SilicosisOmics: an integrated multi-omics platform for silicosis

Objective To address the challenges of fragmented multi-omics data and insufficient integrated analysis in silicosis research, we aim to construct a comprehensive data platform that supports systematic analysis of multi-level molecular alterations in fibrotic lung tissues and facilitates rapid identification of therapeutic targets. Methods We systematically integrated transcriptomic, single-cell transcriptomic, quantitative proteomic, post-translational modification-specific proteomic, and metabolomic data from human and mouse silicotic and control lung tissues. By leveraging bioinformatics tools including DESeq2, Seurat, and CellChat, we developed a web-based visualization platform enabling data visualization and interactive analysis. Results We successfully constructed SilicosisOmics (https://respir.pumc.edu.cn/SilicosisOmics/), a comprehensive multi-omics data platform for silicosis research. The platform integrated five types of omics data derived from both silicotic and non-diseased lung tissues across human and mouse species. SilicosisOmics provided not only online search functionality for static visualization of gene expression and single-cell clustering but also interactive analytical tools supporting user-customized analyses including differential gene expression, pathway enrichment, and cell-cell interaction studies. Conclusions The SilicosisOmics platform offers systematic multi-omics data resources and user-friendly analytical tools for silicosis and pulmonary fibrosis research, thereby facilitating the advancement of mechanistic studies, target discovery, and subsequent translational research.