Comparative analysis between high-grade serous ovarian cancer and healthy ovarian tissues using single-cell RNA sequencing

IntroductionHigh-grade serous ovarian cancer (HGSOC) is the most common histological subtype of ovarian cancer, and is associated with high mortality rates.MethodsIn this study, we analyzed specific cell subpopulations and compared different gene functions between healthy ovarian and ovarian cancer...

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Bibliographic Details
Main Authors: Xiao Zhang, Shihao Hong, Chengying Yu, Xiaozhong Shen, Fangying Sun, Jianhua Yang
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-04-01
Series:Frontiers in Oncology
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Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2023.1148628/full
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Summary:IntroductionHigh-grade serous ovarian cancer (HGSOC) is the most common histological subtype of ovarian cancer, and is associated with high mortality rates.MethodsIn this study, we analyzed specific cell subpopulations and compared different gene functions between healthy ovarian and ovarian cancer cells using single-cell RNA sequencing (ScRNA-seq). We delved deeper into the differences between healthy ovarian and ovarian cancer cells at different levels, and performed specific analysis on endothelial cells.ResultsWe obtained scRNA-seq data of 6867 and 17056 cells from healthy ovarian samples and ovarian cancer samples, respectively. The transcriptional profiles of the groups differed at various stages of ovarian cell development. A detailed comparison of the cell cycle, and cell communication of different groups, revealed significant differences between healthy ovarian and ovarian cancer cells. We also found that apoptosis-related genes, URI1, PAK2, PARP1, CLU and TIMP3, were highly expressed, while immune-related genes, UBB, RPL11, CAV1, NUPR1 and Hsp90ab1, were lowly expressed in ovarian cancer cells. The results of the ScRNA-seq were verified using qPCR.DiscussionOur findings revealed differences in function, gene expression and cell interaction patterns between ovarian cancer and healthy ovarian cell populations. These findings provide key insights on further research into the treatment of ovarian cancer.
ISSN:2234-943X