Influence of PCOS in Obese vs. Non-Obese women from Mesenchymal Progenitors Stem Cells and Other Endometrial Cells: An in silico biomarker discovery

Abstract

Polycystic ovary syndrome (PCOS) is endocrine system disease which affect women ages 18 to 44 where the women’s hormones are imbalance. Recently it has been reported to occur in early age. Alteration of normal gene expression in PCOS has shown negative effects on long-term health issues. PCOS has been the responsible factor for the infertility in women of reproductive age group. Early diagnosis and treatment can improve the women’s health suffering from PCOS. Earlier Studies shows correlation of PCOS upon insulin resistance with significant outcome, Current study shows the linkage between PCOS with obesity and non-obese patients. Gene expression datasets has been downloaded from GEO (control and PCOS affected patients). Normalization of the datasets were performed using R based on RMA and differentially expressed gene (DEG) were selected on the basis of p-value 0.05 followed by functional annotation of selected gene using Enrich R and DAVID. The DEGs were significantly related to PCOS with obesity and other risk factors involved in disease. The Gene Enrichment Analysis suggests alteration of genes and associated pathway in case of obesity. Current study provides a productive groundwork for specific biomarkers identification for the accurate diagnosis and efficient target for the treatment of PCOS.

Towards the early detection of ductal carcinoma (a common type of breast cancer) using biomarkers linked to the PPAR(γ) signaling pathway

“…The Database for Annotation, Visualization and Integrated Discovery (DAVID), an open-source tool for Gene Ontology (GO) annotation of differentially expressed genes is used for gene-gene annotation. GO terms are categorized into biological processes, cellular components and molecular function where biological process refers to complex changes on the granularity level of the cell which is mediated by one or more gene products, cellular component is a part of cell or its extracellular environment which may contain gene product and Molecular Function help us to understand the potential of the molecule to execute the function [23], which may be actively responsible in gene expression variation. Pathways in which the DEGs are involved were identified using KEGG (Kyoto Encyclopedia of Genes and Genomes).…”

Molecular docking analysis of stachydrine and sakuranetin with IL-6 and TNF-α in the context of inflammation

“…Transcriptional analysis of cell cycle regulation in several organisms has originated from the relation of genes in regulating the cell cycle [ 17 , 18 , 19 ]. Microarray-based studies have been considerably identified as remarkable biomarkers not limited to AD but in other disease complexities [ 20 , 21 , 22 ]. The present research objective was to identify the most suitable set of genes that helps in the progression of Alzheimer's Disease, utilizing Machine learning classifiers such as Bayes Net, Naïve Bayes, SMO/SVM, Logit Boost, Decision Table and J48.…”

Differential Gene Expression Analysis of Prostate Cancer for Biomarkers and Potential Drug Targets Identification

“…Microarray has shown to be promising method of choice, which allows the study of genes in an organism under different conditions within a single experimental setup. The analysis of such data is performed using Bioconductor in R environment, which as a matter of fact has become the standard in the field of biomarker discovery and target identification for the effective treatment (Desai et al, 2017).…”