Stem Cell-Associated Signatures Help to Predict Diagnosis and Prognosis in Ovarian Serous Cystadenocarcinoma

Abstract

Ovarian serous cystadenocarcinoma (OV) is a fatal gynecologic cancer with a five-year survival rate of only 46%. Resistance to platinum-based chemotherapy is a prevalent factor in OV patients, leading to increased mortality. The platinum resistance in OV is driven by transcriptome heterogeneity and tumor heterogeneity. Studies have indicated that ovarian cancer stem cells (OCSCs), which are chemoresistant and help in disease recurrence, are enriched by platinum-based chemotherapy. Stem cells have a significant influence on the OV progression and prognosis of OV patients and are key pathology mediators of OV. However, the molecular mechanisms and targets of OV have not yet been fully understood. In this study, systematic research based on the TCGA-OV dataset was conducted for the identification and construction of key stem cell-related diagnostic and prognostic models for the development of multigene markers of OV. A six-gene diagnostic and prognostic model (C19orf33, CBX2, CSMD1, INSRR, PRLR, and SLC38A4) was developed based on the differentially expressed stem cell-related gene model, which can act as a potent diagnostic biomarker and can characterize the clinicopathological properties of OV. The key genes related to stem cells were identified by screening the genes differentially expressed in OV and control samples. The mRNA-miRNA-TF molecular network for the six-gene model was constructed, and the potential biological significance of this molecular model and its impact on the infiltration of immune cells in the OV tumor microenvironment were elucidated. The differences in immune infiltration and stem cell-related biological processes were determined using gene set variation analysis (GSVA) and single-sample gene set enrichment analysis (ssGSEA) for the selection of molecular treatment options and providing a reference for elucidating the posttranscriptional regulatory mechanisms in OV.