Single-cell RNA sequencing in ovarian cancer: Current progress and future prospects

• Published in: Progress in biophysics and molecular biology Vol. 195; pp. 100 - 129
• Main Author: Zhi-Xiong, Chong
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.03.2025
• Subjects: Animals; Female; Heterogeneity; Humans; ovarian cancer; Ovarian Neoplasms - genetics; Ovarian Neoplasms - pathology; ScRNA-seq; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Treatment resistance; Tumor Microenvironment - genetics; tumour microenvironment; ScRNA-seq; Heterogeneity; Treatment resistance; tumour microenvironment; ovarian cancer
• ISSN: 0079-6107; 1873-1732; 1873-1732
• DOI: 10.1016/j.pbiomolbio.2025.01.002

Ovarian cancer is one of the most prevalent gynaecological malignancies. The rapid development of single-cell RNA sequencing (scRNA-seq) has allowed scientists to use this technique to study ovarian cancer development, heterogeneity, and tumour environment. Although multiple original research articles have reported the use of scRNA-seq in understanding ovarian cancer and how therapy resistance occurs, there is a lack of a comprehensive review that could summarize the findings from multiple studies. Therefore, this review aimed to fill this gap by comparing and summarizing the results from different studies that have used scRNA-seq in understanding ovarian cancer development, heterogeneity, tumour microenvironment, and treatment resistance. This review will begin with an overview of scRNA-seq workflow, followed by a discussion of various applications of scRNA-seq in studying ovarian cancer. Next, the limitations and future directions of scRNA-seq in ovarian cancer research will be presented. [Display omitted] •ScRNA-seq is a powerful tool for studying ovarian tumour development, cancer heterogeneity, tumour microenvironment, and the mechanism of treatment resistance in ovarian cancer.•Over 50 original research articles and at least three trials have used scRNA-seq to understand ovarian cancer progression and to identify potential biomarkers and therapeutic targets in this cancer.•Integrating scRNA-seq with bulk RNA-seq, spatial transcriptomic analysis, and an advanced 3D-mass spectrometry technique can help improve our understanding of how the molecular landscape of ovarian tumour changes dynamically as the disease progresses.