Influence of MPO/NLRP3 Axis-Mediated Neutrophil Extracellular Traps on Diabetic Neuropathic Pain in a Mouse Model.

The World Health Organisation estimated that in the year 2000, 150 million people had diabetes mellitus, and it is predicted that this number will rise to 366 million by the year 2030.[1][1] Neuropathy is a common complication of diabetes and is characterised by a progressive loss of peripheral

Diabetic neuropathy is one of the most common chronic complications and is present in approximately 50% of diabetic patients. A bioinformatic approach was used to analyze candidate genes involved in diabetic distal symmetric polyneuropathy and their potential mechanisms. GSE95849 was downloaded from the Gene Expression Omnibus database for differential analysis, together with the identified diabetic peripheral neuropathy-associated genes and the three major metabolism-associated genes in the CTD database to obtain overlapping Differentially Expressed Genes (DEGs). Gene Set Enrichment Analysis and Functional Enrichment Analysis were performed. Protein–Protein Interaction and hub gene networks were constructed using the STRING database and Cytoscape software. The expression levels of target genes were evaluated using GSE24290 samples, followed by Receiver operating characteristic, curve analysis. And Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the target genes. Finally, mRNA-miRNA networks were constructed. A total of 442 co-expressed DEGs were obtained through differential analysis, of which 353 expressed up-regulated genes and 89 expressed down-regulated genes. The up-regulated DEGs were involved in 742 GOs and 10 KEGG enrichment results, mainly associated with lipid metabolism-related pathways, TGF-β receptor signaling pathway, lipid transport, and PPAR signaling pathway. A total of 4 target genes (CREBBP, EP300, ME1, CD36) were identified. Analysis of subject operating characteristic curves indicated that CREBBP (AUC = 1), EP300 (AUC = 0.917), ME1 (AUC = 0.944) and CD36 (AUC = 1) may be candidate serum biomarkers for DPN. Conclusion: Diabetic peripheral neuropathy pathogenesis and progression is caused by multiple pathways, which also provides clinicians with potential therapeutic tools.

BackgroundDiabetic peripheral neuropathy (DPN) is a prevalent and serious complication of diabetes mellitus, impacting the nerves in the limbs and leading to symptoms like pain, numbness, and diminished function. While the exact molecular and immune mechanisms underlying DPN remain incompletely understood, recent findings indicate that mitochondrial dysfunction may play a role in the advancement of this diabetic condition.MethodsTwo RNA transcriptome datasets (codes: GSE185011 and GSE95849), comprising samples from diabetic peripheral neuropathy (DPN) patients and healthy controls (HC), were retrieved from the Gene Expression Omnibus (GEO) database hosted by the National Center for Biotechnology Information (NCBI). Subsequently, differential expression analysis and gene set enrichment analysis were performed. Protein–protein interaction (PPI) networks were constructed to pinpoint key hub genes associated with DPN, with a specific emphasis on genes related to mitochondria and peripheral neuropathy disease (PND) that displayed differential expression. Additionally, the study estimated the levels of immune cell infiltration in both the HC and DPN samples. To validate the findings, quantitative polymerase chain reaction (qPCR) was employed to confirm the differential expression of selected genes in the DPN samples.ResultsThis research identifies four hub genes associated mitochondria or PN. Furthermore, the analysis revealed increased immune cell infiltration in DPN tissues, particularly notable for macrophages and T cells. Additionally, our investigation identified potential drug candidates capable of regulating the expression of the four hub genes. These findings were corroborated by qPCR results, reinforcing the credibility of our bioinformatics analysis.ConclusionsThis study provides a comprehensive overview of the molecular and immunological characteristics of DPN, based on both bioinformatics and experimental methods.

Diabetic peripheral neuropathy (DPN) is a common diabetic complication with a obscure and complicated pathogenesis. Here, we determined that 5-hydroxytryptamine receptor 2 A (5HT2AR) is selectively and pathologically elevated in the sciatic nerve (SN) tissues of late-stage DPN patients and the SN/dorsal root ganglion (DRG) tissues of late-stage DPN model mice (both type 1 and 2 diabetic mice) compared with the other 13 subtypes of 5HTR, and desloratadine (DLT) as a discovered selective antagonist of 5HT2AR ameliorated the DPN-like pathology in mice. To elucidate the underlying mechanisms, proteomic analysis was performed against the SN tissues of DPN mice and verified by assays against both the SN tissues from DPN patients and the DPN mice with specific 5HT2AR knockdown in the SN and DRG tissues by injection of the adeno-associated virus php.s-5HT2AR-RNAi. We determined the activation of the neutrophil extracellular traps (NETs) that is tightly linked to the vascular dysfunction and neuroinflammation in DPN pathology. NETs regulated vascular dysfunction through NE/TF/HIF-1α/MMP-9/2 pathway, and DLT-mediated 5HT2AR inhibition suppressed NETs activation through PKC/NADPH/ROS pathway. Collectively, our findings revealed that 5HT2AR associates with NETs activation in DPN pathology and supported that pharmacological inhibition of 5HT2AR shows promising as a therapeutic strategy for DPN and DLT exhibits high potential in treating the disease.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12974-025-03581-6.

Although diabetic peripheral neuropathy (DPN) is a serious complication of diabetes, its molecular causes are still unclear. The aim of this study was therefore to further investigate the pathogenesis of diabetes through whole transcriptome sequencing. Methods: Based on the whole transcriptome sequencing data, the differentially expressed genes (DEGs) between the DPN and the control groups were identified, and functions and pathways in which DEGs were enriched were analyzed. Thereafter, the protein-protein interaction (PPI) network was built, and the top 20 hub genes were screened via STRING algorithm. Moreover, enrichment analysis for the top 20 hub genes was subsequently performed. Finally, the competing endogenous RNA (ceRNA) network was created. Results: There were 597 DEGs between the DPN group and control groups. The top 20 hub genes were Fos, Tnf, Jun, Cd3e, CD79a, Cd27, Cd79b, Ptprc, Cd22, Cd40lg, Cd19, Cr2, Sell, Il6, Pdlim3, Actn2, Myom2, Tnni2, Ldb3, and Myh7. According to the enrichment analysis, DEGs and hub genes were enriched into ‘B cell receptor signaling pathway’, ‘TNF signaling pathway’, ‘T cell receptor signaling pathway’,‘Th17 cell differentiation’,‘IL-17 signaling pathway’,‘primary immunodeficiency’, and ‘toll-like receptor signaling pathway’. The ceRNA network finally contained 9 mRNAs, 23 miRNAs, and 23 lncRNAs. Conclusion: The findings of this study could provide novel insights into the roles of DEGs in the pathogenesis of DPN.

IntroductionDiabetes mellitus (DM) patients suffer from high morbidity and premature mortality due to various diabetic complications and even cancers. Therefore, this study aimed to identify key genes involved in the pathogenesis of diabetic peripheral neuropathy (DPN) and pancreatic cancer (PC).MethodsWe analyzed three gene expression profiles (GSE95849, GSE28735 and GSE59953) to obtain differentially expressed genes (DEGs). Then, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was then used to establish a protein–protein interaction (PPI) network. The MCODE and cytoHubba plug-ins of Cytoscape were used to select hub genes. Finally, survival analysis of the hub genes was performed using the Kaplan–Meier plotter and GEPIA online tool.ResultsWe first analyzed GSE95849 to obtain DPN-related genes. DEGs were obtained from three groups in GSE95849. The DEGs were enriched in the Toll-like receptor signaling pathway, hematopoietic cell lineage and chemokine signaling pathway. Importantly, we identified three shared genes as hub genes, including TLR4, CCR2 and MMP9. We then analyzed and integrated GSE95849 and GSE28735 to obtain genes common in DM and PC. A total of 58 mutual DEGs were identified, and these DEGs were enriched in the ECM-receptor interaction, focal adhesion and pathways in cancer. Five hub genes (including PLAU, MET, CLU, APOL1 and MMP9) were associated with the overall survival of PC patients. However, the results from the analysis of GSE59953 showed that hyperglycemia or TGF-β1 treatment did not affect the expression level of these hub genes, but the DEGs based on hyperglycemia or TGF-β1 treatment were mostly enriched in the ECM-receptor interaction, focal adhesion and pathways in cancer. Finally, functional enrichment analysis of MMP9 showed that significant genes correlated with MMP9 were associated with the tumorigenicity of cancers, insulin resistance, development of DM and inflammation.ConclusionIn summary, inflammation and immunity-related pathways may play an important role in DM and DPN, while the ECM-receptor interaction, focal adhesion and pathways in cancer pathways may play significant roles in DM and PC. MMP9 may be used as a prognostic marker for PC and may be helpful for the treatment of DM, DPN and PC.

Diabetic peripheral neuropathy (DPN) is a common complication of Type 2 diabetes mellitus (T2DM), which frequently results in disabling neuropathic pain and lower-limb amputation. The identification of noninvasive biomarkers for DPN may help early detection and individualized treatment of DPN. In this study, we identified differentially expressed genes (DEGs) between DPN and the control based on blood-source (GSE95849) and tissue-source gene expression profiles (GSE143979) from the Gene Expression Omnibus (GEO) database using limma, edgeR, and DESeq2 approaches. KEGGG and GO functional enrichments were performed. Hub genes and their correlation with infiltrating immune cells were analyzed. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to quantify hub gene expression. In total, 144 DEGs between DPN and the control were identified. Functional enrichment revealed that the DEGs were mainly enriched in immune-related pathways like the Fc epsilon receptor Ig signaling pathway. By protein-protein interaction (PPI) network analysis, FCER1G, SYK, ITGA4, F13A1, MS4A2, and PTK2B were screened as hub genes with higher expression in DPN patients, among which half were immune genes (FCER1G, PTK2B, and SYK). RT-qPCR demonstrated that mRNA expression of FCER1G, PTK2B, and SYK was significantly increased in patients with DPN compared with both diabetic nonperipheral neuropathy (DNN) and normal subjects. The area under the receiver operating characteristic (ROC) curve of FCER1G, PTK2B, and SYK was 0.84, 0.81, and 0.73, respectively, suggesting their great advantages as diagnostic biomarkers to predict the progression of neuropathy in T2DM. Further analysis indicated that the expression of FCER1G, PTK2B, and SYK was negatively correlated with the cell proportion of significantly altered resting natural killer cells, T follicular helper cells, and activated mast cells, but positively correlated with monocytes. Our findings demonstrated FCER1G, PTK2B, and SYK are potential diagnostic biomarkers and therapeutic targets for DPN, which provides new insight into DPN pathogenesis and therapies.

Diabetic peripheral neuropathy (DPN) is a common complication associated with diabetes, and can affect quality of life considerably. Dorsal root ganglion (DRG) plays an important role in the development of DPN. However, the relationship between DRG and the pathogenesis of DPN still lacks a thorough exploration. Besides, a more in-depth understanding of the cell type composition of DRG, and the roles of different cell types in mediating DPN are needed. Here we conducted single-cell RNA-seq (scRNA-seq) for DRG tissues isolated from healthy control and DPN rats. Our results demonstrated DRG includes eight cell-type populations (e.g., neurons, satellite glial cells (SGCs), Schwann cells (SCs), endothelial cells, fibroblasts). In the heterogeneity analyses of cells, six neuron sub-types, three SGC sub-types and three SC sub-types were identified, additionally, biological functions related to cell sub-types were further revealed. Cell communication analysis showed dynamic interactions between neurons, SGCs and SCs. We also found that the aberrantly expressed transcripts in sub-types of neurons, SGCs and SCs with DPN were associated with diabetic neuropathic pain, cell apoptosis, oxidative stress, etc. In conclusion, this study provides a systematic perspective of the cellular composition and interactions of DRG tissues, and suggests that neurons, SGCs and SCs play vital roles in the progression of DPN. Our data may provide a valuable resource for future studies regarding the pathophysiological effect of particular cell type in DPN.

Diabetic Peripheral Neuropathy (DPN) poses an escalating threat to public health, profoundly impacting well-being and quality of life. Despite its rising prevalence, the pathogenesis of DPN remains enigmatic, and existing clinical interventions fall short of achieving meaningful reversals of the condition. Notably, neurostimulation techniques have shown promising efficacy in alleviating DPN symptoms, underscoring the imperative to elucidate the neurobiochemical mechanisms underlying DPN. This study employs an integrated multi-omics approach to explore DPN and its response to neurostimulation therapy. Our investigation unveiled a distinctive pattern of vesicular glutamate transporter 2 (VGLUT2) expression in DPN, rigorously confirmed through qPCR and Western blot analyses in DPN C57 mouse model induced by intraperitoneal Streptozotocin (STZ) injection. Additionally, combining microarray and qPCR methodologies, we revealed and substantiated variations in the expression of the Amyloid Precursor Protein (APP) family in STZ-induced DPN mice. Analyzing the transcriptomic dataset generated from neurostimulation therapy for DPN, we intricately explored the differential expression patterns of VGLUT2 and APPs. Through correlation analysis, protein-protein interaction predictions, and functional enrichment analyses, we predicted the key biological processes involving VGLUT2 and the APP family in the pathogenesis of DPN and during neurostimulation therapy. This comprehensive study not only advances our understanding of the pathogenesis of DPN but also provides a theoretical foundation for innovative strategies in neurostimulation therapy for DPN. The integration of multi-omics data facilitates a holistic view of the molecular intricacies of DPN, paving the way for more targeted and effective therapeutic interventions.

The mechanism of Nicotinamide Adenine Dinucleotide (NAD+) metabolism-related genes (NMRGs) in diabetic peripheral neuropathy (DPN) is unclear. This study aimed to find new NMRGs biomarkers in DPN. DPN related datasets GSE95849 and GSE185011 were acquired from the Gene Expression Omnibus (GEO) database. 51 NMRGs were collected from a previous article. To explore NMRGs expression in DPN and control samples, differential expression analysis was completed in GSE95849 to obtain differentially expressed genes (DEGs), and the intersection of DEGs and NMRGs was regarded as DE-NMRGs. Next, a protein-protein interaction (PPI) network based on DE-NMRGs was constructed and biomarkers were screened by eight algorithms. Additionally, Gene Set Enrichment Analysis (GSEA) enrichment analysis was completed, biomarker-based column line graphs were constructed, lncRNA-miRNA-mRNA and competing endogenouse (ce) RNA networks were constructed, and drug prediction was completed. Finally, biomarkers expression validation was completed in GSE95849 and GSE185011. 5217 DEGs were obtained from GSE95849 and 21 overlapping genes of DEGs and NMRGs were DE-NMRGs. Functional enrichment analysis revealed that DE-NMRGs were associated with glycosyl compound metabolic process. The PPI network contained 93 protein-interaction pairs and 21 nodes, with strong interactions between NMNAT1 and NAMPT, NADK and NMNAT3, ENPP3 and NUDT12 as biomarkers based on 8 algorithms. Expression validation suggested that ENPP3 and NUDT12 were upregulated in DPN samples (P < 0.05). Moreover, an alignment diagram with good diagnostic efficacy based on ENPP3 and NUDT12 were identified was constructed. GSEA suggested that ENPP3 was enriched in Toll like receptor (TLR) pathway, NUDT12 was enriched in maturity onset diabetes of the young and insulin pathway. Furthermore, 18 potential miRNAs and 36 Transcription factors (TFs) were predicted and the miRNA-mRNA-TF networks were constructed, suggesting that ENPP3 might regulate hsa-miR-34a-5p by affecting MYNN. The ceRNA network suggested that XLOC_013024 might regulate hsa-let-7b-5p by affecting NUDT12. 15 drugs were predicted, with 8 drugs affecting NUDT12 such as resveratrol, and 13 drugs affecting ENPP3 such as troglitazone. ENPP3 and NUDT12 might play key roles in DPN, which provides reference for further research on DPN.

Diabetic peripheral neuropathy (DPN) affects up to 50% of patients with diabetes and is a major cause of morbidity and increased mortality. Its clinical manifestations include painful neuropathic symptoms and insensitivity, which increases the risk for burns, injuries and foot ulceration. Several recent studies have implicated poor glycaemic control, duration of diabetes, hyperlipidaemia (particularly hypertryglyceridaemia), elevated albumin excretion rates and obesity as risk factors for the development of DPN. Although there is now strong evidence for the importance of nerve microvascular disease in the pathogenesis of DPN, the risk factors for painful DPN are not known. However, emerging evidence regarding the central correlates of painful DPN is now afforded by brain imaging. The diagnosis of DPN begins with a careful history of sensory and motor symptoms. The quality and severity of neuropathic pain if present should be assessed using a suitable scale. Clinical examination should include inspection of the feet and evaluation of reflexes and sensory responses to vibration, light touch, pinprick and the 10-g monofilament. Glycaemic control and addressing cardiovascular risk is now considered important in the overall management of the neuropathic patient. Pharmacological treatment of painful DPN includes tricyclic compounds, serotonin-norepinephrine reuptake inhibitors (e.g. duloxetine), anticonvulsants (e.g. pregabalin), opiates, membrane stabilizers, the antioxidant alpha lipoic acid and others. Over the past 7 years, new agents with perhaps less side effect profiles have immerged. Management of patients with painful neuropathy must be tailored to individual requirements and will depend on the presence of other co-morbidities. There is limited literature with regard to combination treatment.

BackgroundDiabetes mellitus is a worldwide disease with high incidence. Diabetic peripheral neuropathy (DPN) is one of the most common but often ignored complications of diabetes mellitus that cause numbness and pain, even paralysis. Recent studies demonstrate that Schwann cells (SCs) in the peripheral nervous system play an essential role in the pathogenesis of DPN. Furthermore, various transcriptome analyses constructed by RNA-seq or microarray have provided a comprehensive understanding of molecular mechanisms and regulatory interaction networks involved in many diseases. However, the detailed mechanisms and competing endogenous RNA (ceRNA) network of SCs in DPN remain largely unknown.MethodsWhole-transcriptome sequencing technology was applied to systematically analyze the differentially expressed mRNAs, lncRNAs and miRNAs in SCs from DPN rats and control rats. Gene ontology (GO) and KEGG pathway enrichment analyses were used to investigate the potential functions of the differentially expressed genes. Following this, lncRNA-mRNA co-expression network and ceRNA regulatory network were constructed by bioinformatics analysis methods.ResultsThe results showed that 2925 mRNAs, 164 lncRNAs and 49 miRNAs were significantly differently expressed in SCs from DPN rats compared with control rats. 13 mRNAs, 7 lncRNAs and 7 miRNAs were validated by qRT-PCR and consistent with the RNA-seq data. Functional and pathway analyses revealed that many enriched biological processes of GO terms and pathways were highly correlated with the function of SCs and the pathogenesis of DPN. Furthermore, a global lncRNA–miRNA–mRNA ceRNA regulatory network in DPN model was constructed and miR-212-5p and the significantly correlated lncRNAs with high degree were identified as key mediators in the pathophysiological processes of SCs in DPN. These RNAs would contribute to the diagnosis and treatment of DPN.ConclusionOur study has shown that differentially expressed RNAs have complex interactions among them. They also play critical roles in regulating functions of SCs involved in the pathogenesis of DPN. The novel competitive endogenous RNA network provides new insight for exploring the underlying molecular mechanism of DPN and further investigation may have clinical application value.

BackgroundPseudomonas aeruginosa(P.aeruginosa)employs c-di-GMP to govern biofilm formation and virulence, enhancing bacterial adhesion and immune evasion. In pleural infections, c-di-GMP simultaneously promotes invasion and modulates neutrophil extracellular traps (NETs), though its mechanistic role remains undefined. While c-di-GMP’s pathogenic contributions are established, its specific regulation of NETs formation during pleural infection has not been explored. Our study investigates c-di-GMP-driven NETs generation in murine infection models.MethodC57BL/6J mice were infected with P.aeruginosa strains expressing high (PAO1△wspF), wild-type (PAO1), or low (PAO1/plac-yhjH) c-di-GMP levels to establish pleural infection. In vitro, c-di-GMP levels and biofilm formation of strains were verified using pcdrA-gfp reporter, crystal violet staining, and confocal microscopy. In vivo, pleural histopathology was observed via HE staining, confocal microscopy and scanning electron microscopy (SEM); RNA sequencing (RNA-seq) of mouse pleural lavage fluid cells was performed, followed by Mfuzz clustering, KEGG/GO enrichment analysis, and protein-protein interaction (PPI) network construction to identify key regulatory genes. Candidate genes (e.g., Cybb, Mapk14, Pik3cd) were validated by qRT-PCR. NETs formation was quantified via immunofluorescence, and measurements of cell-free DNA (cf-DNA) and myeloperoxidase-DNA (MPO-DNA) complexes in pleural lavage fluid.ResultIn vitro, PAO1△wspF showed the highest c-di-GMP level and biofilm formation, followed by PAO1 and PAO1/plac-yhjH. In vivo, infected mice exhibited thoracic purulent secretions and parietal pleura thickening (vs. PBS control), with no obvious morphological differences among the three infected groups. RNA-seq and Mfuzz clustering identified 5 continuously downregulated gene clusters in infected mice; KEGG enrichment showed 48 genes enriched in the ‘neutrophil extracellular trap formation’ pathway. PPI network analysis screened 10 hub genes (including Cybb, Mapk14, Pik3cd), whose differential expression was confirmed by qRT-PCR (p<0.05). NETs detection revealed a c-di-GMP-dependent trend: PAO1△wspF induced the strongest NETs response (highest immunofluorescence intensity, cf-DNA, and MPO-DNA levels), followed by PAO1 and PAO1/plac-yhjH.ConclusionsThis work reveals how P.aeruginosa’s c-di-GMP controls NETs formation in murine pleural infections, bridging bacterial virulence mechanisms with host immune responses. By identifying key regulatory genes, it establishes groundwork for targeted anti-biofilm therapies against chronic infections.

Aims/hypothesisThe individual risk of progression of diabetic peripheral neuropathy is difficult to predict for each individual. Mutations in proteins that are responsible for the process of myelination are known to cause neurodegeneration and display alteration in experimental models of diabetic neuropathy. In a prospective observational human pilot study, we investigated myelin-specific circulating mRNA targets, which have been identified in vitro, for their capacity in the diagnosis and prediction of diabetic neuropathy. The most promising candidate was tested against the recently established biomarker of neural damage, neurofilament light chain protein.MethodsSchwann cells were cultured under high-glucose conditions and mRNAs of various myelin-specific genes were screened intra- and extracellularly. Ninety-two participants with type 2 diabetes and 30 control participants were enrolled and evaluated for peripheral neuropathy using neuropathy deficit scores, neuropathy symptom scores and nerve conduction studies as well as quantitative sensory testing at baseline and after 12/24 months of a follow-up period. Magnetic resonance neurography of the sciatic nerve was performed in 37 individuals. Neurofilament light chain protein and four myelin-specific mRNA transcripts derived from in vitro screenings were measured in the serum of all participants. The results were tested for associations with specific neuropathic deficits, fractional anisotropy and the progression of neuropathic deficits at baseline and after 12 and 24 months.ResultsIn neuronal Schwann cells and human nerve sections, myelin protein zero was identified as the strongest candidate for a biomarker study. Circulating mRNA of myelin protein zero was decreased significantly in participants with diabetic neuropathy (p < 0.001), whereas neurofilament light chain protein showed increased levels in participants with diabetic neuropathy (p < 0.05). Both variables were linked to altered electrophysiology, fractional anisotropy and quantitative sensory testing. In a receiver-operating characteristic curve analysis myelin protein zero improved the diagnostic performance significantly in combination with a standard model (diabetes duration, age, BMI, HbA1c) from an AUC of 0.681 to 0.836 for the detection of diabetic peripheral neuropathy. A follow-up study revealed that increased neurofilament light chain was associated with the development of a hyperalgesic phenotype (p < 0.05), whereas decreased myelin protein zero predicted hypoalgesia (p < 0.001) and progressive loss of nerve function 24 months in advance (HR of 6.519).Conclusions/interpretationThis study introduces a dynamic and non-invasive assessment strategy for the underlying pathogenesis of diabetic peripheral neuropathy. The diagnosis of axonal degeneration, associated with hyperalgesia, and demyelination, linked to hypoalgesia, could benefit from the usage of neurofilament light chain protein and circulating mRNA of myelin protein zero as potential biomarkers.Graphical abstract

ObjectivesWe aimed to explore the shared and specific signalling pathways involved in diabetic retinopathy (DR), diabetic peripheral neuropathy (DPN) and diabetic nephropathy (DN).MethodsDifferentially expressed mRNAs and lncRNAs were identified by high-throughput sequencing. Subsequently, functional enrichment analysis, protein-protein interaction (PPI) analysis and lncRNAs-mRNAs networks were conducted to determine the pathogenic mechanisms underlying DR, DPN and DN.ResultsTwenty-six biological pathways were shared among DR, DPN and DN groups compared to the type 2 diabetes mellitus (T2DM) group without complications, and most of the shared pathways and core proteins were involved in immune and inflammatory responses of microvascular damage. Cytokine‒cytokine receptor interactions and chemokine signalling pathway were the most significant and specific pathways for DR, and the lncRNA‒mRNA regulatory networks affected DR by targeting these pathways. Sphingolipid metabolism and neuroactive ligand-receptor pathways were found to be specific for the pathogenesis of DPN. Moreover, multiple amino acid metabolic pathways were involved in the occurrence and progression of DN.ConclusionsDiabetic retinopathy, DPN and DN exhibited commonality and heterogeneity simultaneously. The shared pathologic mechanisms underlying these diabetic complications are involved in diabetic microvascular damage via immune and inflammatory pathways. Our findings predict several biomarkers and therapeutic targets for these diabetic complications.