Wang Zhihang, Yang Lili, Huang Zhenyu, Li Xuan, Xiao Juan, Qu Yinwei, Huang Lan, Wang Yan
Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
Department of Obstetrics, The First Hospital of Jilin University, Changchun, China.
Front Genet. 2022 Mar 14;13:861954. doi: 10.3389/fgene.2022.861954. eCollection 2022.
In this paper, high-grade serous ovarian cancer (HGSOC) is studied, which is the most common histological subtype of ovarian cancer. We use a new analytical procedure to combine the bulk RNA-Seq sample for ovarian cancer, mRNA expression-based stemness index (mRNAsi), and single-cell data for ovarian cancer. Through integrating bulk RNA-Seq sample of cancer samples from TCGA, UCSC Xena and single-cell RNA-Seq (scRNA-Seq) data of HGSOC from GEO, and performing a series of computational analyses on them, we identify stemness markers and survival-related markers, explore stem cell populations in ovarian cancer, and provide potential treatment recommendation. As a result, 171 key genes for capturing stem cell characteristics are screened and one vital cancer stem cell subpopulation is identified. Through further analysis of these key genes and cancer stem cell subpopulation, more critical genes can be obtained as LCP2, FCGR3A, COL1A1, COL1A2, MT-CYB, CCT5, and PAPPA, are closely associated with ovarian cancer. So these genes have the potential to be used as prognostic biomarkers for ovarian cancer.
在本文中，我们研究了高级别浆液性卵巢癌（HGSOC），它是卵巢癌最常见的组织学亚型。我们使用一种新的分析程序，将卵巢癌的批量RNA测序样本、基于mRNA表达的干性指数（mRNAsi）和卵巢癌的单细胞数据相结合。通过整合来自TCGA、UCSC Xena的癌症样本批量RNA测序样本以及来自GEO的HGSOC单细胞RNA测序（scRNA-Seq）数据，并对其进行一系列计算分析，我们识别出干性标志物和生存相关标志物，探索卵巢癌中的干细胞群体，并提供潜在的治疗建议。结果，筛选出171个捕获干细胞特征的关键基因，并鉴定出一个重要的癌症干细胞亚群。通过对这些关键基因和癌症干细胞亚群的进一步分析，可以获得更多关键基因，如LCP2、FCGR3A、COL1A1、COL1A2、MT-CYB、CCT5和PAPPA，它们与卵巢癌密切相关。因此，这些基因有潜力用作卵巢癌的预后生物标志物。
