Proteomic biomarkers for survival in systemic sclerosis-associated pulmonary hypertension

BackgroundInterstitial lung disease (ILD) and pulmonary hypertension (PH) represent the major causes of mortality in systemic sclerosis (SSc). Patients with systemic sclerosis and combined PH and ILD (SSc-PH-ILD) generally have a poor prognosis. Predictors of survival and of potential benefit of treatment are lacking in patients with SSc-PH-ILD.ObjectiveTo identify specific plasma protein expression patterns associated with survival in patients with SSc-PH-ILD.Materials and methodsPost-hoc analysis of a prospective multicenter French study in patients with PH-ILD. An untargeted proteomic analysis using mass spectrometry was performed to identify plasma protein changes associated with long-term overall survival in patients with SSc-PH-ILD.ResultsThirty two patients were included in the analysis, of whom 13 died during follow-up (median survival: 76.5 months). At baseline, survivors had less severe hemodynamic impairment [pulmonary vascular resistance of 4.4 Wood Units (IQR 3-5.2) vs. 6.2 Wood Units (IQR 4.2-10.7)] and higher carbon monoxide diffusing capacity [median 39% (IQR 35-44%) vs. 25% (IQR 22-30.5%)], than the 13 patients who died. Seven proteins, associated with haemostasis and fibrosis, were differentially expressed according to patients' survival. In the survivor group, two proteins were increased (ADAMTS13, SERPIND1) and five were decreased (PTGDS, OLFM1, C7, IGFBP7, FBN1) compared to the non-survivor groups.ConclusionThe prognosis of SSc-PH-ILD patients is poor. This proteomic approach found 7 plasma proteins (involved in haemostasis and fibrosis pathways) associated with survival. These potential biomarkers may be good candidates to prognostic enrichment.

Automated summary

This study conducted a proteomic analysis on patients with SSc-PH-ILD and identified 7 plasma proteins associated with survival. These proteins are involved in the pathways of haemostasis and fibrosis. These potential biomarkers may be useful for predicting patient prognosis.