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Inhibition of complement system-related gene ITGB2 attenuates epithelial–mesenchymal transition and inflammation in diabetic nephropathy

European Journal of Medical Research volume 30, Article number: 87 (2025) Cite this article

Abstract

Purpose

Emerging evidences have indicated a role of the complement system in the pathogenesis of diabetic nephropathy (DN). Thus, this study was conducted to explore the complement system-related key biomarkers for patients with DN.

Methods

DN microarray datasets were downloaded from the GEO database, followed by differentially expressed genes (DEGs) screening. Complement system-related genes (CSRGs) were searched from various databases. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to screen the DN-related genes, then the differential CSRGs (DCSRGs) were identified, followed by protein–protein interaction (PPI) network construction. In addition, key biomarkers were acquired by two machine learning algorithms, then immune infiltration analysis, Gene Set Enrichment Analysis (GSEA), and potential drugs screening were conducted. Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) and western blotting were utilized to detect the ITGB2 expression. Then the cell viability, inflammatory factors, and the expression of epithelial–mesenchymal transition (EMT) and fibrosis markers were determined by using Cell Counting Kit-8 (CCK-8) assay, enzyme linked immunosorbent assay (ELISA), western blotting assays, respectively.

Results

In total, 1012 DEGs and 974 DN-related genes were screened, and intersection analysis of the three (DN-related genes, DEGs and CSRGs) yielded 13 intersection genes, which were considered as the DCSRGs. Subsequently, 2 key biomarkers were identified by machine learning, namely VWF and ITGB2. The VWF and ITGB2 were both enriched in the pathways of chemokine signaling pathway, CAMs, focal adhesion and natural killer cell-mediated cytotoxicity, and significantly correlated with the activated mast cells, resting NK cells, and macrophages. Also, VWF and ITGB2 were significantly related to the clinical features, including age, serum creatinine level, and GFR (MDRD). Besides, mRNA and protein expression levels of ITGB2 in HG-treated HK-2 cells were remarkably elevated. Moreover, the viability of HK-2 cells, expression of TNF-α, IL-6, IL-12, α-SMA, E-cadherin and vimentin in HK-2 cells changed by HG administration were reversed by ITGB2-silence.

Conclusion

Complement system-related gene ITGB2 was overexpressed in DN, and inhibition of ITGB2 attenuated EMT and inflammation in DN.

Introduction

Diabetic nephropathy (DN) is not only a frequent complication of type 1 and type 2 diabetes mellitus [1], but also one of the most important kidney diseases resulting in end-stage renal disease, which has risen to be the first reason of end-stage renal disease worldwide [2, 3]. According to the International Diabetes Federation, the number of diabetes mellitus patients will increase to 700 million by 2045, and the prevalence of DN, as a common complication of diabetes mellitus, is also increasing year by year [4]. Multiple studies have shown that patients with DN have a significantly increased risk of adverse cardiovascular events, infection and death, which greatly increases socio-economic costs and imposes a heavy economic burden on individuals, families and society [5, 6]. At present, the methods for treating DN are limited, mainly through long-term use of sodium-dependent glucose transporter 2 and renin–angiotensin–aldosterone system inhibitors to control blood glucose and blood pressure, alleviate albuminuria, and delay the kidney disease progression [7, 8]. However, studies have shown that these drugs can only delay but not completely prevent the progression of DN, and most patients with DN will eventually develop end-stage renal disease, and their clinical prognosis is not optimistic [9, 10]. Thus, it is necessary to find effective biomarkers for diagnosing and providing effective therapeutic strategies for DN.

The complement system is the immune response system of the body, which is composed of more than 30 kinds of proteins widely existing in serum, interstitial fluid and cell membrane surface [11, 12]. Complement system, as a significant component of the innate immune system, is the first barrier to accelerate inflammatory responses by improving damaged cells and removing the body’s pathogenic microorganisms [13, 14]. Studies have revealed that the occurrence and development of DN are strongly linked to metabolic abnormalities, innate immunity, and chronic inflammation, etc. [15, 16], and complement system is participated in the progression of a range of diseases, including DN [17,18,19]. Mannan-binding lectin (MBL) is a component of the complement system, and study shows that there is a close relationship between MBL level and microalbuminuria in patients with DN [20], which may be related to the change of glycosylation that can deactivate important complement regulatory proteins on the cells surface, leading to abnormal deposition and MBL activation in the kidney. However, how complement system participated in DN pathogenesis remains unclear.

In this study, the key biomarkers for patients with DN were identified using the Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning, as well as verified in vitro. This study will lay the foundation for the diagnosis and treatment of DN.

Materials and methods

Data source and preliminary processing

Gene expression profile datasets GSE30528 (9 DN patients and 13 healthy controls), GSE30529 (10 DN patients and 12 healthy controls) and GSE104954 (7 DN patients and 18 healthy controls) were acquired from GEO database. The “sva” package [21] (v 3.50.0) was used to merge the GSE30528 and GSE30529 datasets as the training set, and GSE104954 was used as the validation set in this study. Besides, complement system-related genes (CSRGs) were searched from Human Biological Pathway Unification (https://pathcards.genecards.org/), HGNC database [22], and MSigDB database [23] with the keyword of “complement”, and a total of 241 CSRGs were obtained (Supplement file 1).

Identification of differentially expressed genes (DEGs)

The “limma” package [24] (v 3.58.1) was employed to screen DEGs between DN and control samples. Benjamini and Hochberg (BH) method was applied to correct the P value, and the P.adj was obtained. The DEGs were screened with the cutoff value of P.adj < 0.05 and |log2 fold change (FC)|> 0.5.

Weighted Gene Co-expression Network Analysis (WGCNA)

The WGCNA method was applied to identify the DN-related genes. The genes with median absolute deviation (MAD) in the 50% were removed after determining the MAD of each gene. The input genes were analyzed by “WGCNA” package [25] (v 1.72–5) to obtain the key modules. Firstly, square value of related coefficient and average connectivity between the connectivity (k) and p(k) for each power value were calculated by setting a series of powers, and then the optimum power value was chosen to confirm that the connections between genes in the network follow a scale-free network distribution. Secondly, the parameter was set to minModulus Size = 100 to cluster highly associated genes into modules by using clustering and dynamic pruning methods. Then modules strongly associated to DN were identified as key modules by calculating the association between the phenotype (DN) and module with the cutoff value of and P < 0.05. The genes in key modules were considered as the DN-related genes. Then the DN-related genes were intersected with DEGs and CSRGs, and differential CSRGs (DCSRGs) were obtained. Subsequently, “clusterProfiler” (v 4.4.4) was used to conduct enrichment analysis on these DCSRGs with the cutoff value of P.adj < 0.05.

Protein–protein interaction (PPI) network construction

STRING database [26] was employed to search the protein interactions of the DCSRGs with the threshold of score > 0.4, then the PPI network was established.

Identification of key biomarkers

“LASSO” and “Best Subsets Regression (BSR)”, were utilized to screen feature genes based on the obtained DCSRGs. “LASSO” and “BSR” were conducted using “glmnet” [27] (v 4.1–8) and “leaps” [28] (v 3.1), respectively. The “LASSO” was performed with the parameters of random.seed = 12345, nfolds = 10, family = “binomial”, and the parameter in “BSR” was set to random.seed = 2025. Then the overlapping feature genes were considered as the key biomarkers. Moreover, the key biomarkers expression levels between DN and control samples were validated in validation dataset. In addition, the correlation between key biomarkers and clinical features was explored by Nephroseq database [29].

Immune infiltration analysis

“CIBERSORT” algorithm [30] was applied to calculate the fraction of 22 immune cells in samples, and Wilcoxon test was applied to explore the difference of the fraction of immune cells between DN and control samples. The correlation between key biomarkers and immune cells were explored.

GSEA

To further explore the relevant signaling pathways and potential biological mechanisms involved in key biomarkers, correlation analysis was conducted between key biomarkers and all other genes. Then the correlation coefficients were sorted, and GSEA [31] was conducted on the key biomarkers with the cutoff value of P.adj < 0.05 and | Normalized Enrichment Score (NES)|> 1.

Potential drug screening

The DSigDB was employed to analyze the interactions between key biomarkers and related drugs by “enrichR” package (v 3.2) [32]. Then “Cytoscape” software [33] was applied to visualize the key biomarkers-drug network.

Cell culture and transfection

HK-2 cells (CL-0109) purchased from Pricella (Wuhan, China) were maintained in DMEM medium containing normal glucose (5.56 mmol/L) and high glucose (HG; 30 mmol/L) [34] for 24 h in DMEM medium with conventional cultivation conditions. Herein, HG was applied to induce the DN cell model. The loss-of-function assays was performed utilizing lentivirus infection to explore the role of ITGB2 in vitro. For transfection, the sequences for the sh-ITGB2 design were obtained from the Designer of Small Interfering RNA website. ITGB2 shRNA, sense: 5’-GGTGAAGACCTACGAGAAA-3’; antisense: 5’-TTTCTCGTAGGTCTTCACC-3’. The HK-2 cells were transfected with lentivirus containing either sh-ITGB2 or its control (sh-NC) for a duration of 24 h, then exposed to DMEM medium containing HG for 24 h. In brief, the cells were categorized into four groups: control, HG, HG + sh-NC, and HG + sh-ITGB2. For cell viability, a commercial CCK-8 kit (C0037, Beyotime, Shanghai, China) was applied to explore.

Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR)

The qRT-PCR process was carried out in accordance with methods outlined earlier [35]. After treatment, the total RNA was separated using Trizol (15,596,018, Invitrogen, USA) and reverse-transcribed into cDNA by FastKing-RT SuperMix (KR118-02; TIANGEN, Beijing, China). Then qRT-PCR was conducted with SYBR Green PCR Master mix (A4004M, Lifeint, China). The primers used were ITGB2 (Human) forward 5’-GATGACGGCTTCCATTTCGC-3’ and reverse 5’-TGGGGATGATCTCGGTGAGT-3’; GAPDH (Human) forward 5’-CCATGGGGAAGGTGAAGGTC-3’, and reverse 5’-AGTGATGGCATGGACTGTGG −3’. GAPDH was employed as an internal control.

Western blotting analysis

Cells were lysed utilizing RIPA buffer (ST506; Beyotime, Shanghai, China) and BCA protein assay kit (P0010S; Beyotime, Shanghai, China) was applied to detect the protein concentration. Then the obtained proteins were isolated utilizing SDS-PAGE and transferred to PVDF membrane (FFP24; Beyotime, Shanghai, China). Subsequently, the membranes were probed with the antibodies against ITGB2 (1:1000; #DF6896, Affinity, USA), E-cadherin (1:1000; #4065, CST, USA), vimentin (1:1000; #5741, CST, USA), α-SMA (1:1000; #14,968, CST, USA), and GAPDH (1:1000; ab181602, Abcam, USA), followed by secondary antibody goat anti-rabbit IgG H&L (HRP) (1:10,000; ab6721, Abcam, USA). The results were assessed by electrochemical luminescence (ECL) reagents (P1000, APPLYGEN, China).

Enzyme linked immunosorbent assay (ELISA)

The cell supernatant was obtained for ELISA assay. The TNF-α (ml064303; Mlbio, Shanghai, China), IL-6 (ml058097; Mlbio, Shanghai, China), and IL-12 (GOY-1653, Goybio, Shanghai, China) were evaluated with ELISA kits.

Statistical analysis

All data were presented as mean ± standard deviation (SD). Differences between groups were evaluated using one-way ANOVA by GraphPad Prism 7.0 software, followed by Tukey’s post hoc test for comparison. P < 0.05 was indicated statistically significant.

Results

DEGs screening between DN and controls

The before and after removing batch effect of datasets are shown in Fig. 1A and B, respectively. After differential expression analysis, totally 1012 DEGs were acquired, including 613 up- and 399 down-regulated DEGs (Fig. 1C and D). In addition, the CSRG score showed significant difference between DN and controls groups (Fig. 1E), suggesting that CSRG exerted a significant function in the occurrence of DN.

Fig. 1
figure 1

Differentially expressed genes (DEGs) screening between diabetic nephropathy (DN) and controls. Before (A) and after (B) removing batch effect of datasets. Volcano plot (C) and heatmap (D) of top 50 DEGs between DN and control samples. E The difference of complement system-related genes (CSRGs) score between DN and control groups

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Identification of DCSRGs

WGCNA was applied to screen the DN-related genes. The scale-free fit index was set to 0.82 to acquire the minimum “soft” threshold of 12 (Fig. 2A). After calculating the adjacency and dissimilarity coefficients between genes (MEDissThres = 0.3), 10 modules were combined (Fig. 2B) when the parameter set to minModulus Size = 100, and blue module was strongly related to DN (Fig. 2C). Then a total of 974 genes in blue module were considered as the DN-related genes. Subsequently, total 13 overlapping genes were obtained in DN-related genes, DEGs and CSRGs, namely VWF, ITGB2, C1QB, C7, VSIG4, FCN1, F12, KNG1, C1S, C3AR1, C3, C1QA, and C1RL, which were considered as the DCSRGs (Fig. 2D).

Fig. 2
figure 2

Identification of differential complement system-related genes (DCSRGs). A Left: scale-free fit index (proportional independence, y-axis) as a function of soft threshold power (x-axis); right: mean connectivity (degrees, y-axis) as a function of soft threshold power (x-axis). B Gene dendrogram as a result of clustering, where the colored rows below the dendrogram denote the module assignment identified by dynamic tree shearing. C Module–DN correlations. D Venn diagram of the DCSRGs

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Enrichment analysis and PPI network establishment

Enrichment analysis was conducted to investigate the biological function of DCSRGs. GO analysis revealed that these DCSRGs enriched in 234 GO terms, mainly involving in complement activation, humoral immune response, and others complement/immune-related functions (Fig. 3A). Also, KEGG pathways results demonstrated that the enriched pathways largely involved complement and coagulation cascades, Staphylococcus aureus infection, and systemic lupus erythematosus (Fig. 3B). Besides, PPI network of the DCSRGs were established (Fig. 3C), containing 13 nodes and 46 edges.

Fig. 3
figure 3

Enrichment analysis and protein–protein interaction (PPI) network establishment. Top 10 Gene Ontology (GO) terms (A) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (B) of 13 differential complement system-related genes (DCSRGs) enriched. Among the enriched GO terms, blue bars represent Biological Process (BP) terms, green bars represent Cell Component (CC) terms, and orange bars represent Molecular Function (MF) terms. C Protein–protein interaction (PPI) network. Blue nodes indicate downregulated genes, and red nodes indicate upregulated genes

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Key biomarkers screening based on machine learning algorithms

Machine learning has been used in bioinformatics, providing insights into their role in enhancing our understanding of human diseases [36,37,38]. Two machine learning algorithms were utilized to identify key biomarkers. In total, 6 (VWF, ITGB2, C7, VSIG4, KNG1, C3) and 3 (VWF, C1QB, ITGB2) feature genes were obtained by “LASSO” and “BSR” algorithms, respectively (Fig. 4A and B). Notably, 2 common genes were obtained (Fig. 4C), namely VWF and ITGB2, which were considered as the key biomarkers. The AUCs of key biomarkers were all above 0.85 in training set (Fig. 4D), as well as above 0.75 in validation set (Fig. 4E). Furthermore, the VWF and ITGB2 expression levels both remarkably elevated in DN group when compared to those in control group both in training and validation sets (Fig. 4F and G). Also, VWF and ITGB2 were significantly related to the clinical features, including age, serum creatinine level, and GFR(MDRD) (Fig. 4H).

Fig. 4
figure 4

Key biomarkers screening based on machine learning algorithms. Feature genes were obtained using “LASSO” (A) and “BSR” (B). C Venn diagram of key biomarkers. Receiver operating characteristic (ROC) curve of key biomarkers in training (D), and verification (E) sets. The expression levels of key biomarkers between DN and control groups in training (F) and validation (G) sets. *P < 0.05, ****P < 0.0001. H The correlation between key biomarkers and clinical features explored by Nephroseq database

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GSEA, immune infiltration analysis and potential drug screening

GSEA uncovered that VWF and ITGB2 were largely involved in the pathways of chemokine signaling pathway, cell adhesion molecules (CAMs), focal adhesion and natural killer cell-mediated cytotoxicity (Fig. 5A and B). In addition, the relative fraction of immune cells in DN and controls is illustrated in Fig. 5C, and activated mast cells, macrophages, resting NK cells showed significantly difference between DN and controls samples (Fig. 5D). Notably, VWF and ITGB2 were significantly correlated with the activated mast cells, macrophages, and resting NK cells (Fig. 5E). Moreover, in total 98 potential drugs related to VWF and ITGB2 were searched, then the key biomarkers–drug network was constructed (Fig. 5F), including 100 nodes and 107 edges.

Fig. 5
figure 5

Gene Set Enrichment Analysis (GSEA), immune infiltration analysis and potential drug screening. GSEA analysis of VWF (A) and ITGB2 (B). C The relative proportion of immune cells in diabetic nephropathy (DN) and controls. D The fraction of immune cells shown significant difference between DN and normal samples. E The correlation between 2 key biomarkers and immune cells. F Drug–key biomarkers network. Red nodes indicate key biomarkers, and yellow nodes indicate drugs

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Influence of ITGB2 on viability, EMT, fibrosis, and inflammation in HG-treated HK-2 cells

After bioinformatics analysis, the results found two key biomarkers, namely VWF and ITGB2. It is reported that the VWF expression level was elevated in DN, and inhibition of VWF could improve kidney function in diabetes mice [39]. However, no studies have reported the influence of ITGB2 in DN. Thus, the influences of ITGB2 in DN were explored. The ITGB2 mRNA and protein expression levels in HG-treated HK-2 cells were remarkably elevated (P < 0.01; Fig. 6A–C). Considering the overexpression expression of ITGB2 in HG-treated HK-2 cells, the HG-treated HK-2 cells were transfected with sh-ITGB2, and the transfection efficiency was measured (Fig. 6B–C). Then the viability of cells was explored by CCK-8, and the CCK-8 result showed that the HK-2 cells viability was significantly attenuated by HG compared to control group, and it was significantly increased after inhibition of ITGB2 (both P < 0.05; Fig. 6D). In addition, epithelial–mesenchymal transition (EMT) markers, containing E-cadherin and vimentin were analyzed by western blotting, which illustrated that EMT was significantly increased after HG treatment, and it was significantly mitigated by sh-ITGB2 implement (both P < 0.01), as well as the protein level of fibrosis marker α-SMA (Fig. 6E). Also, TNF-α, IL-6, and IL-12 were detected, which indicated that inflammation was significantly enhanced after HG administration, while it was reverse by ITGB2 knockdown (most P < 0.01; Fig. 6F). These data collectively suggested the inhibition of ITGB2 attenuated EMT, fibrosis, and inflammation in HG-treated HK-2 cells.

Fig. 6
figure 6

Influence of ITGB2 on viability, epithelial–mesenchymal transition (EMT), fibrosis, and inflammation in HG-treated HK-2 cells. A The mRNA expression levels of ITGB2 in HG-treated HK-2 cells. Transfection efficiency was detected by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) (B) and western blotting analysis (C). D The proliferation of cells was detected using Cell Counting Kit-8 (CCK-8) assay. E EMT and fibrosis markers, containing E-cadherin, vimentin and αSMA were analyzed by western blotting analysis. F TNF-α, IL-6, and IL-12 expression levels detected by enzyme linked immunosorbent assay (ELISA). **P < 0.01, compared with control group; #P < 0.05, ##P < 0.01, compared with HG + sh-NC group

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Discussion

DN is one of the major causes of death in diabetes mellitus patients [40]. Numerous studies have suggested that complement system exerts significant functions in the pathogenesis of DN [41,42,43]. Therefore, screening specific complement system-related biomarkers might lead to identification of potential diagnostic and therapeutic targets for DN patients. In this study, two complement system-related key biomarkers were identified by WGCNA and machine learning, namely VWF and ITGB2. In addition, the AUCs of VWF and ITGB2 were all above 0.75 in training and validation sets, indicating good predictive performance. Furthermore, the VWF and ITGB2 expression levels both significantly elevated in DN group when compared to those in control group both in training and validation sets, and VWF and ITGB2 were significantly related to the clinical features. The results of this study are in line with numerous reported studies, which revealed that the level of VWF antigen is elevated in type 1 and type 2 diabetes mellitus patients with DN [39, 44, 45]. In addition, Hu et al. using bioinformatics analysis found that ITGB2 was upregulated in DN samples, which could be used as a underlying diagnostic gene for DN [46], as well as the study reported by Xu et al. [29], which were further confirmed that VWF and ITGB2 might viable biomarkers in DN. Besides, GSEA uncovered that VWF and ITGB2 were largely involved in the pathways of chemokine signaling pathway, CAMs, focal adhesion and natural killer cell-mediated cytotoxicity. Thus, VWF and ITGB2 might exert vital functions in the occurrence and development of DN through chemokine signaling pathway, CAMs, focal adhesion and natural killer cell-mediated cytotoxicity pathway.

As we all know, immunity is one of the key mechanisms in the DN progression. Hyperglycemia is a key factor contributing to kidney damage in patients with DN [47]. The stress caused by sustained hyperglycemia can lead to the massive production of inflammatory molecules and the immune complexes accumulation, which is strongly associated to immune cells such as NK cells, mast cells, and T cells [48]. The results of immune infiltration analysis in this study also indicated that VWF and ITGB2 were significantly correlated with the activated mast cells, macrophages, and resting NK cells. It is also reported that macrophages are the main innate immune cells in DN, and their presence in interstitium and glomeruli can be observed in both clinical trials and experimental models of DN [49, 50]. Thus, VWF and ITGB2 might the therapeutic targets for DN immunotherapy. Moreover, in total 98 potential drugs related to VWF and ITGB2 were searched. Among which, progesterone CTD 00006624, ACMC-20mvek CTD 00002629, and 9001–31-4 BOSS, etc., both targeted VWF and ITGB2, which suggesting that progesterone CTD 00006624, ACMC-20mvek CTD 00002629, and 9001–31-4 BOSS, etc., that targeted VWF and ITGB2 might have therapeutic potential for DN.

Besides, this study further verified the key biomarkers in vitro. However, emerging evidences have revealed a role for VWF in the pathogenesis of DN; no studies have reported the specific influence of ITGB2 in DN. Thus, the influences of ITGB2 in DN were explored in depth in this study. HG is a recognized important risk factor for DN and is closely related to the pathology and progression of DN [51, 52]. Therefore, HK-2 cells were maintained in HG (30 mM) medium to build a cellular model of DN [53]. Consistent with the results of bioinformatics analysis, ITGB2 mRNA and protein expression levels in HG-treated HK-2 cells were remarkably elevated. EMT exerts significant functions in interstitial matrix deposition in DN [54]. Up to 30–40% of type 1 or type 2 diabetes mellitus patients will develop to DN with characterized by of ECM protein accumulation [55, 56]. The enhanced ECM deposition then leads to renal fibrosis, ultimately resulting in tubulointerstitial fibrosis, infiltration of inflammatory mediators, and reactivation of α-SMA-positive myofibroblasts [57, 58]. In this study, the viability of HK-2 cells, expression of inflammatory regulators, fibrosis marker α-SMA, and EMT markers in HK-2 cells changed by HG administration were reversed by ITGB2-silence. Taken together, these data suggested that inhibition of ITGB2 alleviated cell viability, EMT, fibrosis, and inflammation in HG-treated HK-2 cells. In addition, the US-FDA have supported numerous siRNA delivery systems for various diseases treatment [59,60,61], whether nanoparticle loading siITGB2 could be used for clinical therapy of DN need further investigated.

This study still has numerous limitations. Firstly, the 241 CSRGs were obtained from Human Biological Pathway Unification, HGNC, and MSigDB databases, which is being updated continuously, and more CSRGs should be discovered. In addition, the molecular mechanisms of ITGB2 in DN should be further explored. Lastly, whether the identified drugs that targeted VWF and ITGB2 could be suitable for clinical application in DN treatment also need to be deeply investigated at the clinical level.

Conclusion

In conclusion, complement system-related gene ITGB2 was overexpressed in DN, which was a potential diagnostic and therapeutic target in DN. In addition, inhibition of ITGB2 mitigated EMT and inflammation in DN. This study offers novel insights for DN diagnosis and treatment.

Availability of data and materials

The raw data of bioinformatics analyses of current study are openly available in public databases listed within the article. The raw data of experiments are available on reasonable requests.

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  1. Nephrology Department, Central Theater Command General Hospital of the Chinese People’s Liberation Army, No.627, Wuluolu, Wuchang District, Wuhan, 430070, Hubei, China

    Jun Peng, Wenqi Zhao, Lu Zhou & Kun Ding

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  2. Wenqi Zhao
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  3. Lu Zhou
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  4. Kun Ding
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Contributions

Conception and design of the research: Jun Peng; Acquisition of data: Wenqi Zhao, Lu Zhou; Analysis and interpretation of data: Wenqi Zhao, Lu Zhou; Statistical analysis: Wenqi Zhao, Kun Ding; Drafting the manuscript: Jun Peng; Revision of manuscript for important intellectual content: Jun Peng, Wenqi Zhao, Lu Zhou, Kun Ding. All authors reviewed the results and approved the final version of the manuscript.

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Peng, J., Zhao, W., Zhou, L. et al. Inhibition of complement system-related gene ITGB2 attenuates epithelial–mesenchymal transition and inflammation in diabetic nephropathy. Eur J Med Res 30, 87 (2025). https://doi.org/10.1186/s40001-025-02323-x

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European Journal of Medical Research

ISSN: 2047-783X

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