1 |a Xu, Juan  |u Harbin Medical University, College of Bioinformatics Science and Technology, Harbin, China (GRID:grid.410736.7) (ISNI:0000 0001 2204 9268) 
700 1 |a Li, Yongsheng  |u Harbin Medical University, School of Interdisciplinary Medicine and Engineering, Harbin, China (GRID:grid.410736.7) (ISNI:0000 0001 2204 9268); Harbin Medical University Cancer Hospital, Department of Radiation Oncology, Harbin, China (GRID:grid.412651.5) (ISNI:0000 0004 1808 3502); The First Affiliated Hospital of Harbin Medical University, Department of Anesthesiology, Harbin, China (GRID:grid.412596.d) (ISNI:0000 0004 1797 9737) 
700 1 |a Li, Xia  |u Hainan Medical University, College of Biomedical Information and Engineering, Haikou, China (GRID:grid.443397.e) (ISNI:0000 0004 0368 7493); Harbin Medical University, College of Bioinformatics Science and Technology, Harbin, China (GRID:grid.410736.7) (ISNI:0000 0001 2204 9268) 
773 0 |t Journal of Translational Medicine  |g vol. 23, no. 1 (Dec 2025), p. 519 
786 0 |d ProQuest  |t Health & Medical Collection 
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856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3290946158/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3290946158/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch