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Query: UMLS:C0476089 (endometrial cancer)
 11,379 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Endometrial cancer (EC) is one of the most common gynecological cancer types worldwide. However, to the best of our knowledge, its underlying mechanisms remain unknown. The current study downloaded three mRNA and microRNA (miRNA) datasets of EC and normal tissue samples, GSE17025, GSE63678 and GSE35794, from the Gene Expression Omnibus to identify differentially expressed genes (DEGs) and miRNAs (DEMs) in EC tumor tissues. The DEGs and DEMs were then validated using data from The Cancer Genome Atlas and subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. STRING and Cytoscape were used to construct a protein-protein interaction network and the prognostic effects of the hub genes were analyzed. Finally, miRecords was used to predict DEM targets and an miRNA-gene network was constructed. A total of 160 DEGs were identified, of which 51 genes were highly expressed and 100 DEGs were discovered from the PPI network. Three overlapping genes between the DEGs and the DEM targets, BIRC5, CENPF and HJURP, were associated with significantly worse overall survival of patients with EC. A number of DEGs were enriched in cell cycle, human T-lymphotropic virus infection and cancer-associated pathways. A total of 20 DEMs and 29 miRNA gene pairs were identified. In conclusion, the identified DEGs, DEMs and pathways in EC may provide new insights into understanding the underlying molecular mechanisms that facilitate EC tumorigenesis and progression.
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PMID:Identification of key pathways and genes in endometrial cancer using bioinformatics analyses. 3065 45

Endometrial carcinoma(EC) is the most common cancer of female reproductive system, thus requiring for new effective biomarkers which could predict the onset of EC and poor prognosis. Our study integrated two GEO datasets(i.e.GSE63678, GSE17025) and TCGA(The Cancer Genome Atlas ) UCEC data to screen out 344 common differentially expressed genes(DEGs), which were further analyzed by GO(gene ontology) functions and KEGG(Kyoto Encyclopedia of Gene and Genome) pathways. KEGG analysis results showed these DEGs were mainly enriched in cell cycle, oocyte meiosis, cellular senescence, carbon metabolism and p53 signaling pathway. Top 20 hub genes with higher degree were selected from PPI(protein-protein interaction) network and 15 of them were associated with the prognosis of EC, that is, CCNB2, CDC20, BUB1B, UBE2C, AURKB, FOXM1, NCAPG, RRM2, TPX2, DLGAP5, CDCA8, CDC45, MKI67, BUB1, KIF2C. UBE2C(Ubiquitin Conjugating Enzyme E2 C) was chosen for further validation in TCGA cohort on mRNA level and in our patient samples on protein level by immunohistochemistry. UBE2C was significantly highly expressed in endometrial carcinoma, and its expression level was associated with advanced FIGO staging and poor prognosis. Cox risk model demonstrated high UBE2C expression was an independent risk factor. Somatic mutations, elevated copy number, DNA hypomethylation all contributed to its overexpression. Therefore, by combination of bioinformatics and experiment, our study provided a unique insight into the pathogenesis and molecular mechanisms underlying EC and discovered new biomarkers for early diagnosis and prognostic prediction. UBE2C could serve as a potential marker to predict poor prognosis and as a therapeutic target.
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PMID:Combining Bioinformatics and Experiments to Identify and Verify Key Genes with Prognostic Values in Endometrial Carcinoma. 3194 95

Ovarian cancer (OC) and endometrial cancer (EC) are two types of the most frequent gynecological malignancies worldwide. However, the prognosis of OC and EC patients remained gloomy. Therefore, there was still an urgent need to identify new biomarkers for early diagnosis and treatment of OC and EC. TCGA datasets were used to screen the KLHL14 expression levels in 18 different types of human cancers. TCGA datasets were also used to analyze the association between KLHL14 expression levels and OS/PFS in OC and EC. Human Protein Atlas was used to detected the KLHL14 protein levels in OC and EC. Kaplan-Meier plotter was used to evaluate the prognostic values of KLHL14 in Ovarian cancer. MAS 3.0 was used to perform GO and KEGG pathway analysis. STRING was used to perform PPI network. KLHL14 was specially expressed in OC and EC samples. Moreover, KLHL14 was found to be up-regulated in all stage of OC and EC samples. By analyzing Kaplan-Meier plotter and TCGA datasets, we found higher KLHL14 expression level was associated with shorter overall and progression-free survival in both OC and EC patients. Furthermore, GO and KEGG analysis of KLHL14 co-expressing genes indicated it played important roles in OC and EC progression. We for the first time reported KLHL14 was specially over-expressed in ovarian and endometrial cancer, up-regulation of KLHL14 was positively associated with worse outcome. Finally, we found knockdown of KLHL14 suppressed OC cell proliferation. KLHL14 could be a potential biomarker and therapy target for OC and EC.
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PMID:KLHL14, an ovarian and endometrial-specific gene, is over-expressed in ovarian and endometrial cancer. 3223 3