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Fazeli, Seyed Amirhossein, Javanmard, Amir-Reza. (1405). Machine Learning Analysis of Single-Cell Transcriptomics Identifies a B-Cell-Associated Fibroblast Signature Predictive of Renal Fibrosis Severity. سامانه مدیریت نشریات علمی, 23(3), 5-5. doi: 10.22034/iji.2026.111015.3189
Seyed Amirhossein Fazeli; Amir-Reza Javanmard. "Machine Learning Analysis of Single-Cell Transcriptomics Identifies a B-Cell-Associated Fibroblast Signature Predictive of Renal Fibrosis Severity". سامانه مدیریت نشریات علمی, 23, 3, 1405, 5-5. doi: 10.22034/iji.2026.111015.3189
Fazeli, Seyed Amirhossein, Javanmard, Amir-Reza. (1405). 'Machine Learning Analysis of Single-Cell Transcriptomics Identifies a B-Cell-Associated Fibroblast Signature Predictive of Renal Fibrosis Severity', سامانه مدیریت نشریات علمی, 23(3), pp. 5-5. doi: 10.22034/iji.2026.111015.3189
Fazeli, Seyed Amirhossein, Javanmard, Amir-Reza. Machine Learning Analysis of Single-Cell Transcriptomics Identifies a B-Cell-Associated Fibroblast Signature Predictive of Renal Fibrosis Severity. سامانه مدیریت نشریات علمی, 1405; 23(3): 5-5. doi: 10.22034/iji.2026.111015.3189

Machine Learning Analysis of Single-Cell Transcriptomics Identifies a B-Cell-Associated Fibroblast Signature Predictive of Renal Fibrosis Severity

Iranian Journal of Immunology
دوره 23، شماره 3، آذر 2026، صفحه 5-5
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22034/iji.2026.111015.3189
نویسندگان
Seyed Amirhossein Fazeli1، 2؛ Amir-Reza Javanmard* 3
1Taleghani General Hospital, Division of Nephrology, Department of Internal Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2Clinical Research and Development Center, Division of Nephrology, Department of Internal Medicine, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
3Skull Base Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
چکیده
Background: Renal fibrosis represents the final common pathway of chronic kidney disease (CKD); however, both its definitive diagnostic biomarkers and the principal cellular mediators driving its progression remain incompletely characterized.
Objective: To derive a machine-learning-based transcriptomic signature from single-cell RNA-sequencing data that predicts kidney fibrosis severity and elucidates the underlying immune-stromal cellular interactions.
Methods: A machine-learning approach incorporating Random Forest and Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to identify a sparse transcriptomic signature. Cell-cell communication networks and pseudotime trajectories were reconstructed to characterize the fibrotic niche. The five-gene signature (CXCL13, CCL19, TNFSF13B, IL6, and TGFB1) was then correlated with eGFR, UACR, and Banff scores, and externally validated in the independent human kidney scRNA-seq dataset GSE183276.
Results: The fibrotic kidney microenvironment exhibited a marked expansion of fibroblasts and CD19+ B-cells. Machine-learning feature selection identified a highly predictive five-gene molecular signature comprising CXCL13, CCL19, TNFSF13B (encoding BAFF), IL6, and TGFB1. Intercellular network analysis revealed dominant B-cell–fibroblast signaling that was significantly associated with fibroblast transdifferentiation into extracellular matrix-producing myofibroblasts. The signature score correlated with eGFR, UACR, and Banff scores. External validation in the independent dataset GSE183276 confirmed the diagnostic robustness of this signature.
Conclusions: We present a validated machine-learning -derived transcriptomic signature that reflects the immune-stromal dynamics of renal fibrosis. This five-gene signature accurately predicts CKD severity and, importantly, implicates B-cell-mediated fibroblast activation as a promising diagnostic biomarker and a potential therapeutic target.
کلیدواژه‌ها
B-cell؛ Fibroblast activation؛ Machine Learning؛ Renal fibrosis؛ Single-cell RNA-sequencing
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