A bioinformatics and system biology technique to identify candidate biomarkers and functional pathways among stress and depression

Stress means a lot of mental or physical strain. It can result from anything that provokes negative emotions, such as an idea or an experience. The World Health Organization cites depression as the leading nonfatal cause of disability worldwide. Major depressive disorder is one of the most common mental health illnesses that results in long-term damage (MDD). A major obstacle to the correct identification of stress and depression is the lack of good diagnostic biomarkers. By analyzing gene networks, we hoped to identify potential biomarkers of this disease. Samples of stress and depression were used to create the microarray and RNA-seq datasets GSE183648 and GSE101521. Through the use of R, it is possible to search for frequently occurring DEGs and filter preprocessed datasets. A Venn diagram illustrating the overlap between different sets of DEGs rules. When a group of DEGs is established, the PPIs network is built from them, with hub nodes selected according to topological features. In the PPI network, we found that ITGB3, ANGPTL4, ITGB1, PPARA, and RXRA serve as important hub genes. This research includes KEGG pathways as well. The development of the Gene Ontology, studies of gene-miRNA interactions, TF-gene interactions, and module analysis networks are all seen as important next steps in this field of study. Last but not least, various therapeutic possibilities have been found with identical DEGs as their primary base.