Abstract
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.
Highlights
Ovarian cancer is not a single disease and can be subdivided into at least five different histological subtypes with diverse identifiable risk factors, cellular origin, molecular compositions, clinical features, and treatment approaches (Prasetyanti and Medema, 2017)
There represents a significant difference in survival rate between the high mRNA expression-based stemness index (mRNAsi) group and the low mRNAsi group, and the Kaplan Meier survival curve shows that the low group enjoyed a lower survival probability (p = 0.03, Figure 2A)
We explore the characteristic of mRNAsi in ovary cancer and compare the expression of mRNAsi between cancer and normal samples
Summary
Ovarian cancer is not a single disease and can be subdivided into at least five different histological subtypes with diverse identifiable risk factors, cellular origin, molecular compositions, clinical features, and treatment approaches (Prasetyanti and Medema, 2017). Improving the genomic understanding of the histological subtypes of ovarian cancer has been an important goal for researchers. This goal can promote researchers to understand the risk factors associated with the disease and develop prevention and treatment strategies. Because ovarian cancer has many subtypes, it leads to strong tumor heterogeneity. Tumor heterogeneity is one of the characteristics of malignant tumors, that is, tumor tissue consists of cell populations with different expression profiles or biological functions, which will lead to differences in tumor growth rate, invasion and metastasis ability, drug sensitivity, and other aspects
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