Unveiling the novel immune and molecular signatures of ovarian cancer: insights and innovations from single-cell sequencing

Ovarian cancer is a highly heterogeneous and lethal malignancy with limited treatment options. Over the past decade, single-cell sequencing has emerged as an advanced biological technology capable of decoding the landscape of ovarian cancer at the single-cell resolution. It operates at the level of...

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Bibliographic Details
Published in:Frontiers in immunology 2023-11, Vol.14, p.1288027
Main Authors: Li, Zhongkang, Gu, Haihan, Xu, Xiaotong, Tian, Yanpeng, Huang, Xianghua, Du, Yanfang
Format: Article
Language:English
Subjects:
Epigenome
Female
Humans
Multiomics
Organoids
ovarian cancer
Ovarian Neoplasms - genetics
single-cell sequencing
Technology
transcriptomics
tumor heterogeneity
tumor immunology
tumor microenvironment
Tumor Microenvironment - genetics
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Summary:Ovarian cancer is a highly heterogeneous and lethal malignancy with limited treatment options. Over the past decade, single-cell sequencing has emerged as an advanced biological technology capable of decoding the landscape of ovarian cancer at the single-cell resolution. It operates at the level of genes, transcriptomes, proteins, epigenomes, and metabolisms, providing detailed information that is distinct from bulk sequencing methods, which only offer average data for specific lesions. Single-cell sequencing technology provides detailed insights into the immune and molecular mechanisms underlying tumor occurrence, development, drug resistance, and immune escape. These insights can guide the development of innovative diagnostic markers, therapeutic strategies, and prognostic indicators. Overall, this review provides a comprehensive summary of the diverse applications of single-cell sequencing in ovarian cancer. It encompasses the identification and characterization of novel cell subpopulations, the elucidation of tumor heterogeneity, the investigation of the tumor microenvironment, the analysis of mechanisms underlying metastasis, and the integration of innovative approaches such as organoid models and multi-omics analysis.
ISSN:1664-3224
1664-3224
DOI:10.3389/fimmu.2023.1288027