Objectives Interstitial lung disease is a significant comorbidity and the leading cause of mortality in patients with systemic sclerosis. Transcriptomic data of systemic sclerosis-associated interstitial lung disease (SSc-ILD) were analysed to evaluate the salient molecular and cellular signatures in comparison with those in related pulmonary diseases and to identify the key driver genes and target molecules in the disease module.

Methods A transcriptomic dataset of lung tissues from patients with SSc-ILD (n=52), idiopathic pulmonary fibrosis (IPF) (n=549), non-specific interstitial pneumonia (n=49) and pulmonary arterial hypertension (n=81) and from normal healthy controls (n=331) was subjected to filtration of differentially expressed genes, functional enrichment analysis, network-based key driver analysis and kernel-based diffusion scoring. The association of enriched pathways with clinical parameters was evaluated in patients with SSc-ILD.

Results SSc-ILD shared key pathogenic pathways with other fibrosing pulmonary diseases but was distinguishable in some pathological processes. SSc-ILD showed general similarity with IPF in molecular and cellular signatures but stronger signals for myofibroblasts, which in SSc-ILD were in a senescent and apoptosis-resistant state. The p53 signalling pathway was the most enriched signature in lung tissues and lung fibroblasts of SSc-ILD, and was significantly correlated with carbon monoxide diffusing capacity of lung, cellular senescence and apoptosis. EEF2, EFF2K, PHKG2, VCAM1, PRKACB, ITGA4, CDK1, CDK2, FN1 and HDAC1 were key regulators with high diffusion scores in the disease module.

Conclusions Integrative transcriptomic analysis of lung tissues revealed key signatures of fibrosis in SSc-ILD. A network-based Bayesian approach provides deep insights into key regulatory genes and molecular targets applicable to treating SSc-ILD.