ArticleScientific reports2017
Feature selection and classification of urinary mRNA microarray data by iterative random forest to diagnose renal fibrosis: a two-stage study.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 24 citations in OpenAlex.
- Genomic characterization of functional high-risk multiple myeloma patients.Blood cancer journal · 2022Article
- The ratio of urinary TREM-1/TREM-2 mRNA expression in chronic kidney disease and renal fibrosis.Annals of medicine · 2021Article
- RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction.Frontiers in genetics · 2020Article
- Machine Learning Models to Predict Primary Sites of Metastatic Cervical Carcinoma From Unknown Primary.Frontiers in genetics · 2020Article
- Bioinformatics-based discovery of the urinary BBOX1 mRNA as a potential biomarker of diabetic kidney disease.Journal of translational medicine · 2019Article
- Decreased Expression of Urinary Mammalian Target of Rapamycin mRNA Is Related to Chronic Renal Fibrosis in IgAN.Disease markers · 2019Article
- Gene selection for microarray data classification via subspace learning and manifold regularization.Medical & biological engineering & computing · 2018Article
- A Review of Matched-pairs Feature Selection Methods for Gene Expression Data Analysis.Computational and structural biotechnology journal · 2018Review
- Urinary Biomarkers for Chronic Kidney Disease with a Focus on Gene Transcript.Chinese medical journal · 2017Review
- OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models.Scientific reports · 2017Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Renal fibrosis is a common pathological pathway of progressive chronic kidney disease (CKD). However, kidney function parameters are suboptimal for detecting early fibrosis, and therefore, novel biomarkers are urgently needed. We designed a 2-stage study and constructed a targeted microarray to detect urinary mRNAs of CKD patients with renal biopsy and healthy participants. We analysed the microarray data by an iterative random forest method to select candidate biomarkers and produce a more accurate classifier of renal fibrosis. Seventy-six and 49 participants were enrolled into stage I and stage II studies, respectively. By the iterative random forest method, we identified a four-mRNA signature in urinary sediment, including TGFβ1, MMP9, TIMP2, and vimentin, as important features of tubulointerstitial fibrosis (TIF). All four mRNAs significantly correlated with TIF scores and discriminated TIF with high sensitivity, which was further validated in the stage-II study. The combined classifiers showed excellent sensitivity and outperformed serum creatinine and estimated glomerular filtration rate measurements in diagnosing TIF. Another four mRNAs significantly correlated with glomerulosclerosis. These findings showed that urinary mRNAs can serve as sensitive biomarkers of renal fibrosis, and the random forest classifier containing urinary mRNAs showed favourable performance in diagnosing early renal fibrosis.
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