Identification of Novel Biomarkers and Potential Therapeutic Targets for Systemic Sclerosis: An Integrated Analysis of Plasma Proteome-Wide Mendelian Randomization and Transcriptome

Our plain-English summary of Identification of Novel Biomarkers and Potential Therapeutic Targets for Systemic Sclerosis: An Integrated Analysis of Plasma Proteome-Wide Mendelian Randomization and Transcriptome, by H. Li, Q. Li, X. Chen, et al (Journal of Inflammation Research, 2025).

The study

• Authors: H. Li, Q. Li, X. Chen, L. Mo, Y. Wang, X. Liu, X. Wang, Z. Qu, J. Wang, Y. Li
• Journal: Journal of Inflammation Research
• Published: 2025;18:17561-17588
• Read it: PubMed · Publisher · Free full text

Abstract

Background: Systemic sclerosis (SSc) is a complex autoimmune connective tissue disease. This study aimed to identify novel biomarkers for SSc through an integrated analysis of plasma proteome-wide Mendelian randomization (MR) and transcriptome, as well as to explore the potential mechanisms.

Methods: The data used were obtained from public databases. Initially, key plasma proteins causally associated with SSc were identified through a two-sample MR analysis. Subsequently, based on the key diseases related to both key plasma proteins (genes) and key drugs targeting these proteins (genes), phenotype scanning was conducted to predict potential adverse side effects of key plasma proteins (genes). Single-cell RNA sequencing (scRNA-seq) analysis was performed to identify key cell types in GSE138669 dataset. Differentially expressed genes (DEGs) within key cell types in SSc were intersected with genes encoding key plasma proteins to obtain candidate biomarkers, whose functions were subsequently explored. By analyzing candidate biomarker expression in GSE138669 and GSE181549 datasets, the biomarkers were identified. Further exploration included regulatory network, cellular heterogeneity, and cell trajectory analyses.

Results: Initially, 106 plasma proteins (corresponding to 104 genes) were identified. It was revealed that targeting 12 key plasma proteins (like CD40LG) for treating SSc might lead to side effects related to specific key diseases (like mesothelioma). After recognizing epithelial cells and fibroblasts as key cell types, 8 candidate biomarkers associated with pathways like "proteasome" were identified. Notably, CCL19 and LOXL2 were identified as biomarkers, which exhibited elevated expression in SSc. Regulatory elements such as FOXL1 and hsa-miR-5001-5p were predicted to target biomarkers. Remarkably, differentiation stages of key cell type with heterogeneity and the biomarker expression patterns across these stages might be associated with SSc progression.

Conclusion: CCL19 and LOXL2 were identified as novel biomarkers for SSc, providing insights into the exploration of the disease's pathogenesis and the development of new therapeutic targets.

© 2025 Li et al.

In plain English

This study did not examine asbestos exposure. It used public genetic and gene-activity data to look for blood proteins linked to systemic sclerosis (scleroderma), an autoimmune disease that scars the skin and internal organs. The authors used Mendelian randomization, which uses inherited gene variants to estimate cause-and-effect links, in a European-descent dataset of 302 people with systemic sclerosis and 213,145 controls. They named 106 plasma proteins as key proteins associated with the disease. A second genetic screen flagged 12 of these proteins where a drug aimed at them might cause side effects. For example, CD40LG was a risk factor for systemic sclerosis but a protective factor for mesothelioma. The authors suggested that targeting it might trigger mesothelioma-related effects. Skin-cell data narrowed the list to 8 candidates. Two of them, CCL19 and LOXL2, were higher in systemic sclerosis skin in both skin datasets, in skin from five patients, and in mouse models. The authors concluded these are potential markers and treatment targets. They noted the small patient numbers and European-only data, and that the side-effect screen covered only 56 diseases.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Bioinformatic analysis: two-sample Mendelian randomization using public genetic data, combined with single-cell and bulk RNA sequencing datasets of skin, with laboratory checks in human skin samples and in mouse models
• Who was studied: FinnGen genetic dataset of individuals of European descent: 302 systemic sclerosis cases and 213,145 controls. Also skin datasets GSE138669 (12 SSc, 10 healthy samples) and GSE181549 (113 SSc, 44 healthy samples), skin from five SSc patients and matched healthy controls in Xi'an, China, and 15 male ICR mice
• Exposure: Genetically predicted levels of plasma proteins (no asbestos or other occupational exposure was studied)
• Main results:
  • 106 plasma proteins (corresponding to 104 genes) were named key plasma proteins causally associated with systemic sclerosis after all tests
  • In the phenotype scan, CD40LG was a risk factor for systemic sclerosis but a protective factor for mesothelioma. The authors suggested targeting it in systemic sclerosis may trigger mesothelioma-related adverse effects
  • Intersecting key plasma protein genes with genes differently expressed in skin epithelial cells and fibroblasts gave 8 candidate biomarkers
  • CCL19 and LOXL2 were higher in the systemic sclerosis group in both skin datasets (p less than 0.001)
  • In human skin, the proportion of positively stained area for CCL19 and LOXL2 was higher in systemic sclerosis patients than in controls
  • In mice (n = 5 per group), CCL19 and LOXL2 mRNA levels were higher in bleomycin- and HClO-induced models than in normal controls
• The authors' conclusion: The authors concluded that CCL19 and LOXL2 have potential roles as molecular markers and therapeutic targets in skin fibrosis related to systemic sclerosis
• Limitations the authors state:
  • Genetic data came only from European populations, limiting generalizability
  • Only 302 systemic sclerosis patients were included, a small sample for Mendelian randomization
  • Focusing on fibroblasts and epithelial cells likely biased the selected targets
  • Validation used a limited number of clinical samples
  • The side-effect phenotype scan covered only 56 diseases, and the predicted risks require experimental validation

Topics

Medical terms in this study, explained in our Lexicon: Biomarker, Mesothelioma, Fibroblast.

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