Beckman Micaela F, Alexander Adam, Mougeot Jean-Luc C, Bahrani Mougeot Farah
Translational Research Laboratories, Department of Oral Medicine/Oral & Maxillofacial Surgery and Cannon Research Center, Atrium Health Carolinas Medical Center, Charlotte, NC 28203, USA.
Otolaryngology/Head and Neck Surgery, Wake Forest University School of Medicine, Winston-Salem, NC 27110, USA.
Int J Mol Sci. 2025 Jul 2;26(13):6379. doi: 10.3390/ijms26136379.
Sjögren's Disease (SjD), Rheumatoid Arthritis (RA), and Systemic Lupus Erythematosus (SLE) are autoimmune diseases with overlapping genetic features, yet the etiologies of these diseases are poorly understood. Using these rheumatic diseases as an example of proof of concept, our aim was to develop a tool that simplifies analysis of gene-disease associations applicable to any disease and to perform comparisons. This tool is meant to provide insights into associated gene symbols and gene expression data to identify candidate biomarkers in common among these diseases. The Diseasesv2.0 and GTExv8 databases were utilized for data collection, providing searchable disease names, affiliated gene symbols, confidence scores (ranging from 0 to 5, with 5 being the most confident), and gene expression across the panel of 54 tissue types present in GTExv8. Data infrastructure was established on a Postgres database using Plotlyv5.17.0 and Streamlitv1.27.2 Python packages. The resulting database was used to investigate the genetic associations among SjD, RA, and SLE, including confidence scores from 2.50 to 5.00. STRINGv12 analysis determined significant pathways (FDR < 0.05). Analysis using our tool revealed the following refined gene associations for each disease: SjD based on 'Sjogren' search term ( = 12 genes), RA ( = 231 genes), and SLE ( = 137 genes). We found seven genes in common, namely, , , , , , , and . With the exception of IL17A, these genes were expressed in tissue types known or suggested to be affected by SjD. STRINGv12 determined significant KEGG pathways involving interleukin signaling, cytokine signaling, and the immune system. We developed a tool that simplifies the data mining process, allowing users to search for diseases of interest and view common gene associations and gene expression. Some of the genes identified through our tool may be further explored to better understand SjD pathogenesis and systemic impact.
干燥综合征（SjD）、类风湿关节炎（RA）和系统性红斑狼疮（SLE）是具有重叠遗传特征的自身免疫性疾病，然而这些疾病的病因仍知之甚少。以这些风湿性疾病为例进行概念验证，我们的目标是开发一种工具，简化适用于任何疾病的基因 - 疾病关联分析并进行比较。该工具旨在提供相关基因符号和基因表达数据的见解，以识别这些疾病中共同的候选生物标志物。利用Diseasesv2.0和GTExv8数据库进行数据收集，提供可搜索的疾病名称、相关基因符号、置信度得分（范围从0到5，5为最置信）以及GTExv8中54种组织类型的基因表达。使用Plotlyv5.17.0和Streamlitv1.27.2 Python包在Postgres数据库上建立数据基础设施。所得数据库用于研究SjD、RA和SLE之间的遗传关联，包括2.50至5.00的置信度得分。STRINGv12分析确定了显著通路（FDR < 0.05）。使用我们的工具进行分析揭示了每种疾病的以下细化基因关联：基于“Sjogren”搜索词的SjD（ = 12个基因）、RA（ = 231个基因）和SLE（ = 137个基因）。我们发现了七个共同基因，即 、 、 、 、 、 和 。除IL17A外，这些基因在已知或提示受SjD影响的组织类型中表达。STRINGv12确定了涉及白细胞介素信号传导、细胞因子信号传导和免疫系统的显著KEGG通路。我们开发了一种简化数据挖掘过程的工具，允许用户搜索感兴趣的疾病并查看常见基因关联和基因表达。通过我们的工具鉴定出的一些基因可能需要进一步探索，以更好地理解SjD的发病机制和全身影响。
