The analysis of risk factors for diabetic nephropathy progression and the construction of a prognostic database for chronic kidney diseases

Abstract

Background: Diabetic nephropathy (DN) affects 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 development 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 affect DN progression from numerous published studies, we collected and analyzed human DN prognostic markers (independent risk factors for DN progression).

Methods: One hundred and fifty-one DN prognostic markers were collected manually by reading 2365 papers published between 01/01/2002 and 12/15/2018. One hundred and fifteen prognostic markers of other four common CKDs were also collected. GO and KEGG enrichment analysis was done using g:Profiler, 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 significantly enriched corresponding to different 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 five common CKDs, offering some bioinformatics 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: Bioinformatics analysis; Database; Diabetic nephropathy; Prognostic marker; Progression; Risk factor.

Fig. 1

Specimen sources of prognostic proteins and their observed increase/decrease associated with worse prognosis of DN. Blue arrow represents protein change in blood, green arrow is for urine specimen, and orange arrow for kidney tissue

Fig. 2

Grouping based on the end point events and corresponding clinical parameters. a End point events and corresponding clinical parameters. b Grouping of DN prognostic genes and proteins according to the end point events involved in different studies

Fig. 3

KEGG enrichment analysis of DN prognostic genes and proteins corresponding to different end point events. TNF signaling pathway, PI3K-Akt signaling pathway, NF-kappa B signaling pathway, MAPK signaling pathway, HIF-1 signaling pathway and FoxO signaling pathway all belong to “Signal transduction” pathways

Fig. 4

Overview of regulatory relationships among DN prognostic molecules in enriched signal transduction pathways. Solid line represents molecular interaction or relation. Dotted line represents indirect link, state change or unknown reaction. Red line represents link in the cytoplasm. Molecule in the rectangle represents gene product, mostly protein but including RNA

Fig. 5

Protein expression and location of DN prognostic molecules in renal tissues using the HPA [24]. The asterisk (*) denotes specific protein expression in kidney. Bold indicates high protein expression, and proteins expressed in both glomeruli and tubules are in red

Fig. 6

Web interfaces of the dbPKD. a The browse interface of dbPKD for prognostic markers in blood. b The search interface for a gene symbol. c The analysis interface which includes three modules: survival analysis, enrichment analysis and Venn analysis. d The download page of dbPKD with url and description