Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia; however, early diagnosis of the disease is challenging. Research suggests that biomarkers found in blood, such as microRNAs (miRNA), may be promising for AD diagnostics. Experimental data on miRNA–target interactions (MTI) associated with AD are scattered across databases and publications, thus making the identification of promising miRNA biomarkers for AD difficult. In response to this, a list of experimentally validated AD-associated MTIs was obtained from miRTarBase. Cytoscape was used to create a visual MTI network. STRING software was used for protein–protein interaction analysis and mirPath was used for pathway enrichment analysis. Several targets regulated by multiple miRNAs were identified, including: BACE1, APP, NCSTN, SP1, SIRT1, and PTEN. The miRNA with the highest numbers of interactions in the network were: miR-9, miR-16, miR-34a, miR-106a, miR-107, miR-125b, miR-146, and miR-181c. The analysis revealed seven subnetworks, representing disease modules which have a potential for further biomarker development. The obtained MTI network is not yet complete, and additional studies are needed for the comprehensive understanding of the AD-associated miRNA targetome.
Leukemias are a group of malignancies of the blood and bone marrow. Multiple types of leukemia are known, however reliable treatments have not been developed for most leukemia types. Furthermore, even relatively reliable treatments can result in relapses. MicroRNAs (miRNAs) are a class of short, noncoding RNAs responsible for epigenetic regulation of gene expression and have been proposed as a source of potential novel therapeutic targets for leukemias. In order to identify central miRNAs for leukemia, we conducted data synthesis using two databases: miRTarBase and DISNOR. A total of 137 unique miRNAs associated with 16 types of leukemia were retrieved from miRTarBase and 86 protein-coding genes associated with leukemia were retrieved from the DISNOR database. Based on these data, we formed a visual network of 248 miRNA-target interactions (MTI) between leukemia-associated genes and miRNAs associated with ≥4 leukemia types. We then manually reviewed the literature describing these 248 MTIs for interactions identified in leukemia studies. This manually curated data was then used to visualize a network of 64 MTIs identified in leukemia patients, cell lines and animal models. We also formed a visual network of miRNA-leukemia associations. Finally, we compiled leukemia clinical trials from the ClinicalTrials database. miRNAs with the highest number of MTIs were miR-125b-5p, miR-155-5p, miR-181a-5p and miR-19a-3p, while target genes with the highest number of MTIs were TP53, BCL2, KIT, ATM, RUNX1 and ABL1. The analysis of 248 MTIs revealed a large, highly interconnected network. Additionally, a large MTI subnetwork was present in the network visualized from manually reviewed data. The interconnectedness of the MTI subnetwork suggests that certain miRNAs represent central disease molecules for multiple leukemia types. Additional studies on miRNAs, their target genes and associated biological pathways are required to elucidate the therapeutic potential of miRNAs in leukemia.
Cancer and cardiovascular diseases (CVD) account for approximately 27.5 million deaths every year. While they share some common environmental risk factors, their shared genetic risk factors are not yet fully understood. The aim of the present study was to aggregate genetic risk factors associated with the comorbidity of cancer and CVDs. For this purpose, we: (1) created a catalog of genes associated with cancer and CVDs, (2) visualized retrieved data as a gene-disease network, and (3) performed a pathway enrichment analysis. We performed screening of PubMed database for literature reporting genetic risk factors in patients with both cancer and CVD. The gene-disease network was visualized using Cytoscape and the enrichment analysis was conducted using Enrichr software. We manually reviewed the 181 articles fitting the search criteria and included 13 articles in the study. Data visualization revealed a highly interconnected network containing a single subnetwork with 56 nodes and 146 edges. Genes in the network with the highest number of disease interactions were JAK2, TTN, TET2, and ATM. The pathway enrichment analysis revealed that genes included in the study were significantly enriched in DNA damage repair (DDR) pathways, such as homologous recombination. The role of DDR mechanisms in the development of CVDs has been studied in previously published research; however, additional functional studies are required to elucidate their contribution to the pathophysiology to CVDs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.