Endometrial Cancer

Endometrial cancer (EC) is a commonly occurred malignancy of the female reproductive tract that arises from the uterus lining. While the occurrence of the disease varies widely among countries, EC has become the most common female cancer in areas like North America, Europe, and middle-income developing countries such as South Africa and India. EC is classified into two groups, type I and II endometrioid tumors. Type I is estrogen-dependent, obesity is the major risk factor, and it has a favourable prognosis; in contrast, type II tumors occur in elderly, non-obese women, are estrogen-independent and exhibit worse outcomes.

Computational Approaches

• Identify differentially expressed genes in EC by analysing datasets from the NCBI-GEO data repository.
• Perform gene overrepresentation analyses considering adjusted p-value < 0.05 as significant for all the enrichment analyses.
• Perform the gene functional annotations through gene ontology (GO) and KEGG pathway enrichment analysis using DAVID.
• Employ the statistical method Limma to perform differential analysis of transcriptomes of EC downloaded from the Gene Expression Omnibus.
• Construct protein-protein interaction networks of the proteins encoded by the common differentially expressed genes (DEGs) of EC.
• Identify hub proteins from the protein-protein interactions (PPI) network analysis using the STRING database.
• Analyze the PPI network using the NetworkAnalyst tool.
• Conduct survival analysis on the hub proteins to assess the values of interest using SurvExpress.
• Cross-validation of differential expression and survival analysis of the hub genes, using independent RNA-Sequencing datasets of EC from the cancer genome atlas (TCGA) via SurvExpress.

Several key hub proteins (CDC20, EZH2, TOP2A, SPTBN1) have been detected, based on a topological analysis of the PPI network which play vital roles in the progression and regulation of EC. Dysregulated genes can be involved in several altered molecular pathways, including protein digestion and absorption, cysteine and methionine metabolism, ECM-receptor interaction, and drug metabolism.

We are dedicated to uncovering the mechanisms of endometrial cancer behavior, utilizing deep sequencing technologies, advanced biostatistical approaches, and computational analysis methods. We are also interested in how the machine learning-based integration of different datasets can aid in the discovery of new cancer subgroups and biomarkers. In addition, 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. Md FoyzurRahman, et al. A bioinformatics approach to decode core genes and molecular pathways shared by breast cancer and endometrial cancer. Informatics in Medicine Unlocked. 2019.