ArticleJournal of immunology research2022
Revealing Potential Diagnostic Gene Biomarkers Associated with Immune Infiltration in Patients with Renal Fibrosis Based on Machine Learning Analysis.
Article in Journal of immunology research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 21 citations in OpenAlex.
- The impact of hypoxia and glycolysis on liver fibrosis.Journal of translational medicine · 2026Article
- A Potential Novel Molecular Interaction in Bronchiolitis Obliterans Syndrome in Lung Transplantation Patients: The Role of SERPINA3 and Osteoprotegerin.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- Interpretable Machine Learning Identifies Hub Biomarkers of Renal Fibrosis and Their Potential Medical Applications.Drug design, development and therapy · 2026Article
- Two-Stage Probability-Enhanced Regression on Property Matrices and LLM Embeddings Enables State-of-the-Art Prediction of Gene Knockdown by Modified siRNAs.International journal of molecular sciences · 2025Article
- Non-invasive assessment techniques for renal fibrosis: advances and perspectives.Renal failure · 2025Review
- A Novel Immune-Related Three-Gene Signature and Immune Infiltration Insights in Psoriasis and Chronic Kidney Disease.Clinical, cosmetic and investigational dermatology · 2025Article
- Article
- Predicting feature genes correlated with immune infiltration in patients with abdominal aortic aneurysm based on machine learning algorithms.Scientific reports · 2024Article
- Development and evaluation of a chronic kidney disease risk prediction model using random forest.Frontiers in genetics · 2024Article
- APC and ZBTB2 May Mediate M2 Macrophage Infiltration to Promote the Development of Renal Fibrosis: A Bioinformatics Analysis.BioMed research international · 2024Article
- Machine learning for small interfering RNAs: a concise review of recent developments.Frontiers in genetics · 2023Review
- Construction of a neural network diagnostic model for renal fibrosis and investigation of immune infiltration characteristics.Frontiers in immunology · 2023Article
- Machine learning-based prediction of candidate gene biomarkers correlated with immune infiltration in patients with idiopathic pulmonary fibrosis.Frontiers in medicine · 2023Article
- Identification of immune-related molecular clusters and diagnostic markers in chronic kidney disease based on cluster analysis.Frontiers in genetics · 2023Article
- Article
- Integrated bioinformatical analysis, machine learning andFrontiers in pharmacology · 2022Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic kidney disease is characterized by the development of renal fibrosis. The basic mechanisms of renal fibrosis have not yet been fully investigated despite significant progress in understanding the etiology of the disease. In this work, the researchers sought to identify potential diagnostic indicators for renal fibrosis. From the GEO database, we were able to acquire two gene expression profiles with publically available data (GSE22459 and GSE76882, respectively) from human renal fibrosis and control samples. 215 renal fibrosis specimens and 124 normal specimens were examined for differentially expressed genes (DEGs). The SVM-RFE and LASSO regression models were used to discover potential markers. CIBERSORT was applied to estimate the combined cohorts' immune cell fraction compositional trends in renal fibrosis. RT-PCR was used to examine the expression of ISG20 in renal fibrosis and healthy samples. In vitro experiments were applied to examine the function of ISG20 knockdown on the progression of renal fibrosis. In this study, we identified 24 DEGs. The result of LASSO and SVM-RFE identified nine critical genes. ROC assays confirmed the diagnostic value of the above nine genes for renal fibrosis. Immune cell infiltration analysis revealed that ISG20 and SERPINA3 were both found to be correlated with T cell follicular helper, neutrophils, T cell CD4 memory activated, eosinophils, T cell CD8, dendritic cell activated, B cell memory, monocytes, macrophage M2, plasma cells, T cell CD4 naïve, mast cell resting, B cell naïve, T cell regulatory, and NK cell activated. Finally, we observed that the expression of ISG20 and SERPINA3 was distinctly increased in renal fibrosis samples compared with normal samples. ISG20 siRNA significantly suppressed the progression of renal fibrosis in vitro. Overall, this study identified nine diagnostic biomarkers for renal fibrosis. ISG20 may be a novel therapeutic target of renal fibrosis.
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