Yingsha Yao

Key genes and associated mechanisms of PCOS and EC comorbidity: A bioinformatics analysis

Endometrial cancer (EC) is a major reproductive system tumor and a common cancer in women. Polycystic ovary syndrome (PCOS) is one of the most prevalent female reproductive endocrine disorders. The incidence of EC is significantly higher in individuals with PCOS. This study seeks to elucidate the organic correlations and interaction mechanisms between the 2 diseases through series of exploration of key genes with a bioinformatics analysis. The PCOS sample data and the EC single-cell dataset were downloaded from the gene expression omnibus database. The EC sample data were retrieved from the cancer genome atlas public database. The random survival forest method was employed to identify key genes associated with the prognosis of PCOS and EC comorbidity. Corresponding analyses on functional pathway enrichment, regulatory networks, and immune micro-environment are conducted. From a bioinformatics perspective, the association and interaction mechanisms between PCOS and EC comorbidity were explored to provide research and development references for the prevention and control of PCOS and EC comorbidity. Five key genes associated with the prognosis of PCOS and EC comorbidity were identified using the random survival forest method. The identified genes are SYTL1, PARVG, ID4, IL1RN, and S100A9. The abnormal expression of these key genes has impacted various enrichment pathways, including the TGF-β signaling-pathway, motif regulatory networks (such as motif cisbp__M4556), and miRNA regulatory networks, which encompass genes such as ATM, BARD1 and BRCA1. Furthermore, these also influence the immune cell microenvironment, such as T cells regulatory. Collectively, these key genes play a significant role in the occurrence and progression of comorbidities through the pathways mentioned above. The dysregulation of key genes (SYTL1, PARVG, ID4, IL1RN, S100A9) in the context of PCOS-EC comorbidities, along with their associated enrichment pathways, including the TGF-β signaling-pathway and immune microenvironment, plays a significant role in the occurrence and progression of EC.

Four differentially expressed exosomal miRNAs as prognostic biomarkers and therapy targets in endometrial cancer: Bioinformatic analysis

Endometrial cancer (EC) is one of the most common gynecological malignancies worldwide. Accumulated evidence has demonstrated exosomes of cancer cells carry microRNAs (miRNAs) to nonmalignant cells to induce metastasis. Our study aimed to find possible biomarkers of EC. Data for miRNA expression related with exosome from EC patients were downloaded from The Cancer Genome Atlas database, and the miRNA expression profiles associated with exosomes of EC were downloaded from the National Center for Biotechnology Information. We used different algorithms to analyze the differential miRNA expression, infer the relative proportion of immune infiltrating cells, predict chemotherapy sensitivity, and comprehensively score each gene set to evaluate the potential biological function changes of different samples. The gene ontology analysis and Kyoto encyclopedia of genome genomics pathway analysis were performed for specific genes. A total of 13 differential miRNAs were identified, of which 4 were up-regulated. The 4 miRNAs, that is hsa-miR-17-3p, hsa-miR-99b-3p, hsa-miR-193a-5p, and hsa-miR-320d, were the hub exosomal miRNAs that were all closely related to the clinic phenotypes and prognosis of patients. This study preliminarily indicates that the 4 hub exosomal miRNAs (hsa-miR-17-3p, hsa-miR-99b-3p, hsa-miR-193a-5p, and hsa-miR-320d) could be used as prognostic biomarkers or therapy targets in EC. Further studies are required to make sure of their real feasibility and values in the EC clinic and the relative research.