Uterine Cancer

Uterine cancer includes two types of cancer: endometrial cancer and uterine sarcoma. Uterine cancer symptoms include bleeding between periods or after menopause. Uterine cancer treatment often consists of a hysterectomy to remove the uterus (womb). Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. Endometrial cancer has more frequent mutations in the PI(3)K/AKT pathway than any other tumour type studied by The Cancer Genome Atlas (TCGA) so far. In addition, endometrial carcinomas have novel exclusivity of KRAS and CTNNB1 mutations and a distinct mechanism of pathway activation.

Data Sources

• Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/)
• Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp)
• The Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david.ncifcrf.gov/)
• Search Tool for the Retrieval of Interacting Genes (STRING, https://string-db.org/)

Computational Approaches

• Use microarray analysis to identify differentially expressed genes (DEGs) from samples.
• Use PANTHER, DAVID and Metascape to perform gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and cBioPortal for progesterone receptor (PGR) coexpression analysis.
• Use Gene Expression Omnibus (GEO) microarray for validation.
• Performe the protein-protein interaction network (PPI) and modular analyses using Metascape and Cytoscape.
• Performe further validation by real-time polymerase chain reaction (RT-PCR).

Lipid metabolism, immune system and inflammation, extracellular environment-related processes and pathways account for a significant portion of the enriched terms. Using computational analyses, we can identify DEGs and determine comprehensive gene networks. We are trying to propose possible mechanisms of disease progress and identify therapeutic and prognostic targets in uterine cancer.

Why Choose Us?

CD ComputaBio utilizes microarray, deep sequencing platforms, advanced biostatistical and computational analyses methods to detect biological signals in highly dimensional and often noisy genomic data. We are also interested in how the machine learning-based integration of multi-omic datasets can aid in the discovery of new cancer subgroups and biomarkers.

Moreover, CD ComputaBio has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. Contact us for more service details.

Reference:

1. Li W, Wang S, Qiu C, Liu Z, Zhou Q, Kong D, Ma X, Jiang J. Comprehensive bioinformatics analysis of acquired progesterone resistance in endometrial cancer cell line. J Transl Med. 2019 Feb 27;17(1):58.