# GeneHarmony: A Knowledge-Based Tool for Biomarker Discovery in Disease: Sjögren’s Disease vs. Rheumatoid Arthritis and Systemic Lupus Erythematosus

## TL;DR

This paper introduces GeneHarmony, a tool that helps find shared genes and pathways in autoimmune diseases like Sjögren’s Disease, Rheumatoid Arthritis, and Systemic Lupus Erythematosus.

## Contribution

The novel contribution is the development of GeneHarmony, a knowledge-based tool for analyzing gene-disease associations and identifying biomarkers across diseases.

## Key findings

- Seven genes (CD4, CD8A, IL6, IL17A, TNFS13B, TNF, TRIM21) were found common to Sjögren’s Disease, Rheumatoid Arthritis, and Systemic Lupus Erythematosus.
- STRINGv12 analysis identified significant KEGG pathways related to interleukin and cytokine signaling, and immune system processes.
- The tool simplifies data mining for gene associations and expression across diseases using databases like Diseasesv2.0 and GTExv8.

## Abstract

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 (n = 12 genes), RA (n = 231 genes), and SLE (n = 137 genes). We found seven genes in common, namely, CD4, CD8A, IL6, IL17A, TNFS13B, TNF, and TRIM21. 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.