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. 2023 Dec 29;102(52):e36802. doi: 10.1097/MD.0000000000036802

Identification of potential crosstalk genes and mechanisms between periodontitis and diabetic nephropathy through bioinformatic analysis

Huijuan Lu a,*, Jia Sun a, Jieqiong Sun a
PMCID: PMC10754619  PMID: 38206700

Abstract

Periodontitis and diabetic nephropathy are significant public health concerns globally and are closely related with each other. This study aimed to identify potential crosstalk genes, pathways, and mechanisms associated with the interaction between periodontitis and diabetic nephropathy. Expression profiles of periodontitis and diabetic nephropathy were retrieved from the Gene expression omnibus gene expression omnibus database, and differentially expressed genes (DEGs) were screened, followed by identification of co-expressed differential genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using R software. A protein-protein interaction network was constructed via STRING website, and key crosstalk genes were selected using Cytoscape. Subsequent gene ontology and KEGG analyses were conducted for the key genes, and a validation dataset was obtained from the gene expression omnibus database for differential gene validation. The TRRUST website was employed to identify transcription factors (TFs) associated with the key crosstalk genes between periodontitis and diabetic nephropathy, followed by differential analysis of TFs. A total of 17 crosstalk genes were obtained. Among them, SAMSN1, BCL2A1, interleukin-19, IL1B, RGS1, CXCL3, CCR1, CXCR4, CXCL1, and PTGS2 were identified as key crosstalk genes between periodontitis and diabetic nephropathy. Additionally, 16 key TFs were discovered. This bioinformatic analysis revealed potential crosstalk genes between periodontitis and diabetic nephropathy. The identified key genes participate in signaling pathways, including cytokine signaling and chemokine signaling transduction, which might collectively influence these 2 diseases. These genes may serve as potential biomarkers guiding future research in this field.

Keywords: bioinformatics, crosstalk genes, diabetic nephropathy, periodontitis

1. Introduction

Periodontitis is an immunoinflammatory disease of the periodontal tissues caused by microbiota imbalance.[1] It affects approximately 61.9% of the global population,[2,3] posing a significant threat to human health. In addition to immune-inflammation, genetic factors play a crucial role in periodontitis, particularly among young patients, and are responsible for up to 50% of periodontitis cases.[4,5] Periodontitis is closely associated with various systemic diseases, including hypertensive heart disease, metabolic disorders such as diabetes, autoimmune diseases, and even cancer.[6–11]

Diabetic nephropathy (DN) represents one of the most prominent public health challenges worldwide. It ranks among the most common causes of end-stage renal disease,[12] contributing to approximately 40% of new patients requiring renal replacement therapy.[13] Diabetic nephropathy is a complex and heterogeneous disease, and its pathogenesis remains incompletely understood. Genetic factors, glomerular hemodynamic changes, oxidative stress and inflammation, as well as interstitial fibrosis and tubular atrophy, are all implicated in the development of diabetic nephropathy.[14–18]

Both periodontitis and diabetic nephropathy impose substantial global public health and financial burdens, particularly in developing economies.[19] Several studies have demonstrated the interaction between periodontitis, diabetes, and chronic kidney disease.[20–25] When hyperglycemia, renal injury, and periodontitis coexist, increased inflammatory cytokines, enhanced insulin resistance, and the degree of periodontal inflammation in diabetic patients directly impact glycemic control and the progression of diabetic nephropathy.[26–29] However, due to the chronic nature of both diseases, influenced by multiple factors and shared risk factors, the precise mechanisms underlying their association remain uncertain. Therefore, it is imperative to further elucidate the intrinsic connection between these 2 diseases, aiming to identify novel targets for effective prevention and treatment strategies.

In this study, we employed bioinformatics analysis to search for crosstalk genes between periodontitis and diabetic nephropathy. We speculated on key crosstalk genes and their associated signaling pathways, thereby exploring the underlying mechanisms of interaction between these 2 diseases. The findings of this investigation may provide valuable insights for future studies in this field.

2. Methods

2.1. Data collection

Expression profile datasets related to periodontitis and diabetic nephropathy were retrieved from the gene expression omnibus database (https://www.ncbi.nlm.nih.gov/geo/). For periodontitis, the experimental set GSE10334 and the validation set GSE16134 were obtained. The GSE10334 dataset included 247 gingival papilla specimens (183 in PERIODONTITIS group and 64 in control group), while the GSE16134 dataset contained 310 gingival papilla specimens (241 in periodontitis group and 69 in control group). For diabetic nephropathy, the GSE142153 experimental set and the GSE30122 validation set were used. The GSE142153 dataset comprised 40 samples (23 DN patients, 7 end-stage renal disease patients and 10 healthy controls), and the GSE30122 dataset consisted of 69 samples (50 DN patients and 19 healthy controls).

2.2. Identification of crosstalk differential genes

Data preprocessing and standardization were performed, followed by identification of differentially expressed genes (DEGs) using the “Limma” package in R (version 4.2.3). DEGs were selected based on |log FC (fold change) | ≥ 0.6 and adjusted P value < .05. Cluster heatmaps and volcano plots of the DEGs were generated using the “pheatmap” and “ggplot2” packages, respectively. Venn diagrams were created using the “VennDiagram” package to identify crosstalk DEGs for further analysis.

2.3. Functional enrichment analysis of crosstalk genes

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the “clusterProfiler,” “enrichplot,” and “ggplot2” packages in R. P < .05 was considered statistically significant.

2.4. Construction of protein-protein interaction network (PPI)

The STRING[30] online platform (http://string.embl.de/) was utilized to construct the PPI network for the identified crosstalk genes. The minimum required interaction score was set at 0.4, representing a moderate level of confidence.

2.5. Identification and functional enrichment analysis of hub genes

Cytoscape software[31] was employed to visualize the PPI network, and significant modules within the network were identified using the MCODE plugin. Significant modules were selected based on degree cutoff = 2, node score cutoff = 0.2, max depth = 100 and k score = 2. Hub crosstalk genes[32] in the network were identified using CytoHubba. GeneMANIA database[33](http://genemania.org) was utilized to generate a network map of hub crosstalk genes. GO and KEGG enrichment analyses were performed on these hub crosstalk genes.

2.6. Validation of hub crosstalk genes

To validate the expression of hub crosstalk genes, the GSE16134 and GSE30122 validation sets were used. The “Limma” package in R was employed for differential expression analysis.

2.7. Identification of transcription factors

Transcriptional regulation involves intricate interactions, including direct binding of transcription factors (TFs) to regulatory elements of target genes and complex relationships between TFs and TF-binding proteins. To explore transcriptional regulation networks, we utilized the TRRUST database[34] (grnpedia.org/trrust/), which provides manually annotated information on transcriptional regulatory interactions. Firstly, we identified the TFs associated with the hub crosstalk genes using the TRRUST database. Subsequently, differential analysis of TFs was performed on the GSE10334 and GSE142153 datasets using the “Limma” package in R software.

3. Results

The research process is depicted in Figure 1.

Figure 1.

Figure 1.

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Flow-chart of datasets analysis in this paper.

3.1. Crosstalk differential genes

Differential analysis identified 588 genes differentially expressed in PERIODONTITIS samples and 440 genes in diabetic nephropathy samples (Fig. 2A–D). Among these, there were 17 crosstalk genes between periodontitis and diabetic nephropathy, including 15 up-regulated genes (CXCR4, FAM46C, CXCL1, RGS1, IL1B, SLC2A3, CCR1, G0S2, SAMSN1, periodontitisE4B, HBD, interleukin-19 (IL-19), BCL2A1, CXCL3, PTGS2) and 2 down-regulated genes (DSC1, KRT2) (Fig. 3).

Figure 2.

Figure 2.

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Identification of differentially expressed genes. (A) A heatmap of the top 100 DEGs in GSE10334, (B) a heatmap of the top 100 DEGs in GSE142153, (C) a volcano plot of DEGs in GSE10334, (D) a volcano plot of DEGs in GSE142153.

Figure 3.

Figure 3.

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Venn diagram of the intersection of PD DEGs and DN DEGs (up and down). DN = diabetic nephropathy, DEGs = differentially expressed genes, PD = periodontitis.

3.2. Functional and enrichment analysis of crosstalk genes

To investigate the underlying biological processes and functions associated with the 17 identified crosstalk genes, we conducted GO and KEGG enrichment analyses. The results revealed significant enrichments in various biological processes, including leukocyte chemotaxis, cell chemotaxis, leukocyte migration, chemokine-mediated signaling pathway, and myeloid leukocyte migration. Additionally, the enriched cellular components included tertiary granule, cornified envelope, and ficolin-1-rich granule membrane. Molecular functions involved cytokine activity, CXCR chemokine receptor binding, and C-C chemokine receptor activity (Fig. 4A–C). KEGG enrichment analysis indicated that the crosstalk genes were significantly enriched in pathways such as viral protein interaction with cytokine and cytokine receptor, NF-kappa B signaling pathway, cytokine-cytokine receptor interaction, IL-17 signaling pathway, and TNF signaling pathway (Fig. 4D–E).

Figure 4.

Figure 4.

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Functional enrichment analyses of the crosstalk genes. (A) GO analysis of the shared genes (TOP10), (B) GO analysis of the shared genes (TOP10) (bar plot), (C) GO analysis of the shared genes (circus plot), (D) KEGG analysis of the shared genes (top10) (bubble plot), (E) KEGG analysis of the shared genes (top10) (bar plot). GO = gene ontology, KEGG = Kyoto encyclopedia of genes and genomes.

3.3. Construction of PPI network

To identify genes with essential functions among the crosstalk genes, we utilized web-based visualization tools such as STRING and Cytoscape to construct a PPI network. The network consisted of 12 nodes and 21 edges (Fig. 5).

Figure 5.

Figure 5.

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PPI network of crosstalk genes. PPI = protein-protein interaction network.

3.4. Identification of hub genes

To determine the key hub genes in the PPI network, we employed the MCODE plugin in Cytoscape to identify significant genes and networks (Fig. 6A). Using the CytoHubba plugin and ranking based on connectivity, we identified SAMSN1, BCL2A1, IL-19, IL1B, RGS1, CXCL3, CCR1, CXCR4, CXCL1, and PTGS2 as the top 10 hub crosstalk genes playing critical roles in the progression of periodontitis and diabetic nephropathy. (Fig. 6B) Functional identification of these hub genes was performed using GeneMANIA, revealing their involvement in cellular response to chemokines, cell chemotaxis, response to chemokine, leukocyte chemotaxis, response to lipopolysaccharide, and cellular response to molecule of bacterial origin (Fig. 6C).

Figure 6.

Figure 6.

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Identification of hub genes. (A) The interaction network of hub genes found by the MCODE plugin, (B) Hub gene map, (C) GeneMANIA network of hub gene function.

3.5. Functional and enrichment analysis of hub genes

To explore the biological processes and functions associated with the hub crosstalk genes, we performed GO and KEGG enrichment analyses. GO enrichment analysis revealed enrichments in biological processes such as leukocyte chemotaxis, chemokine-mediated signaling pathway, and cytokine-mediated signaling pathway. The enriched cellular components were primarily organelle outer membrane, outer membrane, and nuclear outer membrane. Furthermore, the hub genes exhibited molecular functions such as cytokine activity, CXCR chemokine receptor binding, C-C chemokine receptor activity, and G protein-coupled chemoattractant receptor activity (Fig. 7A–C). KEGG enrichment analysis indicated that the hub genes were mainly enriched in the viral protein interaction with cytokine and cytokine receptor, NF-kappa B signaling pathway, and cytokine-cytokine receptor interaction pathway. (Fig. 7D–F).

Figure 7.

Figure 7.

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Functional enrichment analyses of the hub genes. (A) GO analysis of the hub genes (TOP10) (bar plot), (B) GO analysis of the hub genes (TOP10) (bubble plot), (C) GO analysis of the hub genes GO (circus plot), (D) KEGG analysis of the hub genes (bar plot), (E) KEGG analysis of the hub genes (bubble plot), (F) KEGG analysis of the hub genes KEGG (circus plot). GO = gene ontology, KEGG = Kyoto encyclopedia of genes and genomes.

3.6. Validation of hub gene expression

To confirm the expression significance of the hub genes identified in our study, we validated the hub genes in independent datasets GSE16134 and GSE30122. In GSE16134, significant differences were observed in the expression of PTGS2 and SAMSN1 genes between the periodontitis group and control group (Fig. 8A). In the validation set GSE30122, significant differences were observed in the expressions of BCL2A1, CXCL1, CXCR4, SAMSN1, and RGS1 between the diabetic nephropathy group and the control group. However, no significant differences were found in the expressions of IL-19, IL1B, CXCL3, CCR1, and PTGS2 between the 2 groups (Fig. 8B).

Figure 8.

Figure 8.

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Violin plot. (A) Violin plot for periodontitis validation results. (B) Violin plot for diabetic nephropathy validation results.

3.7. Transcription factor analysis of hub genes

TFs play a crucial role in regulating gene expression by binding to specific DNA sequences. To explore the effects of TFs on hub genes and diseases, we utilized TRRUST to identify TFs regulating the hub genes. We found that 6 hub genes, CXCL1, PTGS2, IL1B, BCL2A1, CXCR4, and CCR1, were regulated by 16 TFs, including HMGA1, RELA, NFKB1, STAT1, NFIL3, JUNB, CEBPD, REL, USF2, CEBPB, USF1, HDAC1, CREB1, YY1, JUN, and SP1. To further investigate the relationship between key crosstalk genes and TFs, we constructed a regulatory network comprising 22 nodes and 40 interactions (Fig. 9). Within this network, the PTGS2 gene was regulated by multiple TFs, while the CCR1 gene was regulated by a single TF.

Figure 9.

Figure 9.

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Regulatory network of transcription factors.

To determine the differential expression of TFs regulating hub genes between the disease group and the control group, TF differential expression analysis was performed in the experimental datasets GSE10334 and GSE142153. In GSE10334, significant differences were observed in the expressions of CEBPB, HDAC1, JUN, JUNB, NFIL3, REL, NFKB1, and YY1 between periodontitis patients and the control group. These TFs primarily regulated the genes CXCL1, PTGS2, BCL2A1, IL1B, and CXCR4. However, no significant differences were found in the expressions of HMGA1, RELA, STAT1, USF2, CEBPB, USF1, CREB1, and SP1 between periodontitis patients and controls (Fig. 10A). In GSE142153, significant differences were observed in the expressions of CEBPB, CEBPD, NFIL3, and REL between diabetic nephropathy patients and controls. These TFs mainly regulated the genes IL1B, PTGS2, CXCL1, and BCL2A1. Conversely, there were no significant differences observed in the expression of RELA, NFKB1, STAT1, JUNB, USF2, USF1, CREB1, YY1, JUN, and SP1 in diabetic nephropathy patients and controls (Fig. 10B).

Figure 10.

Figure 10.

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Violin plot. (A) Violin plot for validation results of periodontitis transcription factors, (B) violin plot for validation results of diabetic nephropathy transcription factors.

4. Discussion

Periodontitis is widely recognized as a complex multifactorial disease affected by the interactions between bacteria, host immunity, inflammatory responses, and genetic and environmental factors. The immune-inflammatory response emerges as a pivotal component within this complex framework. A recent review[35] has provided detailed insights into the intricate relationships and underlying mechanisms connecting localized and systemic inflammation in periodontitis with other systemic diseases, including diabetes, inflammatory bowel disease, and Alzheimer disease. Periodontitis can exert systemic effects on the development and progression of various systemic diseases through bacteremia, low-grade systemic inflammation,[36] and increased bone marrow activity. For instance, it can exacerbate insulin resistance and blood glucose abnormalities,[37] leading to the occurrence and progression of diabetic nephropathy. Conversely, systemic diseases such as type 2 diabetes mellitus can increase the inflammatory burden on periodontal tissues, upregulate the expression of pro-inflammatory mediators in local tissues and the circulatory system, and modulate the oral microbiota to promote susceptibility to periodontitis.[36,38,39] These processes interact with each other, forming a vicious cycle. However, the specific mechanisms underlying the mutual influence between periodontitis and diabetic nephropathy remain unclear. Therefore, we employed bioinformatics approaches to explore the crosstalk genes and pathways associated with these 2 diseases, aiming to provide insights into further elucidating the underlying mechanisms.

We investigated RNA-seq datasets obtained from the gene expression omnibus database, identifying DEGs shared between periodontitis and diabetic nephropathy. Protein interaction networks and regulatory networks were constructed to gain a deeper understanding of how these DEGs interact in the context of these 2 diseases. In this study, a total of 17 overlapping DEGs were identified in periodontitis and diabetic nephropathy. Through PPI network analysis and CytoHubba ranking, 10 hub genes were identified, including SAMSN1, BCL2A1, IL-19, IL1B, RGS1, CXCL3, CCR1, CXCR4, CXCL1, and PTGS2. Functional and pathway enrichment analyses revealed that these crosstalk genes were mainly enriched in leukocyte chemotaxis, chemokine-mediated signaling pathway, cytokine-mediated signaling pathway, response to lipopolysaccharide, fever generation, CXCR chemokine receptor binding, signaling receptor activator activity, and G protein-coupled chemoattractant receptor activity.

According to the enrichment results, it is evident that both diseases are highly involved in chemokine signaling pathways and cytokine signaling pathways. Previous studies have shown that chemokines are closely associated with diabetic nephropathy[40] and also play a role in the development of periodontitis.[41] To date, approximately 50 chemokines and 20 chemokine receptors have been identified. Based on the type of cysteine spacing at their N-terminus, chemokines can be classified into C-C, CXC, CX3C, and C types. In diabetic nephropathy animal models, many different families of chemokines, such as CCL2, CCL20, CXCL5, CXCL7, and CXCL12, are up-regulated in glomeruli and proximal tubules.[42,43] In the inflammatory environment of diabetic nephropathy, mesenchymal stem cells can coordinate local and systemic innate and adaptive immune responses through the release of chemokines.[44] Additionally, chemokines also participate in the progression of periodontitis, and several studies targeting chemokine regulation have been conducted for the treatment of periodontitis.[45] The chemokine-mediated pathways may play significant roles in both periodontitis and diabetic nephropathy. Among the 10 hub genes identified in our study, CXCL3, CCR1, CXCR4, and CXCL1 are associated with chemokines. Regarding CXCR4, studies have shown its association with glomerulosclerosis and podocyte injury.[46] In the context of periodontitis, the expression of CXCR4 in peripheral blood myeloid dendritic cells in periodontitis patients is consistently higher compared to healthy controls.[47] Recent animal experimental studies have shown that local injection of CXCR4-miR126-Exo at the site of periodontitis in rats effectively reduces bone resorption and osteoclastogenesis, inhibiting the progression of periodontitis.[48] Regarding CXCL1, recent animal studies have found dysregulated expression of CXCL1 in male mice with insulin resistance and diabetes-associated periodontitis, which regulates neutrophil recruitment. This suggests that CXCL1 may be a novel direction for the treatment of periodontitis or wound healing in diabetes.[49] It is evident that chemokines play important roles in both of these diseases, which aligns with the findings of our study. Additionally, our study revealed the regulatory effect of TFs on CXCR4 and CXCL1. CXCR4 is regulated by 6 TFs: RELA, NFKB1, USF2, USF1, CREB1, and YY1, while CXCL1 is regulated by 5 TFs: HMGA1, RELA, NFKB1, CEBPD, and SP1. This highlights the significant role of TFs in modulating hub genes.

Cytokines also play a pivotal role in both diseases by promoting host immune responses.[50,51] In our study, 10 hub genes were identified, including the inflammatory cytokines IL-19 and IL1B. IL-19 is a newly discovered cytokine belonging to the interleukin-10 family. IL-19 plays an essential role in many inflammatory processes and can induce vascular potential in endothelial cells. Studies have shown that IL-19 can promote the occurrence of diabetic vascular complications,[52] including diabetic nephropathy.[53] IL-19 is expressed in human endothelial cells and can be detected in infiltrating monocytes and macrophages in glomeruli and/or interstitium of kidney tissues from diabetic patients. Therefore, infiltrating macrophages may contribute to elevated levels of IL-19, and IL-19 is involved in the inflammatory response. Additionally, IL-19 levels were significantly elevated in patients with periodontitis compared to healthy controls.[54,55] Based on these findings, we propose a hypothesis that the initiation of inflammation in periodontitis leads to an increase in serum IL-19. Elevated IL-19 may deposit in the glomeruli and/or interstitium of kidney tissues, induce angiogenesis in endothelial cells, exacerbate endothelial cell proliferation, and aggravate diabetic nephropathy. Studies have also shown that interleukin-1β (IL-1β) is a crucial pro-inflammatory cytokine associated with tubulointerstitial inflammation and plays a role in the progression of diabetic nephropathy.[56] Furthermore, studies[57] have confirmed the association between periodontitis and elevated levels of IL-1β by measuring IL-1β levels in periodontal pathogens. Therefore, inhibiting these cytokines may help prevent the development of these inflammatory comorbidities.[35] Our experimental findings revealed that IL1B occupies a central position within the hub genes network and demonstrates connections with multiple hub genes. Furthermore, IL1B is regulated by 10 TFs: HMGA1, RELA, NFKB1, STAT1, NFIL3, JUNB, REL, CEBPB, YY1, and JUN, indicating a complex regulatory mechanism. Among these regulating TFs, NFKB1, NFIL3, JUNB, CEBPB, YY1, and JUN exhibit differential expression in periodontitis, while NFIL3 and REL show differential expression in diabetic nephropathy. Notably, NFIL3 demonstrates differential expression in both periodontitis and diabetic nephropathy datasets, suggesting its potentially important regulatory role in both diseases.

The specific immune mechanisms regulated by chemokine and cytokine-mediated signaling pathways in periodontitis and diabetic nephropathy remain unclear, with multiple possible pathways involved, including effects on macrophage phenotype, kinase systems, and neutrophil mobilization. According to our findings, chemokine and cytokine-mediated signaling pathways significantly contribute to the progression of periodontitis and diabetic nephropathy. However, it is currently unclear which specific signal(s) are responsible. Studies have shown that the chemotaxis and activation of neutrophils are beneficial for host defense against bacterial infection and inflammation. However, excessively active and reactive neutrophils appear to be one of the causes of tissue destruction in periodontitis,[58] suggesting that neutrophil chemotaxis and activation may be a key factor.

From the aforementioned results, it is evident that a substantial portion of the key gene pathways and hub genes analyzed are associated with inflammatory diseases. Therefore, it can be concluded that immune-inflammation serves as the primary mechanism connecting periodontitis and diabetic nephropathy. Periodontitis is initiated by local inflammation, which can exacerbate systemic inflammatory status through various inflammatory factors and chemokines, ultimately accelerating the development of diabetic nephropathy through mechanisms such as elevated blood glucose, insulin resistance, and aggravated endothelial cell angiogenesis. Conversely, diabetic nephropathy increases the inflammatory burden on periodontal tissues, upregulates the expression of pro-inflammatory factors in the tissue and circulatory system, and modulates the dental microbiota to worsen periodontitis. A recent single-center randomized controlled study[59] has shown that effective treatment of periodontitis can improve diabetes and kidney function, suggesting that patient outcomes may benefit from the initial management of inflammation.

In this study, bioinformatics techniques were employed to investigate gene expression and transcription patterns and discover potential biomarkers that may reveal key pathological pathways regulating periodontitis and diabetic nephropathy. Through overlap, core connection, and gene filtering, common DEGs in periodontitis and diabetic nephropathy were identified. Subsequently, we determined signaling pathways and GO processes, ultimately constructing PPI networks of the identified DEGs. Additionally, transcription analysis was used to identify the TFs involved. Therefore, our research may contribute to improving decision-making processes in the field of personalized healthcare. However, this study has several limitations. Due to the exploratory nature of this study, which is based on existing public datasets, extensive validation using clinical samples and further experimental research are still needed to enhance our understanding of these diseases.

5. Conclusion

Our findings demonstrate shared mechanisms between periodontitis and diabetic nephropathy through crosstalk genes, supporting the close interrelationship between periodontitis and diabetic nephropathy. The genes SAMSN1, BCL2A1, IL-19, IL1B, RGS1, CXCL3, CCR1, CXCR4, CXCL1, and PTGS2 participate in immune-inflammatory processes by acting on chemokine and cytokine signaling pathways, thus affecting both diseases. These genes may serve as potential biomarkers guiding future research in this field.

Author contributions

Conceptualization: Huijuan Lu.

Formal analysis: Jieqiong Sun.

Funding acquisition: Jia Sun.

Methodology: Huijuan Lu.

Resources: Jia Sun.

Supervision: Jia Sun.

Writing – original draft: Huijuan Lu, Jieqiong Sun.

Writing – review & editing: Huijuan Lu.

Abbreviations:

DEGs
differentially expressed genes
DN
diabetic nephropathy
GO
gene ontology
IL-19
interleukin-19
IL-1β
interleukin-1β
KEGG
Kyoto encyclopedia of genes and genomes,
PPI
protein-protein interaction network,
TFs
transcription factors

The datasets generated during and/or analyzed during the current study are publicly available.

The authors have no funding and conflicts of interest to disclose.

How to cite this article: Lu H, Sun J, Sun J. Identification of potential crosstalk genes and mechanisms between periodontitis and diabetic nephropathy through bioinformatic analysis. Medicine 2023;102:52(e36802).

Contributor Information

Jia Sun, Email: sunjieqiong1991@163.com.

Jieqiong Sun, Email: sunjieqiong1991@163.com.

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