Cross-disease analysis identifies the inflammatome as a transcriptional program of inflammation

Inflammation is a complex biological process that upregulates numerous genes compared to homeostasis. These transcriptional changes can dominate the statistical signal in differential expression analyses and confound the detection of disease-specific effects. Here, we exploit inflammatory signals in public clinical transcriptomic datasets to derive the inflammatome, a comprehensive set of 2,000 genes consistently upregulated in various inflammatory diseases vs. healthy controls. Within this set, we define the inflammation signature, a high-confidence subset of 100 genes capturing the most consistent changes. We demonstrate how these gene sets can be used to identify and filter inflammation-associated changes in gene expression and protein abundance. Additionally, we introduce a sample-wise inflammation score derived from the inflammation signature and show its correlation with clinical disease severity. To support broad usability, we provide these functionalities in a user-friendly Shiny app. This resource will enable users to assess the effect of inflammation in future transcriptomic and proteomic studies.