Frontiers in pharmacology · 2025
Role of biomarker SOCS1 in peritoneal dialysis-associated peritoneal fibrosis and immune infiltration based on machine learning screening.
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Ma Xin, He Xiaona, Wang Yun, Grzegorzek Marcin, Long Wenjie, Chen Hongxi, Gao Fang, Mao Nan, Huang Xinyu
Аннотация
INTRODUCTION/OBJECTIVES: Chronic peritoneal dialysis (PD) induces peritoneal fibrosis through bioincompatible fluid exposure and inflammation. This study aimed to identify fibrosis biomarkers via bioinformatics, assess immune cell correlations, and validate diagnostic utility in clinical/animal models.
METHODS: We analyzed the GSE125498 peritoneal fluid transcriptome to identify differentially expressed genes (DEGs). Functional enrichment, protein interaction networks, and pathway analyses were performed. Three machine learning algorithms screened diagnostic markers, validated by ROC curves and nomogram modeling. Immune infiltration patterns were correlated with biomarkers. Clinical validation included SOCS-1/TGF-β1 quantification in 86 PD patients' effluent and a CKD rat fibrosis model to assess SOCS-1/TGF-β/Smad pathway interactions.
RESULTS: Four hub genes (PIM2, HSH2D, MYO3B, SOCS1; AUC = 0.914) were identified as PD fibrosis biomarkers. Monocyte infiltration increased significantly in long-term PD cohorts and inversely correlated with all biomarkers. SOCS1 exhibited positive associations with CD4+/CD8+ T cells and M1 macrophages, but negative correlations with resting mast cells and monocytes. Clinically, SOCS-1 levels in PD effluent showed non-linear temporal dynamics. Rat models confirmed SOCS-1 overexpression in fibrotic peritoneum and its functional link to TGF-β/Smad signaling.
CONCLUSION: SOCS-1 emerges as a novel biomarker for PD-related peritoneal fibrosis, with monocyte-mediated mechanisms playing a critical role. Animal studies implicate SOCS-1 in TGF-β/Smad-driven fibrogenesis. These findings provide mechanistic insights and translational tools for monitoring PD complications.
METHODS: We analyzed the GSE125498 peritoneal fluid transcriptome to identify differentially expressed genes (DEGs). Functional enrichment, protein interaction networks, and pathway analyses were performed. Three machine learning algorithms screened diagnostic markers, validated by ROC curves and nomogram modeling. Immune infiltration patterns were correlated with biomarkers. Clinical validation included SOCS-1/TGF-β1 quantification in 86 PD patients' effluent and a CKD rat fibrosis model to assess SOCS-1/TGF-β/Smad pathway interactions.
RESULTS: Four hub genes (PIM2, HSH2D, MYO3B, SOCS1; AUC = 0.914) were identified as PD fibrosis biomarkers. Monocyte infiltration increased significantly in long-term PD cohorts and inversely correlated with all biomarkers. SOCS1 exhibited positive associations with CD4+/CD8+ T cells and M1 macrophages, but negative correlations with resting mast cells and monocytes. Clinically, SOCS-1 levels in PD effluent showed non-linear temporal dynamics. Rat models confirmed SOCS-1 overexpression in fibrotic peritoneum and its functional link to TGF-β/Smad signaling.
CONCLUSION: SOCS-1 emerges as a novel biomarker for PD-related peritoneal fibrosis, with monocyte-mediated mechanisms playing a critical role. Animal studies implicate SOCS-1 in TGF-β/Smad-driven fibrogenesis. These findings provide mechanistic insights and translational tools for monitoring PD complications.