Systemic sclerosis-associated pulmonary arterial hypertension and pulmonary fibrosis: exploring biomarker discriminators with advanced omics in a Caucasian cohort.

INTRODUCTION

Systemic sclerosis (Scleroderma; SSc) is associated with high morbidity and mortality, particularly in patients with pulmonary arterial hypertension (SSc-PAH) and pulmonary fibrosis (SSc-PF). Effective risk stratification and treatment of SSc remains a significant challenge. This proof-of-concept study aimed to identify potential biomarkers capable of distinguishing between three SSc patient groups, defined by no pulmonary involvement (SSc-NLD; n=30), SSc-PAH (n=30), SSc-PF (n=30) compared to healthy controls (HC; n=30).

METHODS

The study employed Olink-based proteomics using the Cardiovascular II and Immuno-oncology panels, and untargeted metabolomic profiling using Ultra-high Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS), to discover distinct molecular signatures.

RESULTS

Proteomics analysis revealed significantly elevated levels of MCP-1, MCP-3, and MCP-4 in SSc-PF compared to all other groups. However, no robust discriminatory cytokines were identified for SSc-PAH or SSc-NLD. Validation of systemic MCP-1 and IL-6 by ELISA supported the proteomics findings. IL-33 levels were found to be reduced in the SSc-PAH group. Increased levels of pro-inflammatory sIL-6R were also identified in SSc-PAH and SSc-PF, indicating shared inflammatory pathways. Protein-protein interaction analyses demonstrated greater network complexity in SSc-PF, with pathway analysis suggesting overlapping biological mechanisms across pulmonary groups. Metabolomics analysis uncovered a unique panel of metabolites altered exclusively in SSc-PAH, including quinolinate, dimethylarginines, hydroxyasparagine and orotidine. In contrast, no metabolites were uniquely discriminatory for SSc-PF or SSc-NLD. Metabolite-metabolite interaction networks revealed nicotinate and nicotinamide metabolism as the more significantly enriched metabolic pathways in SSc-PAH. Correlation analyses identified distinct protein-metabolite profiles across groups. Of note is the loss of IL-33-related metabolic associations specific to SSc-PAH.

DISCUSSION: This study identified a candidate biomarker panel comprising three cytokines and ten metabolites capable of differentiating between SSc-PAH, SSc-PF, SSc-NLD, and HC. Biomarkers of SSc-PAH were linked to nicotinate and nicotinamide, as well as tryptophan metabolism, whereas those of SSc-PF reflected immune cell infiltration and fibrosis. These findings highlight the potential biomarker panels for diagnosis and targeted therapeutic development.