An integrated bioinformatic analysis of microarray datasets to identify biomarkers and miRNA-based regulatory networks in leishmaniasis.

Micro RNAs (miRNAs, miRs) and relevant networks might exert crucial functions during differential host cell infection by the different Leishmania species. Thus, a bioinformatic analysis of microarray datasets was developed to identify pivotal shared biomarkers and miRNA-based regulatory networks for Leishmaniasis. A transcriptomic analysis by employing a comprehensive set of gene expression profiling microarrays was conducted to identify the key genes and miRNAs relevant for Leishmania spp. infections. Accordingly, the gene expression profiles of healthy human controls were compared with those of individuals infected with Leishmania mexicana, L. major, L. donovani, and L. braziliensis. The enrichment analysis for datasets was conducted by utilizing EnrichR database, and Protein-Protein Interaction (PPI) network to identify the hub genes. The prognostic value of hub genes was assessed by using receiver operating characteristic (ROC) curves. Finally, the miRNAs that interact with the hub genes were identified using miRTarBase, miRWalk, TargetScan, and miRNet. Differentially expressed genes were identified between the groups compared in this study. These genes were significantly enriched in inflammatory responses, cytokine-mediated signaling pathways and granulocyte and neutrophil chemotaxis responses. The identification of hub genes of recruited datasets suggested that TNF, SOCS3, JUN, TNFAIP3, and CXCL9 may serve as potential infection biomarkers and could deserve value as prognostic biomarkers for leishmaniasis. Additionally, inferred data from miRWalk revealed a significant degree of interaction of a number of miRNAs (hsa-miR-8085, hsa-miR-4673, hsa-miR-4743-3p, hsa-miR-892c-3p, hsa-miR-4644, hsa-miR-671-5p, hsa-miR-7106-5p, hsa-miR-4267, hsa-miR-5196-5p, and hsa-miR-4252) with the majority of the hub genes, suggesting such miRNAs play a crucial role afterwards parasite infection. The hub genes and hub miRNAs identified in this study could be potentially suggested as therapeutic targets or biomarkers for the management of leishmaniasis.

A meta-analysis of microarray datasets to identify biological regulatory networks in Alzheimer's disease.

Background: Alzheimer's Disease (AD) is an age-related progressive neurodegenerative disorder characterized by mental deterioration, memory deficit, and multiple cognitive abnormalities, with an overall prevalence of ∼2% among industrialized countries. Although a proper diagnosis is not yet available, identification of miRNAs and mRNAs could offer valuable insights into the molecular pathways underlying AD's prognosis. Method: This study aims to utilize microarray bioinformatic analysis to identify potential biomarkers of AD, by analyzing six microarray datasets (GSE4757, GSE5281, GSE16759, GSE28146, GSE12685, and GSE1297) of AD patients, and control groups. Furthermore, this study conducted gene ontology, pathways analysis, and protein-protein interaction network to reveal major pathways linked to probable biological events. The datasets were meta-analyzed using bioinformatics tools, to identify significant differentially expressed genes (DEGs) and hub genes and their targeted miRNAs'. Results: According to the findings, CXCR4, TGFB1, ITGB1, MYH11, and SELE genes were identified as hub genes in this study. The analysis of DEGs using GO (gene ontology) revealed that these genes were significantly enriched in actin cytoskeleton regulation, ECM-receptor interaction, and hypertrophic cardiomyopathy. Eventually, hsa-mir-122-5p, hsa-mir-106a-5p, hsa-mir-27a-3p, hsa-mir16-5p, hsa-mir-145-5p, hsa-mir-12-5p, hsa-mir-128-3p, hsa-mir 3200-3p, hsa-mir-103a-3p, and hsa-mir-9-3p exhibited significant interactions with most of the hub genes. Conclusion: Overall, these genes can be considered as pivotal biomarkers for diagnosing the pathogenesis and molecular functions of AD.