# SLE-diseaseome: a comprehensive meta-collection of systemic lupus erythematosus relevant functional pathways

## TL;DR

The paper introduces SLE-diseaseome, a comprehensive collection of gene pathways relevant to systemic lupus erythematosus, to help understand disease heterogeneity and improve precision medicine.

## Contribution

The novel contribution is the creation of a robust, integrative multi-cohort gene set collection for SLE using multiple studies and pathway databases.

## Key findings

- SLE-diseaseome combines gene signatures from multiple sources to provide reliable disease-specific functional references.
- The resource enables better functional interpretation of molecular data in clinical studies for systemic lupus erythematosus.
- The pipeline and dataset are publicly available for further research and analysis.

## Abstract

Systemic lupus erythematosus patients exhibit a broad clinical spectrum of manifestations and suffer from high rates of treatment failure. These can be attributed to disease heterogeneity due to differentially dysregulated pathways. Precision medicine considering the individualized molecular disease driving mechanisms is a promising strategy to address challenges imposed by disease heterogeneity. Available patient blood transcriptome data coupled with pathway-based single-sample scoring approaches have been extensively employed to reveal molecular footprints of disease states and progression as well as delineate population heterogeneity. However, systemic understanding of pathways involved in disease pathogenesis remains lacking.

We created a SLE-diseaseome, an integrative multi-cohort collection of disease-relevant functional gene sets. This resource contains a comprehensive collection of disease-specific gene signatures combining knowledge from several pathway databases and signature sources robustly defined by integrating multiple studies. It offers reliable and extensive reference signatures in a disease-specific manner for functional interpretation of molecular data from clinical studies.

The code used to run the pipeline and the R object containing the SLE-diseaseome collection are available at https://github.com/dtordom/SLEDiseaseome.