title: Feature selection and classification of urinary mRNA microarray data by iterative random forest to diagnose renal fibrosis: a two-stage study.
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.
authors.affiliation: Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, Nanjing, Jiangsu, China.
authors.forename: Le-Ting
authors.initials: LT
authors.lastname: Zhou
authors.affiliation: Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, Nanjing, Jiangsu, China.
authors.forename: Yu-Han
authors.initials: YH
authors.lastname: Cao
authors.affiliation: Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, Nanjing, Jiangsu, China.
authors.forename: Lin-Li
authors.initials: LL
authors.lastname: Lv
authors.affiliation: Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, Nanjing, Jiangsu, China.
authors.forename: Kun-Ling
authors.initials: KL
authors.lastname: Ma
authors.affiliation: Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, Nanjing, Jiangsu, China.
authors.forename: Ping-Sheng
authors.initials: PS
authors.lastname: Chen
authors.affiliation: Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, Nanjing, Jiangsu, China.
authors.forename: Hai-Feng
authors.initials: HF
authors.lastname: Ni
authors.affiliation: CT Bioscience CO. LTD, Changzhou, Jiangsu, China.
authors.forename: Xiang-Dong
authors.initials: XD
authors.lastname: Lei
authors.affiliation: Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, Nanjing, Jiangsu, China.
authors.forename: Bi-Cheng
authors.initials: BC
authors.lastname: Liu
mesh_terms: D000328:Adult; D015415:Biomarkers / Q000652:urine; D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D005260:Female; D005355:Fibrosis; D006801:Humans; D007674:Kidney Diseases / Q000473:pathology / Q000652:urine*; D008297:Male; D008875:Middle Aged; D012333:RNA, Messenger / Q000145:classification / Q000652:urine*
chemical_list: D015415:Biomarkers; D012333:RNA, Messenger