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The Analysis of Risk Factors for Diabetic Nephropathy Progression and The Wang et al. J Transl Med (2019) 17:264 https://doi.org/10.1186/s12967-019-2016-y Journal of Translational Medicine RESEARCH Open Access The analysis of risk factors for diabetic nephropathy progression and the construction of a prognostic database for chronic kidney diseases Gang Wang1,2, Jian Ouyang3, Shen Li2, Hui Wang2, Baofeng Lian3, Zhihong Liu1,2* and Lu Xie3* Abstract Background: Diabetic nephropathy (DN) afects about 40% of diabetes mellitus (DM) patients and is the leading cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) all over the world, especially in high- and middle-income countries. Most DN has been present for years before it is diagnosed. Currently, the treatment of DN is mainly to prevent or delay disease progression. Although many important molecules have been discovered in hypothesis-driven research over the past two decades, advances in DN management and new drug develop- ment have been very limited. Moreover, current animal/cell models could not replicate all the features of human DN, while the development of Epigenetics further demonstrates the complexity of the mechanism of DN progression. To capture the key pathways and molecules that actually afect DN progression from numerous published studies, we collected and analyzed human DN prognostic markers (independent risk factors for DN progression). Methods: One hundred and ffty-one DN prognostic markers were collected manually by reading 2365 papers published between 01/01/2002 and 12/15/2018. One hundred and ffteen prognostic markers of other four common CKDs were also collected. GO and KEGG enrichment analysis was done using g:Profler, and a relationship network was built based on the KEGG database. Tissue origin distribution was derived mainly from The Human Protein Atlas (HPA), and a database of these prognostic markers was constructed using PHP Version 5.5.15 and HTML5. Results: Several pathways were signifcantly enriched corresponding to diferent end point events. It is shown that the TNF signaling pathway plays a role through the process of DN progression and adipocytokine signaling pathway is uniquely enriched in ESRD. Molecules, such as TNF, IL6, SOD2, etc. are very important for DN progression, among which, it seems that “AGER” plays a pivotal role in the mechanism. A database, dbPKD, was constructed containing all the collected prognostic markers. Conclusions: This study developed a database for all prognostic markers of fve common CKDs, ofering some bioin- formatics analyses of DN prognostic markers, and providing useful insights towards understanding the fundamental mechanism of human DN progression and for identifying new therapeutic targets. Keywords: Diabetic nephropathy, Progression, Risk factor, Prognostic marker, Bioinformatics analysis, Database *Correspondence: [email protected]; [email protected] 2 National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210016, China 3 Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China Full list of author information is available at the end of the article © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Wang et al. J Transl Med (2019) 17:264 Page 2 of 12 Background Regarding this situation, we collected all DN prog- DN, also known as “diabetic kidney disease (DKD)”, is one nostic markers (risk factors for DN progression) from of the most important diabetic microvascular complica- both routine and high-throughput research based on tions, afecting 30–45% patients with either type 1 DM human samples in the past two decades and performed (T1DM) or type 2 DM (T2DM), with a peak incidence additional bioinformatics analyses, hoping to ofer some in the 10–20 years duration of DM [1–3]. DN, patho- insights into the mechanism of DN progression, which logically, is often characterized by glomerular basement might help DN research and the discovery of new thera- membrane (GBM) thickening, glomerular mesangial peutic targets for DN. matrix expansion, and formation of glomerular nodular We constructed a database dbPKD [20], for prognos- sclerosis in its advanced stages [4], and clinically, is usu- tic markers of DN, as well as other CKDs including IgA ally defned by proteinuria occurrence or declined renal nephropathy (IgAN), idiopathic membranous nephropa- function, e.g. reduced glomerular fltration rate (GFR) [1, thy (IMN), primary focal segmental glomerulosclerosis 5]. DN patients exhibiting modest or no albuminuria may (pFSGS) and Lupus nephritis (LN). Tere have been no progress to ESRD [6, 7]. DN is the leading cause of CKD previously focused databases for risk factors of kidney and ESRD in high-income countries and likely worldwide diseases. dbPKD may provide a resource for searching [8–11], and also a single strong predictor of mortality in reported prognostic factors for common CKDs. patients with DM [12]. Even worse, the absolute number of DN patients continues to increase and the incidence of Methods ESRD from DN keeps expanding [13], consistent with the Data collection global DM pandemic [9, 14]. All DN prognostic markers (risk factors for DN pro- Currently, tight glucose control and strict blood pres- gression) were collected by screening through related sure control (especially with medications that inhibit the literature. We searched the PubMed database using 32 renin-angiotensin system) remain the mainstay of man- keywords, e.g. “DN”, “DKD”, “diabetic kidney disease”, agement for DN. Although some progress has been made “diabetic nephropathy”, “ESRD”, “marker”, etc. (Additional in reducing diabetes-related mortality and delaying the fle 1: Table S1). Totally, 2365 papers published between development of kidney disease from DM, the percent- 01/01/2002 and 12/15/2018 were collected, including age of DN patients who progress to ESRD has not sub- both routine research and high-throughput research. stantially declined [5]. Disappointingly, there has been an Reviews and non-English literature were excluded frst. impasse in the development of new drugs for DN, with Initial screening of literature was based on title and no success in Phase 3 clinical trials [15]. One reason is abstract. Four hundred and three papers were retained the lack of accurate understanding of the underlying for further fltration. Teir contents were checked for pathophysiological mechanisms of human DN develop- information in detail. Filtrations were carried out accord- ment and progression. Targeting single molecules and/ ing to rules: (1) the research subjects must be human, that or pathways that were important in DN development is, samples used for the prognosis study must be derived and progression based on hypothesis-driven research has from humans; (2) the disease studied must be DN, or a not yielded signifcant advances in DN treatment in the synonym of its defnition, such as DKD; (3) markers must past two decades. On one hand, mechanisms underlying be potentially prognostic, which means that these mark- DN development and progression are complicated with ers should be potential risk/protective factors of GFR many interacting molecules and a number of crosstalk decline, doubling of serum creatinine, CKD progression, pathways. On the other hand, current animal and cell ESRD or even death closely related to kidney damage. culture models mainly replicate the early stage of and/ In addition, markers used to predict signifcant albumi- or recapitulate certain features of human DN, failing to nuria/proteinuria progression in DN patients were also reproduce the whole process of DN development and included; (4) only markers that were rigorously verifed to progression [16]. In addition, patients who strictly com- be independent risk factors for DN progression in multi- plied with treatment recommendations can still develop variate analysis were fnally collected, and several markers overt DN whereas patients with similar or poor compli- with only univariate analysis results in current prognostic ance may not. Likewise, not all DM patients with micro- studies were also collected; (5) markers of multiple omic- albuminuria progress to macroalbuminuria or ESRD levels were collected, including genes (involving mRNA, (some patients even revert and the microalbuminuria SNP, CNV, etc.), proteins, microRNAs, and mixed clini- disappears). Terefore, more broad-based approaches cal indicators (referring to all the prognostic markers that including systems biology and multiple omics are being are not genes, proteins, or microRNAs). applied to understanding DN pathological mechanisms Besides DN prognostic markers, we also collected today [17–19]. prognostic markers of other four CKDs (IgAN, IMN, Wang et al. J Transl Med (2019) 17:264 Page 3 of 12 pFSGS and LN). Te collection guidelines were basically of information: reference, research parameters, marker the same as that for DN data. However, there were several annotation, prognostic efect(s) and
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