CN113186272A - Application of VCAN as biomarker in DKD prognosis prediction reagent or kit - Google Patents
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Abstract
The invention discloses application of VCAN and a protein versican expressed by the VCAN as a biomarker in preparation of a DKD prognostic diagnostic reagent or a kit. The pivot gene VCAN indicates the progression of DKD as a biomarker. High levels of the VCAN-expressing protein versican are indicative of increased tubular interstitial inflammatory factors and proinflammatory cells caused by DKD. The versican is used as a biomarker to detect the protein level of the versican in the tubulointerstitial space. High levels of the versican protein are indicative of poor prognosis in DKD patients. After the information is combined with clinical indexes, the information has good prediction value and can be applied to preparation of a reagent or a kit for DKD patient prognosis diagnosis.
Description
Technical Field
The invention relates to the technical field of biomedicine, in particular to application of a gene VCAN as a biomarker in preparation of a reagent or a kit for predicting diabetic nephropathy prognosis early warning.
Background
DKD (diabetic nephropathy) is the leading cause of CKD (chronic kidney disease) in china, surpassing glomerulonephritis. Diabetes accounts for 30% -50% of CKD population, affecting 2.85 billion adults worldwide. The number of patients with CKD is increasing year by year. In view of the mortality and morbidity of DKD, more and more research is attempting to uncover the pathogenesis of DKD and drive drug development to slow down or even reverse the progression of DKD. Alterations in hemodynamics, metabolic disorders, immune disorders and impairment of the filtration barrier are the major causes of DKD kidney injury. There is increasing evidence that tubulointerstities play an important initiating and determining role in the pathogenesis of DKD. Apoptosis and fibrosis of the tubular cells and interstitium occur due to decreased oxygen transport, mitochondrial dysfunction, increased oxygen consumption, non-ischemic pathways, and the like. Thus, several clinical trials have shown efficacy in slowing DKD progression, including the use of ARB/ACEI (IDNT (2001), renal (2001)), SGLT-2 inhibitors (emareg (2015), canvasas (2017) and CREDENCE (2019)), and GLP-1 analogs (LEADER (2016), REWIND (2019)). However, due to the vague understanding of DKD, there is currently no major clinical trial for DKD immune disorders. The role of inflammation in the progression of DKD has received unprecedented attention in recent years.
Currently, the development of high throughput technologies and online bioinformatics databases enables researchers to explore genes associated with disease, revealing underlying mechanisms of disease. WGCNA, a widely used bioinformatics analysis method, groups genes having similar expression patterns together and provides trait-related gene information depending on expression data values. Although there are a number of studies involving the exploration of DKD genomic expression, there is no study of tubulointerstities on DKD samples, and most bioinformatics studies lack validation.
Versican is translated from the VCAN gene and is a component of the extracellular matrix and plays a role in regulating cell adhesion, proliferation, migration, apoptosis and extracellular matrix assembly. It also plays a role in the regulation of inflammation due to its interaction with immune cell receptors and chemokines. It has four subtypes, V0, V1, V2 and V3. These isoforms differ in the fact that the two alternatively spliced glycosaminoglycan (GAG) domains are Chondroitin Sulfate (CS) attachment domains. V0, V1 and V3 are present in most tissues. V0 and V1 are the major subtypes that accumulate in diseased tissues, and it appears that both subtypes play a role in the inflammatory process. V3 is thought to inhibit the pro-inflammatory function of V0/V1 due to the lack of the CS domain. V2 is only expressed in the central nervous system.
It was found that versican exerts a pro-inflammatory effect by affecting the adhesion of myeloid and lymphoid cells. Leukocyte adhesion (including activated T cells and monocytes) is regulated by versican. Furthermore, versican is also involved in the assembly and engineering of ECM. Versican interacts with HA, tenascin-R, fibulin1, fibrillin, etc. By controlling the organization of extracellular matrix molecules, the invasion of cells is regulated. Therefore, versican has the ability to modulate inflammatory cell infiltration in diseased tissues. Another way in which Versican affects inflammation is to modulate cytokine release. For example, versican induces macrophages to secrete TNF- α and IL-6. However, there is currently a lack of exploration for the predictive value of VCAN and versican in DKD. Clinical DKD patient prognosis still requires reliance on physician analysis of patient clinical indications and laboratory test results. In conclusion, the proposal of the novel biomarker has important significance for predicting the DKD development and prognosis, and can also provide potential therapeutic targets for people to overcome DKD.
Disclosure of Invention
In view of the above, the main object of the present invention is to provide the application of VCAN and its expressed protein versican as biomarker in the preparation of DKD prognostic diagnostic reagent or kit.
In order to express the purpose, the invention provides the following technical scheme:
the VCAN gene is used as a biomarker in the preparation of a reagent or a kit for predicting DKD (diabetic nephropathy) prognosis early warning.
The VCAN gene is a biomarker at a specific site on the human chromosome of 5q14.2-q 14.3.
The pivot gene VCAN indicates the progression of DKD as a biomarker.
Application of an antibody of VCAN expression protein versican in preparation of a DKD prognosis diagnosis reagent or kit.
The high level of the versican protein expressed by VCAN is predictive of increased tubular interstitial inflammatory factors and proinflammatory cells caused by DKD.
Further, the protein versican, encoded by VCAN, comprises four subtypes V0, V1, V2, V3. There are four subtypes, V0, V1, V2, and V3, versican.
Further, the versican is used as a biomarker to detect the transcription level in the renal tubular interstitium, and the high transcription level indicates that the DKD patient has poor prognosis. High levels of the versican protein are indicative of poor prognosis in DKD patients.
Further, antibody to versican was used to detect the protein level in tubulointerstitial matrix by direct or indirect valency flow cytometry.
Compared with the prior art, the invention has the following advantages:
the present invention performed a weighted gene co-expression network analysis (WGCNA) of 7DKD patients and 17 live donor lines in a common data set GSE 104954. By constructing a co-expression network, the gene modules associated with the outcome are determined. In the module, 1) differential expression (| log FC | >1, adjustment P value <0.05), 2) GS >0.2, MM >0.8, 3) MCC algorithm rank front 30 is used as a screening standard, and VCAN is obtained as a key gene related to DKD prognosis. Furthermore, in common data set GSE104954, the expression of VCAN in the DKD tubulointerstitial injury group was also significantly higher than in the living donor kidney group.
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In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
FIG. 1 shows the hierarchical clustering results obtained after construction of a weighted gene co-expression network. In panel a, volcanic representations of all genes are shown and annotated with the top 10 DEGs. DEGs of DKD and LD (normal living kidney-providing) samples are shown in panel B.
FIG. 2 shows a functional analysis of the DEGs identified in FIG. 1. Graph a bubble diagrams for GO and KEGG. B-graph IPA top 30 typical vias. C fig. IPA top 30 typical vias.
Fig. 3 shows 7DKD and 17LD transcription data WGCNA from GSE 104954. (A) Sample clustering of 7DKD and 17LD transcription data. (B) Analyzing the soft threshold power of the fitted scale-free topological model and the average connectivity of the soft threshold power; 5 was chosen as the value to construct a scaleless network. (C) Dendrograms of gene modules. The branches represent different gene modules, and each leaf represents a gene-in-cluster dendrogram. (D) Heat map of co-expression correlation of each gene weighted gene. (E) Correlation between modular signature genes and clinical signatures. Clinical features included DKD and LD, and corresponding correlations and p-values were given. (F) The light cyan region represents the highest positive correlation.
Fig. 4 shows the pivot genes in 7 DKDs and 17LD transcription data of GSE 104954. (A) Venn diagram of DEGs and hub genes. (B) The central gene identified by cytohubba was used. (C) Cell maps were used to study the interaction of cross-genes.
Fig. 5 shows the verification of VCAN in GSE 30122. (A) GSE30122 volcano, annotated for the first 10 DEGs. DKD versus LD versus VCAN expression, p <0.0001(B) DKD and LD Kidney tissue versican immunohistochemical staining.
Fig. 6 shows DKD compared to LD cell type enrichment score with a p value of 0.05. As can be seen from the figure, there is a correlation between VCAN expression levels and the respective cell rankings (p-value >0.05) except that basophils, platelets and Treg cells have no correlation with VCAN expression levels.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The experimental conditions and methods, for which specific conditions are not indicated in the examples, are generally in accordance with conventional conditions or with conditions recommended by the manufacturer.
The invention aims to screen out genes closely related to DKD tubulointerstitial injury prognosis through WGCNA analysis of an existing DKD patient RNA sequencing data set, further investigate the potential value of the genes as biomarkers in a verification data set and a clinical sample, provide a basis for DKD prognosis early warning, and increase the application value of the existing prognosis system by making the screened genes and proteins thereof into reagents or kits.
In order to achieve the above object, the technical scheme adopted by the invention is as follows:
1. tubulointerstitial microarray gene expression profiles were downloaded from the GEO database (https:// www.ncbi.nlm.nih.gov/GEO /). Data from GSE104954 was used as the query array, including 7DKD patients (GSM2811029-GSM2811035) and 17 live donors (GSM2811044-GSM 2811060). Data from GSE30122 was used for validation. The data was downloaded as raw data and analyzed in the affy package of R version 3.6.2. Analysis of differentially expressed genes was performed using limma software package. The critical value of DEG is defined as the adjusted P value <0.05 and log2FC > 1.
2. The DEGs were functionally analyzed using the online database g, Profile (https:// bit. cs. ut. ee/g Profiler/gost). And demonstrating the data of the top 10 digits in four sub-databases of MF, CC, BP and KEGG by using a ggplot2 software package respectively. IPA software (Qiagen, usa) was also used to explore the pathways involved. Therein, 30 typical paths and top 1 network are shown.
3. Gene expression evaluation and cluster analysis were performed using the WGCNA R package. Gene expression sequence analysis was performed on GSE 104954. The main processes of WGCNA are the construction and module identification of a coexpression network, the identification of disease-related modules and the enrichment analysis of key modules. Modules and phenotype associated genes were screened under Gene Significance (GS) >0.2, Module Member (MM) >0.8 conditions. These genes were further introduced into cytoscap 3.7.2 to identify the pivot genes. The pivot genes are sorted by using cytohubba, and several topological sorting algorithms comprise degree, an edge filtering component (EPC), a Maximum Neighborhood Component (MNC), a maximum neighborhood component Density (DMNC), a maximum population centrality (MCC), a centricity (BN) based on a shortest path, eccentricity, compactness, radial degree, intermediate degree, stress and the like. Subsequently, the intersection of the pivot gene and the DEGs data was taken.
Example 1 pivotal Gene Screen based on public data set WGCNA analysis
The subjects of this example were 7DKD patients (GSM2811029-GSM2811035) and 17 live donors (GSM2811044-GSM2811060) in the GEO platform GSE104954 dataset. The experimental results are as follows:
and downloading a GSE104954 data set in the GEO public data set, and identifying 563 DEGs compared with the LD group after judging that no sample is required to be removed through cluster analysis. These differences were defined as adjusted P values <0.05 and log2FC > 1. Among them, 316 genes were up-regulated and 247 were down-regulated. All genes are shown in FIG. 1, and DEGs are listed in Table 1. The first 30 DEGs in logFC order are listed in Table 1. In addition, the DeGs of the top 10 ranks of logFC are marked on the volcano diagram.
The DEGs are uploaded to g: Profiler to identify GO and KEGG. As shown in fig. 2A, the most involved processes or components in GO include immune system processes, response to external stimuli, immune response (biological processes), external space, external region (cellular components), glycosaminoglycan binding, signal receptor binding, and same protein binding (molecular functions). Mainly related to the KEGG pathway are rheumatoid arthritis, cytokine-cytokine receptor interaction and staphylococcus aureus infection. In addition, the DEGs were analyzed with IPA software. The canonical pathway is enriched for LXR/RXR activation, acute phase response signals, complement system, etc. (figure 2B).
We selected power value 5 as the most recent value of the scale-free topology fit index 0.8 (fig. 3B). We analyzed gene-module and gene-gene associations (fig. 3C, D). Finally we get the module to feature relationship (fig. 3E). The light cyan module was positively correlated with DKD (Pearson correlation ratio of 0.73) with the lowest P value (5 e-5). In addition, genes of the light cyan module were significantly correlated with module membership (fig. 3F).
In the light cyan module, we set GS >0.2 and MM >0.8 as cutoffs. We obtained a group of 773 modular member significantly related genes. These genes were analyzed in cytoscape, using cytoshubba to explore the pivot genes (fig. 4B). Integrating the results of WGCNA and DEGs we finally identified 8 hub genes, VCAN, PTPRC, RASSF5, CASP1, PLAC8, cor 1A, MARCKS and MPEG1 (fig. 4A, C), with the change in VCAN being most pronounced.
Example 2 correlation analysis of VCAN levels with DKD cathepsin expression levels in two public datasets
To further examine the consistent changes in DKD of VCAN, we performed deg analysis in GSE30122 (fig. 5A). VCAN is listed in the first ten of DEGs. The relative mRNA content of VCAN differed significantly between DKD and LD (P value < 0.0001). In addition, immunohistochemical staining of kidney tissue showed higher proteoglycan expression levels in tubulointerstitium from DKD kidney tissue than from live donors (fig. 5B).
xCell performs cell type enrichment analysis from gene expression data of 64 immune and stromal cell types. Our data show that DKD group is higher than LD group in immune score, microenvironment score and matrix score. Of the 64 cell types, there were 35 cell scores that differed between DKD and LD, including monocytes, treg, DC, mast cells, Th2 cells, CD8+ Tem, and like immune cells (fig. 6). Of these 35 cells, 32 cells were associated with VCAN gene expression to varying degrees (fig. 6).
Table 1. the first 30 differentially expressed genes from 7DKD patients and 17 healthy controls.
Combining the above results, the present invention determines that VCAN is a pivotal gene in DKD tubulointerstitial injury by integrating DEG analysis and WGCNA. And further validated in kidney tissue of DKD patients and live donors. Furthermore, based on functional enrichment and signaling pathways, versican is presumed to play a role in immune injury. It is also speculated that versican levels are associated with the accumulation of immune cells on the kidney during DKD progression. After the information is combined with clinical indexes, the information has good prediction value and can be applied to preparation of a reagent or a kit for DKD patient prognosis diagnosis.
Claims (9)
1. The gene VCAN is used as a biomarker in the preparation of a reagent or a kit for predicting the prognosis early warning of diabetic nephropathy.
2. The use according to claim 1, wherein the gene VCAN is 5q14.2-q14.3 as a biomarker at a particular site on the human chromosome.
3. The use of claim 1, wherein a high level of the gene VCAN-expressed protein versican is indicative of an increase in tubulointerstitial inflammatory factors and proinflammatory cells caused by diabetic nephropathy.
Application of versican protein in preparation of reagent or kit for prognosis of diabetic nephropathy.
5. The use of claim 4, wherein said versican protein is encoded by the gene VCAN.
6. The use of claim 4, wherein said versican protein comprises four isoforms V0, V1, V2, V3.
7. The use of claim 4, wherein said versican protein is used as a biomarker to detect tubulointerstitial protein levels.
8. The use of claim 7, wherein a high versican protein level is indicative of a poor prognosis in a diabetic nephropathy patient.
9. The use of claim 7, wherein said versican protein is assayed for protein levels in the tubulointerstitial space by direct or indirect valency flow cytometry.
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| CN202110498848.1A CN113186272A (en) | 2021-05-08 | 2021-05-08 | Application of VCAN as biomarker in DKD prognosis prediction reagent or kit |
| ZA2022/03755A ZA202203755B (en) | 2021-05-08 | 2022-04-01 | Application of gene vcan as a biomarker in preparing reagents and kits for the prognosis prediction of diabetic kidney disease |
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| CN107858417A (en) * | 2017-09-22 | 2018-03-30 | 中国人民解放军南京军区南京总医院 | Detect versicanV1mRNA kit and its application in urine |
| CN111521814A (en) * | 2020-04-14 | 2020-08-11 | 蒋松 | Application of secretory leukocyte protease inhibitor SLPI as diabetic nephropathy DN (DN) prognostic marker |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107858417A (en) * | 2017-09-22 | 2018-03-30 | 中国人民解放军南京军区南京总医院 | Detect versicanV1mRNA kit and its application in urine |
| CN111521814A (en) * | 2020-04-14 | 2020-08-11 | 蒋松 | Application of secretory leukocyte protease inhibitor SLPI as diabetic nephropathy DN (DN) prognostic marker |
Non-Patent Citations (4)
| Title |
|---|
| FANGHAO CAI: "Identification of Key Genes of Human Advanced Diabetic Nephropathy Independent of Proteinuria by Transcriptome Analysis", 《BIOMED RESEARCH INTERNATIONAL》 * |
| MICHAEL RUDNICKI等: "Increased Renal Versican Expression Is Associated with Progression of Chronic Kidney Disease", 《PLOS ONE》 * |
| 耿晓东等: "生物信息学深入分析糖尿病肾脏疾病的分子作用网络", 《中华糖尿病杂志》 * |
| 韩润鸿: "C3a和suPAR调控versican V1过表达在慢性肾小管间质损伤中的作用", 《中国博士学位论文全文数据库（医药卫生科技辑）》 * |
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