Abstract
Background: Interstitial lung diseases (ILDs), which encompass >200 diseases involving excess deposition of extracellular matrix (ECM) in the lung, have been extensively characterized by whole genome expression sequencing (transcriptomics). However, a consensus of molecular aberrations in ILD has yet to be determined. We aimed to identify a molecular signature of ILD subtypes through integration of data obtained from transcriptomic-profiled lung samples.
Methods: A literature search was conducted in two databases (MEDLINE and EMBASE) to identify 5,337 publications, which were then screened to keep 18 studies involving ILD transcriptomics. Microarray and RNA-seq data from these studies were obtained from the Gene Expression Omnibus and integrated via the Multivariate INTegration (MINT) framework.
Results: We identified 17 studies examining lung samples from subjects with the ILD subtype idiopathic pulmonary fibrosis (IPF). Gene expression data was integrated via MINT and used to develop an IPF classification model (Figure 1; 14 datasets) consisting of 30 genes (e.g. MMP7, COL3A1, CXCL14), which was validated on 4 test datasets with an AUC of 0.96. Similar models were developed for hypersensitivity pneumonitis (HP; 3 datasets; 99 genes) and systemic sclerosis-associated ILD (SSc-ILD; 2 datasets; 11 genes).
Conclusion: We derived molecular signatures of 3 ILD subtypes, which may be used to improve diagnostic and therapeutic approaches.
Footnotes
Cite this article as ERJ Open Research 2022; 8: Suppl. 8, 84.
This article was presented at the 2022 ERS Lung Science Conference, in session “Poster Session 2”.
This is an ERS Lung Science Conference abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).
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