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

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Publicado en:Journal of Translational Medicine vol. 23, no. 1 (Dec 2025), p. 519
Autor principal: Zhang, Can
Otros Autores: Li, Si, Guo, Jiyu, Pan, Tao, Zhang, Ya, Gao, Yueying, Pan, Jiwei, Liu, Meng, Yang, Qingyi, Yu, Jinyang, Xu, Juan, Li, Yongsheng, Li, Xia
Publicado:
Springer Nature B.V.
Materias:
Ovarian cancer
Transcriptomes
Gene expression
Datasets
Medical prognosis
Ecotypes
Cancer therapies
Immunology
Sensitivity analysis
Lymphocytes
Tumor microenvironment
Machine learning
Standard scores
Tumors
Dimensional analysis
Cell cycle
Chemotherapy
Learning algorithms
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:1479-5876
DOI:10.1186/s12967-025-06521-3
Fuente:Health & Medical Collection