Issue
Copyright (c) 2025 SANİYE ELVAN ÖZTÜRK
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The undersigned hereby assign all rights, included but not limited to copyright, for this manuscript to CMB Association upon its submission for consideration to publication on Cellular and Molecular Biology. The rights assigned include, but are not limited to, the sole and exclusive rights to license, sell, subsequently assign, derive, distribute, display and reproduce this manuscript, in whole or in part, in any format, electronic or otherwise, including those in existence at the time this agreement was signed. The authors hereby warrant that they have not granted or assigned, and shall not grant or assign, the aforementioned rights to any other person, firm, organization, or other entity. All rights are automatically restored to authors if this manuscript is not accepted for publication.Shared gene functions in autoimmune diseases identified via integrated bioinformatics analysis
Corresponding Author(s) : Saniye Elvan Öztürk
Cellular and Molecular Biology,
Vol. 71 No. 11: Issue 11
Abstract
Genetic transmission has minimal impact on autoimmune diseases compared to environmental factors. In conclusion, studies now focus on gene expression changes for diagnosis and treatment. This study used stringent cut-off values (p < 0.05, Log2(FoldChange) > 5) to monitor gene expression changes. Gene Ontology and Reactome Pathways enrichment analyses were performed, and interactions between differentially expressed genes (DEGs) were analyzed using the STRING database. Biomarker candidate genes were investigated across ten autoimmune diseases. Non-coding genes, particularly LINC01833 (upregulated in four diseases) and CD177 (upregulated in three diseases), were significant. The VCX, SLC, and KLK families were notably upregulated. Non-coding RNAs RNU5D-1 and MIR3648-1 were shared in two disease groups. Among shared genes between multiple sclerosis (MS) and ankylosing spondylitis (AS), ALPL, CHI3L1, HBM, MYL4, and PI3 were prominently downregulated. This study highlights the identification of differentially expressed signature genes across ten autoimmune diseases with high significance cut-offs (p < 0.05, Log2(FoldChange) > 5), suggesting their potential as significant targets for diagnosis and treatment.
Keywords
Download Citation
Endnote/Zotero/Mendeley (RIS)BibTeX
