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Integrative multi-omics and machine learning approach reveals tumor microenvironment-associated prognostic biomarkers in ovarian cancer

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机构： [1]Department of Gynecology, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China. [2]Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine Sun Yat-sen University Cancer Center, Guangzhou, China. [3]Department of Thoracic Surgery, the Second Affiliated Hospital of Soochow University, Suzhou, China. [4]Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China.

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关键词： Ovarian cancer (OC) tumor microenvironment (TME) prognostic biomarkers single-cell RNA sequencing (scRNA-seq) machine learning

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Ovarian cancer (OC) is a globally prevalent malignancy with significant morbidity and mortality, yet its heterogeneity poses challenges in treatment and prognosis. Recognizing the crucial role of the tumor microenvironment (TME) in OC progression, this study leverages integrative multi-omics and machine learning to uncover TME-associated prognostic biomarkers, paving the way for more personalized therapeutic interventions.Employing a rigorous multi-omics approach, this study analyzed single-cell RNA sequencing (scRNA-seq) data from OC and normal tissue samples, including high-grade serous OC (HGSOC) from the Gene Expression Omnibus (GEO: GSE184880) and The Cancer Genome Atlas (TCGA) OC cohort, utilizing the Seurat package to annotate 700 TME-related genes. A prognostic model was developed using the least absolute shrinkage and selection operator (LASSO) regression and independently validated against similarly composed HGSOC datasets. Comprehensive gene expression and immune cell infiltration analyses were conducted, employing advanced algorithms like xCell to delineate the immune landscape of HGSOC.Our investigation unveiled distinctive immune cell infiltration patterns and gene expression profiles within the TME of HGSOC. Notably, the prevalence of exhausted CD8+ T cells in high-risk patient samples emerged as a critical finding, underscoring the dualistic nature of the immune response in OC. The developed prognostic model, incorporating immune cell markers, exhibited robust predictive accuracy for patient outcomes, showing significant correlations with immunotherapy responses and drug sensitivities.This study presents a groundbreaking exploration of the OC TME, offering vital insights into its molecular intricacies. By systematically deciphering the TME-associated gene signatures, the research illuminates the potential of these biomarkers in refining patient prognosis and guiding treatment strategies. Our findings underscore the necessity for personalized medicine in OC treatment, potentially enhancing patient survival rates and quality of life. This study marks a significant stride in understanding and combatting the complexities of OC.2024 AME Publishing Company. All rights reserved.

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出版当年[2023]版：

大类 | 4 区医学

小类 | 4 区肿瘤学

最新[2023]版：

大类 | 4 区医学

小类 | 4 区肿瘤学

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第一作者机构： [1]Department of Gynecology, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China.

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