Jing Sun

TTK, CDC25A, and ESPL1 as Prognostic Biomarkers for Endometrial Cancer

Objective. Endometrial cancer (EC) is one of the most common malignant gynaecological tumours worldwide. This study was aimed at identifying EC prognostic genes and investigating the molecular mechanisms of these genes in EC. Methods. Two mRNA datasets of EC were downloaded from the Gene Expression Omnibus (GEO). The GEO2R tool and Draw Venn Diagram were used to identify differentially expressed genes (DEGs) between normal endometrial tissues and EC tissues. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Next, the protein‐protein interactions (PPIs) of these DEGs were determined by the Search Tool for the Retrieval of Interacting Genes (STRING) tool and Cytoscape with Molecular Complex Detection (MCODE). Furthermore, Kaplan‐Meier survival analysis was performed by UALCAN to verify genes associated with significantly poor prognosis. Next, Gene Expression Profiling Interactive Analysis (GEPIA) was used to verify the expression levels of these selected genes. Additionally, a reanalysis of the KEGG pathways was performed to understand the potential biological functions of selected genes. Finally, the associations between these genes and clinical features were analysed based on TCGA cancer genomic datasets for EC. Results. In EC tissues, compared with normal endometrial tissues, 147 of 249 DEGs were upregulated and 102 were downregulated. A total of 64 upregulated genes were assembled into a PPI network. Next, 14 genes were found to be both associated with significantly poor prognosis and highly expressed in EC tissues. Reanalysis of the KEGG pathways found that three of these genes were enriched in the cell cycle pathway. TTK, CDC25A, and ESPL1 showed higher expression in cancers with late stage and higher tumour grade. Conclusion. In summary, through integrated bioinformatics approaches, we found three significant prognostic genes of EC, which might be potential therapeutic targets for EC patients.

Network Pharmacology‐Based and Clinically Relevant Prediction of the Potential Targets of Chinese Herbs in Ovarian Cancer Patients

Reports increasingly suggest that Chinese herbal medicine (CHM) has been used to treat ovarian cancer (OvCa) with a good curative effect; however, the molecular mechanisms underlying CHM are still unclear. In this retrospective study, we explored CHM’s molecular targets for the treatment of OvCa based on clinical data and network pharmacology. We used the Kaplan‐Meier method and Cox regression analysis to verify the survival rate of 202 patients with CHM‐treated OvCa. The association between CHM and survival time was analyzed by bivariate correlation. A target network of CHM active ingredients against OvCa was established via network pharmacology. Cox regression analysis showed that CHM is an independent favorable prognostic factor. The median survival time was 91 months in the CHM group and 65 months in the non‐CHM group. The survival time of FIGO stage III patients in the two groups was 91 months and 52 months, and the median survival period of FIOG stage IV patients was 60 months and 22 months, respectively (p < 0.001). Correlation analysis demonstrated that 12 herbs were closely associated with prognosis, especially in regard to the long‐term benefits. Bioinformatics analysis indicated that the anti‐OvCa activity of these 12 herbs occurs mainly through the regulation of apoptosis‐related protein expression, which promotes OvCa cell apoptosis and inhibits OvCa development. They also regulate the progress of OvCa treatment by promoting or inhibiting protein expression on the p53 signaling pathway and by inhibiting the NF‐κB signaling pathway by directly inhibiting NF‐κB.