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Urinary Biomarkers of Renal Fibrosis

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Renal Fibrosis: Mechanisms and Therapies

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1165))

Abstract

Renal fibrosis is the common pathological pathway of progressive CKD. The commonly used biomarkers in clinical practice are not optimal to detect injury or predict prognosis. Therefore, it is crucial to develop novel biomarkers to allow prompt intervention. Urine serves as a valuable resource of biomarker discovery for kidney diseases. Owing to the rapid development of omics platforms and bioinformatics, research on novel urinary biomarkers for renal fibrosis has proliferated in recent years. In this chapter, we discuss the current status and provide basic knowledge in this field. We present novel promising biomarkers including tubular injury markers, proteins related to activated inflammation/fibrosis pathways, CKD273, transcriptomic biomarkers, as well as metabolomic biomarkers. Furthermore, considering the complex nature of the pathogenesis of renal fibrosis, we also highlight the combination of biomarkers to further improve the diagnostic and prognostic performance.

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References

  • Aflarian M, Zelnick LR, Ruzinski J, Kestenbaum B, Himmelfarb J, de Boer IH et al (2015) Urine matrix metalloproteinase-7 and risk of kidney disease progression and mortality in type 2 diabetes. J Diabetes Complicat 29:1024–1031

    Article  Google Scholar 

  • Aregger F, Uehlinger DE, Witowski J, Brunisholz RA, Hunziker P, Frey FJ et al (2014) Identification of IGFBP-7 by urinary proteomics as a novel prognostic marker in early acute kidney injury. Kidney Int 85:909–919

    Article  CAS  PubMed  Google Scholar 

  • Argiles A, Siwy J, Duranton F, Gayrard N, Dakna M, Lundin U et al (2013) CKD273, a new proteomics classifier assessing CKD and its prognosis. PLoS ONE 8:e62837

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Astor BC, Koettgen A, Hwang S-J, Bhavsar NA, Fox CS, Coresh J (2011) Trefoil factor 3 predicts incident chronic kidney disease: a case-control study nested within the atherosclerosis risk in communities (ARIC) study. Am J Nephrol 34:291–297

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bachmayr-Heyda A, Reiner AT, Auer K, Sukhbaatar N, Aust S, Bachleitner-Hofmann T et al (2015) Correlation of circular RNA abundance with proliferation—exemplified with colorectal and ovarian cancer, idiopathic lung fibrosis, and normal human tissues. Sci Rep 5:8057

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Betz BB, Jenks SJ, Cronshaw AD, Lamont DJ, Cairns C, Manning JR et al (2016) Urinary peptidomics in a rodent model of diabetic nephropathy highlights epidermal growth factor as a biomarker for renal deterioration in patients with type 2 diabetes. Kidney Int 89:1125–1135

    Article  CAS  PubMed  Google Scholar 

  • Bhavsar NA, Koettgen A, Coresh J, Astor BC (2012) Neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule 1 (KIM-1) as predictors of incident CKD Stage 3: the atherosclerosis risk in communities (ARIC) Study. Am J Kidney Dis 60:233–240

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Burns KD, Hiremath S (2012) Urinary angiotensinogen as a biomarker of chronic kidney disease: ready for prime time? Nephrol Dial Transplant 27:3010–3013

    Article  CAS  PubMed  Google Scholar 

  • Cao YH, Lv LL, Zhang X, Hu H, Ding LH, Yin D et al (2015) Urinary vimentin mRNA as a potential novel biomarker of renal fibrosis. Am J Physiol Renal Physiol 309:F514–F522

    Article  CAS  PubMed  Google Scholar 

  • Cheng L, Sun X, Scicluna BJ, Coleman BM, Hill AF (2014) Characterization and deep sequencing analysis of exosomal and non-exosomal miRNA in human urine. Kidney Int 86:433–444

    Article  CAS  PubMed  Google Scholar 

  • Craciun FL, Bijol V, Ajay AK, Rao P, Kumar RK, Hutchinson J et al (2015) RNA sequencing identifies novel translational biomarkers of kidney fibrosis. J Am Soc Nephrol 27:1702–1713

    Article  PubMed  PubMed Central  Google Scholar 

  • Critselis E, Heerspink HL (2016) Utility of the CKD273 peptide classifier in predicting chronic kidney disease progression. Nephrol Dial Transplant 31:249–254

    CAS  PubMed  Google Scholar 

  • Dear JW, Street JM, Bailey MA (2013) Urinary exosomes: a reservoir for biomarker discovery and potential mediators of intrarenal signalling. Proteomics 13:1572–1580

    Article  CAS  PubMed  Google Scholar 

  • Duan ZY, Cai GY, Bu R, Lu Y, Hou K, Chen XM (2016) Selection of urinary sediment miRNAs as specific biomarkers of IgA nephropathy. Sci Rep 6:23498

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Duranton F, Lundin U, Gayrard N, Mischak H, Aparicio M, Mourad G et al (2014) Plasma and urinary amino acid metabolomic profiling in patients with different levels of kidney function. Clin J Am Soc Nephrol 9:37–45

    Article  CAS  PubMed  Google Scholar 

  • Erdbrugger U, Le TH (2016) Extracellular vesicles in renal diseases: more than novel biomarkers? J Am Soc Nephrol 27:12–26

    Article  CAS  PubMed  Google Scholar 

  • Fiehn O (2002) Metabolomics- the link between genotypes and phenotypes. Plant Mol Biol 48:155–171

    Article  CAS  PubMed  Google Scholar 

  • Gonzalez-Calero L, Martinez PJ, Martin-Lorenzo M, Baldan-Martin M, Ruiz-Hurtado G, de la Cuesta F et al (2017) Urinary exosomes reveal protein signatures in hypertensive patients with albuminuria. Oncotarget 8:44217–44231

    Article  PubMed  PubMed Central  Google Scholar 

  • Good DM, Zuerbig P, Argiles A, Bauer HW, Behrens G, Coon JJ et al (2010) Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease. Mol Cell Proteomics 9:2424–2437

    Article  PubMed  PubMed Central  Google Scholar 

  • He W, Tan RJ, Li Y, Wang D, Nie J, Hou FF et al (2012) Matrix metalloproteinase-7 as a surrogate marker predicts renal Wnt/beta-Catenin activity in CKD. J Am Soc Nephrol 23:294–304

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hsu CY, Xie DW, Waikar SS, Bonventre JV, Zhang XM, Sabbisetti V et al (2017) Urine biomarkers of tubular injury do not improve on the clinical model predicting chronic kidney disease progression. Kidney Int 91:196–203

    Article  CAS  PubMed  Google Scholar 

  • Huang YS, Hsieh HY, Shih HM, Sytwu HK, Wu CC (2014) Urinary Xist is a potential biomarker for membranous nephropathy. Biochem Biophys Res Commun 452:415–421

    Article  CAS  PubMed  Google Scholar 

  • Hwang JH, Lee JP, Kim CT, Yang SH, Kim JH, An JN et al (2016) Urinary periostin excretion predicts renal outcome in IgA nephropathy. Am J Nephrol 44:481–492

    Article  CAS  PubMed  Google Scholar 

  • Jeck WR, Sharpless NE (2014) Detecting and characterizing circular RNAs. Nat Biotechnol 32:453–461

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kim KM, Abdelmohsen K, Mustapic M, Kapogiannis D, Gorospe M (2017) RNA in extracellular vesicles. Wiley Interdisciplinary Reviews-Rna 8

    Google Scholar 

  • Klein J, Bascands J-L, Mischak H, Schanstra JP (2016) The role of urinary peptidomics in kidney disease research. Kidney Int 89:539–545

    Article  CAS  PubMed  Google Scholar 

  • Latterich M, Schnitzer JE (2011) Streamlining biomarker discovery. Nat Biotechnol 29:600–602

    Article  CAS  PubMed  Google Scholar 

  • Lebherz-Eichinger D, Klaus DA, Reiter T, Hoerl WH, Haas M, Ankersmit HJ et al (2014) Increased chemokine excretion in patients suffering from chronic kidney disease. Transl Res 164:433–443

    Article  CAS  PubMed  Google Scholar 

  • Lebherz-Eichinger D, Tudor B, Ankersmit HJ, Reiter T, Haas M, Roth-Walter F et al (2015) Trefoil Factor 1 excretion is increased in early stages of chronic kidney disease. PLoS ONE 10:e0138312

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Li B, Hartono C, Ding R, Sharma VK, Ramaswamy R, Qian B et al (2001) Noninvasive diagnosis of renal-allograft rejection by measurement of messenger RNA for perforin and granzyme B in urine. N Engl J Med 344:947–954

    Article  CAS  PubMed  Google Scholar 

  • Liu BC, Tang TT, Lv LL, Lan HY (2018) Renal tubule injury: a driving force toward chronic kidney disease. Kidney Int 93:568–579

    Article  CAS  PubMed  Google Scholar 

  • Lorenzen JM, Haller H, Thum T (2011) MicroRNAs as mediators and therapeutic targets in chronic kidney disease. Nat Rev Nephrol 7:286–294

    Article  CAS  PubMed  Google Scholar 

  • Lv LL, Cao YH, Ni HF, Xu M, Liu D, Liu H et al (2013a) MicroRNA-29c in urinary exosome/microvesicle as a biomarker of renal fibrosis. Am J Physiol-Ren Physiol 305:F1220–F1227

    Article  CAS  Google Scholar 

  • Lv LL, Cao Y, Liu D, Xu M, Liu H, Tang RN et al (2013b) Isolation and quantification of MicroRNAs from urinary exosomes/microvesicles for biomarker discovery. Int J Biol Sci 9:1021–1031

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Lv LL, Cao YH, Pan MM, Liu H, Tang RN, Ma KL et al (2014) CD2AP mRNA in urinary exosome as biomarker of kidney disease. Clin Chim Acta 428:26–31

    Article  CAS  PubMed  Google Scholar 

  • Lyu LL, Feng Y, Liu BC (2017) Urinary biomarkers for chronic kidney disease with a focus on gene transcript. Chin Med J 130:2251–2256

    Article  PubMed  PubMed Central  Google Scholar 

  • Magalhaes P, Pejchinovski M, Markoska K, Banasik M, Klinger M, Svec-Billa D et al (2017) Association of kidney fibrosis with urinary peptides: a path towards non-invasive liquid biopsies? Sci Rep 7:16915

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Mateescu B, Kowal EJK, van Balkom BWM, Bartel S, Bhattacharyya SN, Buzas EI et al (2017) Obstacles and opportunities in the functional analysis of extracellular vesicle RNA- an ISEV position paper. J Extracell Vesicles 6:1286095

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • McGeechan K, Macaskill P, Irwig L, Liew G, Wong TY (2008) Assessing new biomarkers and predictive models for use in clinical practice a clinician’s guide. Arch Intern Med 168:2304–2310

    Article  PubMed  Google Scholar 

  • McMahon GM, Hwang SJ, Clish CB, Tin A, Yang Q, Larson MG et al (2017) Urinary metabolites along with common and rare genetic variations are associated with incident chronic kidney disease. Kidney Int 91:1426–1435

    Article  CAS  PubMed  Google Scholar 

  • Mills KT, Kobori H, Hamm LL, Alper AB, Khan IE, Rahman M et al (2012) Increased urinary excretion of angiotensinogen is associated with risk of chronic kidney disease. Nephrol Dial Transplant 27:3176–3181

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mischak H, Delles C, Vlahou A, Vanholder R (2015) Proteomic biomarkers in kidney disease: issues in development and implementation. Nat Rev Nephrol 11:221–232

    Article  CAS  PubMed  Google Scholar 

  • Moons KGM, Kengne AP, Grobbee DE, Royston P, Vergouwe Y, Altman DG et al (2012a) Risk prediction models: II. External validation, model updating, and impact assessment. Heart 98:691–698

    Article  PubMed  Google Scholar 

  • Moons KGM, Kengne AP, Woodward M, Royston P, Vergouwe Y, Altman DG et al (2012b) Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart 98:683–690

    Article  PubMed  Google Scholar 

  • Mou S, Wang Q, Li J, Shi B, Ni Z (2012) Urinary excretion of liver-type fatty acid-binding protein as a marker of progressive kidney function deterioration in patients with chronic glomerulonephritis. Clin Chim Acta 413:187–191

    Article  CAS  PubMed  Google Scholar 

  • Muralidharan J, Ramezani A, Hubal M, Knoblach S, Shrivastav S, Karandish S et al (2017) Extracellular microRNA signature in chronic kidney disease. Am J Physiol-Ren Physiol 312:F982–F991

    Article  CAS  Google Scholar 

  • Nadkarni GN, Rao V, Ismail-Beigi F, Fonseca VA, Shah SV, Simonson MS et al (2016) Association of urinary biomarkers of inflammation, injury, and fibrosis with renal function decline: the ACCORD trial. Clin J Am Soc Nephrol 11:1343–1352

    Article  PubMed  PubMed Central  Google Scholar 

  • Nguyen TQ, Tarnow L, Andersen S, Hovind P, Parving HH, Goldschmeding R et al (2006) Urinary connective tissue growth factor excretion correlates with clinical markers of renal disease in a large population of type 1 diabetic patients with diabetic nephropathy. Diabetes Care 29:83–88

    Article  CAS  PubMed  Google Scholar 

  • O’Seaghdha CM, Hwang SJ, Bhavsar NA, Koettgen A, Coresh J, Astor BC et al (2011) Lower urinary connective tissue growth factor levels and incident CKD Stage 3 in the general population. Am J Kidney Dis 57:841–849

    Article  PubMed  CAS  Google Scholar 

  • O’Seaghdha CM, Hwang SJ, Larson MG, Meigs JB, Vasan RS, Fox CS (2013) Analysis of a urinary biomarker panel for incident kidney disease and clinical outcomes. J Am Soc Nephrol 24:1880–1888

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Ovrehus MA, Zuerbig P, Vikse BE, Hallan SI (2015) Urinary proteomics in chronic kidney disease: diagnosis and risk of progression beyond albuminuria. Clin Proteomics 12:21

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Papadopoulos T, Belliere J, Bascands JL, Neau E, Klein J, Schanstra JP (2015) miRNAs in urine: a mirror image of kidney disease? Expert Rev Mol Diagn 15:361–374

    Article  CAS  PubMed  Google Scholar 

  • Papadopoulos T, Krochmal M, Cisek K, Fernandes M, Husi H, Stevens R et al (2016) Omics databases on kidney disease: where they can be found and how to benefit from them. Clin Kidney J 9:343–352

    Article  PubMed  PubMed Central  Google Scholar 

  • Peralta CA, Katz R, Bonventre JV, Sabbisetti V, Siscovick D, Sarnak M et al (2012) Associations of urinary levels of kidney injury molecule 1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL) with kidney function decline in the multi-ethnic study of atherosclerosis (MESA). Am J Kidney Dis 60:904–911

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pisitkun T, Shen RF, Knepper MA (2004) Identification and proteomic profiling of exosomes in human urine. Proc Natl Acad Sci U S A 101:13368–13373

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pontillo C, Jacobs L, Staessen JA, Schanstra JP, Rossing P, Heerspink HJ et al (2016) A urinary proteome-based classifier for the early detection of decline in glomerular filtration. Nephrol Dial Transpl 32:1510–1516

    Google Scholar 

  • Posada-Ayala M, Zubiri I, Martin-Lorenzo M, Sanz-Maroto A, Molero D, Gonzalez-Calero L et al (2014) Identification of a urine metabolomic signature in patients with advanced-stage chronic kidney disease. Kidney Int 85:103–111

    Article  CAS  PubMed  Google Scholar 

  • Prakoura N, Chatziantoniou C (2017) Periostin in kidney diseases. Cell Mol Life Sci 74:4315–4320

    Article  CAS  PubMed  Google Scholar 

  • Puzantian HV, Townsend RR (2013) Understanding kidney function assessment: the basics and advances. J Am Assoc Nurse Pract 25:334–341

    PubMed  Google Scholar 

  • Rifai N, Gillette MA, Carr SA (2006) Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol 24:971–983

    Article  CAS  PubMed  Google Scholar 

  • Riser BL, Cortes P, Denichilo M, Deshmukh PV, Chahal PS, Mohammed AK et al (2003) Urinary CCN2 (CTGF) as a possible predictor of diabetic nephropathy: preliminary report. Kidney Int 64:451–458

    Article  CAS  PubMed  Google Scholar 

  • Satirapoj B, Wang Y, Chamberlin MP, Dai T, LaPage J, Phillips L et al (2012) Periostin: novel tissue and urinary biomarker of progressive renal injury induces a coordinated mesenchymal phenotype in tubular cells. Nephrol Dial Transpl 27:2702–2711

    Article  CAS  Google Scholar 

  • Satirapoj B, Tassanasorn S, Charoenpitakchai M, Supasyndh O (2015) Periostin as a tissue and urinary biomarker of renal injury in type 2 diabetes mellitus. PLoS ONE 10:e0124055

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Sato Y, Wharram BL, Lee SK, Wickman L, Goyal M, Venkatareddy M et al (2009) Urine podocyte mRNAs mark progression of renal disease. J Am Soc Nephrol 20:1041–1052

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sawaguchi M, Araki S-i, Kobori H, Urushihara M, Haneda M, Koya D et al (2012) Association between urinary angiotensinogen levels and renal and cardiovascular prognoses in patients with type 2 diabetes mellitus. J Diabetes Investig 3:318–324

    Article  CAS  PubMed  Google Scholar 

  • Schanstra JP, Zurbig P, Alkhalaf A, Argiles A, Bakker SJ, Beige J et al (2015) Diagnosis and prediction of CKD progression by assessment of urinary peptides. J Am Soc Nephrol 26:1999–2010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shoukry A, Bdeer SE-A, El-Sokkary RH (2015) Urinary monocyte chemoattractant protein-1 and vitamin D-binding protein as biomarkers for early detection of diabetic nephropathy in type 2 diabetes mellitus. Mol Cell Biochem 408:25–35

    Article  CAS  PubMed  Google Scholar 

  • Sun JZ, Zhang S, Shi BH, Zheng DX, Shi J (2017) Transcriptome identified lncRNAs associated with renal fibrosisin UUO rat model. Front Physiol 8:658

    Article  PubMed  PubMed Central  Google Scholar 

  • Szeto CC, Chan RW, Lai KB, Szeto CY, Chow KM, Li PK et al (2005) Messenger RNA expression of target genes in the urinary sediment of patients with chronic kidney diseases. Nephrol Dial Transplant 20:105–113

    Article  CAS  PubMed  Google Scholar 

  • Szeto CC, Ching-Ha KB, Ka-Bik L, Mac-Moune LF, Cheung-Lung CP, Gang W et al (2012) Micro-RNA expression in the urinary sediment of patients with chronic kidney diseases. Dis Markers 33:137–144

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tan HL, Yap JQ, Qian Q (2016) Acute kidney injury: tubular markers and risk for chronic kidney disease and end-stage kidney failure. Blood Purif 41:144–150

    Article  CAS  PubMed  Google Scholar 

  • Tangri N, Stevens LA, Griffith J, Tighiouart H, Djurdjev O, Naimark D et al (2011) A predictive model for progression of chronic kidney disease to kidney failure. Jama-J Am Med Assoc 305:1553–1559

    Article  CAS  Google Scholar 

  • Titan SM, Vieira JM Jr, Dominguez WV, Moreira SRS, Pereira AB, Barros RT et al (2012) Urinary MCP-1 and RBP: independent predictors of renal outcome in macroalbuminuric diabetic nephropathy. J Diabetes Complicat 26:546–553

    Article  CAS  Google Scholar 

  • Van der Hauwaert C, Savary G, Hennino MF, Pottier N, Glowacki F, Cauffiez C (2015) MicroRNAs in kidney fibrosis. Nephrol Ther 11:474–482

    Article  PubMed  Google Scholar 

  • Verhave JC, Bouchard J, Goupil R, Pichette V, Brachemi S, Madore F et al (2013) Clinical value of inflammatory urinary biomarkers in overt diabetic nephropathy: a prospective study. Diabetes Res Clin Pract 101:333–340

    Article  CAS  PubMed  Google Scholar 

  • Wang G, Kwan BCH, Lai FMM, Chow KM, Li PKT, Szeto CC (2012) Urinary miR-21, miR-29, and miR-93: novel biomarkers of fibrosis. Am J Nephrol 36:412–418

    Article  CAS  PubMed  Google Scholar 

  • Wang G, Lai FMM, Chow KM, Kwan BCH, Pang WF, Luk CCW et al (2015) Urinary mRNA levels of ELR-negative CXC chemokine ligand and extracellular matrix in diabetic nephropathy. Diabetes-MetabIsm Res Rev 31:699–706

    Article  CAS  Google Scholar 

  • Wang L, Wang Y, Chang Q (2016) Feature selection methods for big data bioinformatics: a survey from the search perspective. Methods 111:21–31

    Article  CAS  PubMed  Google Scholar 

  • Wang W, Sue ACH, Goh WWB (2017a) Feature selection in clinical proteomics: with great power comes great reproducibility. Drug Discovery Today 22(6):912–918

    Article  CAS  PubMed  Google Scholar 

  • Wang X, Dai Y, Zhang WF, Sun DL, Zhang XZ (2017b) Microarray based circRNA expression profiles in uremic plasma and PBMCs due to chronic glomerulonephritis. Arch Biol Sci 69:523–534

    Article  Google Scholar 

  • Yamamoto T, Nakagawa T, Suzuki H, Ohashi N, Fukasawa H, Fujigaki Y et al (2007) Urinary angiotensinogen as a marker of intrarenal angiotensin II activity associated with deterioration of renal function in patients with chronic kidney disease. J Am Soc Nephrol 18:1558–1565

    Article  CAS  PubMed  Google Scholar 

  • Yamamoto CM, Murakami T, Oakes ML, Mitsuhashi M, Kelly C, Henry RR et al (2018) Uromodulin mRNA from urinary extracellular vesicles correlate to kidney function decline in Type 2 diabetes mellitus. Am J Nephrol 47:283–291

    Article  CAS  PubMed  Google Scholar 

  • Zhao YY (2013) Metabolomics in chronic kidney disease. Clin Chim Acta 422:59–69

    Article  CAS  PubMed  Google Scholar 

  • Zhao YY, Liu J, Cheng XL, Bai X, Lin RC (2012) Urinary metabonomics study on biochemical changes in an experimental model of chronic renal failure by adenine based on UPLC Q-TOF/MS. Clin Chim Acta 413:642–649

    Article  CAS  PubMed  Google Scholar 

  • Zhao Y, Zhu L, Zhou T, Zhang Q, Shi S, Liu L et al (2015) Urinary CXCL1: a novel predictor of IgA nephropathy progression. PLoS ONE 10:e0119033

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zheng M, Lv LL, Ni J, Ni HF, Li Q, Ma KL et al (2011) Urinary podocyte-associated mRNA profile in various stages of diabetic nephropathy. PLoS ONE 6:e20431

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zheng M, Lv LL, Cao YH, Liu H, Ni J, Dai HY et al (2012) A pilot trial assessing urinary gene expression profiling with an mRNA array for diabetic nephropathy. PLoS ONE 7:e34824

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhou H, Pisitkun T, Aponte A, Yuen PST, Hoffert JD, Yasuda H et al (2006) Exosomal fetuin-a identified by proteomics: a novel urinary biomarker for detecting acute kidney injury. Kidney Int 70:1847–1857

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhou Q, Chung ACK, Huang XR, Dong Y, Yu X, Lan HY (2014) Identification of novel long noncoding RNAs associated with TGF-β/Smad3-mediated renal inflammation and fibrosis by RNA sequencing. Am J Pathol 184:409–417

    Article  CAS  PubMed  Google Scholar 

  • Zhou LT, Lv LL, Pan MM, Cao YH, Liu H, Feng Y et al (2016) Are urinary tubular injury markers useful in chronic kidney disease? a systematic review and meta analysis. PLoS ONE 11:e0167334

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zhou D, Tian Y, Sun L, Zhou LL, Xiao LX, Tan RJ et al (2017a) Matrix metalloproteinase-7 is a urinary biomarker and pathogenic mediator of kidney fibrosis. J Am Soc Nephrol 28:598–611

    Article  CAS  PubMed  Google Scholar 

  • Zhou LT, Cao YH, Lv LL, Ma KL, Chen PS, Ni HF et al (2017b) Feature selection and classification of urinary mRNA microarray data by iterative random forest to diagnose renal fibrosis: a two-stage study. Sci Rep 7:39832

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhou LT, Qiu S, Lv LL, Li ZL, Liu H, Tang RN et al (2018) Integrative bioinformatics analysis provides insight into the molecular mechanisms of chronic kidney disease. Kidney Blood Press Res 43:568–581

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was financially supported by the National Key Research and Development Program of China (2018YFC1314004), the National Natural Science Foundation of China (No.81720108007, 81670696, 81470922 and 31671194), the Clinic Research Center of Jiangsu Province (No. BL2014080).

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  1. Institute of Nephrology, Zhong Da Hospital, Southeast University School of Medicine, DingJiaQiao Road, Nanjing, China

    Le-Ting Zhou, Lin-Li Lv & Bi-Cheng Liu

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  2. Lin-Li Lv
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  1. Zhongda Hospital, Southeast University School of Medicine, Southeast University, Nanjing, Jiangsu, China

    Bi-Cheng Liu

  2. Department of Medicine and Therapeutics, Li Ka Shing Institute of Health Sciences, Chinese University of Hong Kong, Hong Kong, Hong Kong

    Hui-Yao Lan

  3. Zhongda Hospital, Southeast University School of Medicine, Southeast University, Nanjing, Jiangsu, China

    Lin-Li Lv

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Zhou, LT., Lv, LL., Liu, BC. (2019). Urinary Biomarkers of Renal Fibrosis. In: Liu, BC., Lan, HY., Lv, LL. (eds) Renal Fibrosis: Mechanisms and Therapies. Advances in Experimental Medicine and Biology, vol 1165. Springer, Singapore. https://doi.org/10.1007/978-981-13-8871-2_30

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