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Updated: Nov 25, 2025
Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
Published on: June 16, 2020
Identifying early pulmonary arterial hypertension biomarkers in systemic sclerosis: machine learning on proteomics
Yasmina Bauer1,2, Simon de Bernard3, Peter Hickey4,5
1Galapagos GmbH, Basel, Switzerland.
Insights
A new eight-protein biomarker panel can help detect pulmonary arterial hypertension (PAH) in systemic sclerosis (SSc) patients early. This discovery aids in improved screening and understanding of PAH in SSc.
Area of Science:
- Biochemistry
- Biomarkers
- Proteomics
Background:
- Pulmonary arterial hypertension (PAH) is a severe complication of systemic sclerosis (SSc).
- Early detection of PAH in SSc patients is crucial for improved outcomes.
- Current screening methods can be improved with reliable blood-based biomarkers.
Purpose of the Study:
- To identify a proteomic biomarker signature for discriminating SSc patients with and without PAH.
- To validate the identified biomarker panel in an independent cohort.
- To explore potential mechanistic insights into early PAH pathogenesis in SSc.
Main Methods:
- Serum samples from SSc patients with and without PAH were analyzed using proteomic screening (313 proteins).
- Machine learning (Random Forest) was employed to identify a biomarker panel.
- The identified panel was validated in an independent SSc cohort.
Main Results:
- A novel panel of eight proteins (collagen IV, endostatin, IGFBP-2, IGFBP-7, MMP-2, neuropilin-1, NT-proBNP, RAGE) was identified.
- The panel effectively discriminated PAH from non-PH in SSc patients (AUC 0.741 in discovery, 81.1% accuracy in validation).
- The biomarker panel demonstrated good sensitivity and specificity in both cohorts.
Conclusions:
- An eight-protein biomarker panel shows potential for early PAH detection in SSc patients.
- This panel may offer novel insights into the pathogenesis of PAH in the context of SSc.
- Further research can validate and implement this panel for clinical screening.
Abstract:
Pulmonary arterial hypertension (PAH) is a devastating complication of systemic sclerosis (SSc). Screening for PAH in SSc has increased detection, allowed early treatment for PAH and improved patient outcomes. Blood-based biomarkers that reliably identify SSc patients at risk of PAH, or with early disease, would significantly improve screening, potentially leading to improved survival, and provide novel mechanistic insights into early disease. The main objective of this study was to identify a proteomic biomarker signature that could discriminate SSc patients with and without PAH using a machine learning approach and to validate the findings in an external cohort.Serum samples from patients with SSc and PAH (n=77) and SSc without pulmonary hypertension (non-PH) (n=80) were randomly selected from the clinical DETECT study and underwent proteomic screening using the Myriad RBM Discovery platform consisting of 313 proteins. Samples from an independent validation SSc cohort (PAH n=22 and non-PH n=22) were obtained from the University of Sheffield (Sheffield, UK).Random forest analysis identified a novel panel of eight proteins, comprising collagen IV, endostatin, insulin-like growth factor binding protein (IGFBP)-2, IGFBP-7, matrix metallopeptidase-2, neuropilin-1, N-terminal pro-brain natriuretic peptide and RAGE (receptor for advanced glycation end products), that discriminated PAH from non-PH in SSc patients in the DETECT Discovery Cohort (average area under the receiver operating characteristic curve 0.741, 65.1% sensitivity/69.0% specificity), which was reproduced in the Sheffield Confirmatory Cohort (81.1% accuracy, 77.3% sensitivity/86.5% specificity).This novel eight-protein biomarker panel has the potential to improve early detection of PAH in SSc patients and may provide novel insights into the pathogenesis of PAH in the context of SSc.
