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J Cell Mol Anesth

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https://doi.org/10.5812/jcma-145349

Introducing Pain-Related Molecular Pathways in Painful Diabetic Neuropathy Via Protein Interaction Networks

Author(s):
Nasrin Amiri-DashatanNasrin Amiri-Dashatan1, Reyhaneh Farrokhi-YektaReyhaneh Farrokhi-Yekta2, Mehdi KoushkiMehdi Koushki3, Nayebali AhmadiNayebali AhmadiNayebali Ahmadi ORCID4,*
1Zanjan Metabolic Diseases Research Center, Zanjan University of Medical Sciences, Zanjan, Iran
2Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3Department of Clinical Biochemistry, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
4Proteomics Research Center, Department of Medical Lab Sciences, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Journal of Cellular & Molecular Anesthesia:Vol. 9, issue 1; e145349
Published online:Feb 08, 2024
Article type:Research Article
Received:Aug 05, 2023
Accepted:Feb 08, 2024
How to Cite:Amiri-Dashatan N, Farrokhi-Yekta R, Koushki M, Ahmadi N. Introducing Pain-Related Molecular Pathways in Painful Diabetic Neuropathy Via Protein Interaction Networks. J Cell Mol Anesth. 2024;9(1):e145349. doi: https://doi.org/10.5812/jcma-145349

Abstract

Background:

Painful diabetic neuropathy (PDN) is one of the most drastic complications of diabetes. Patients with PDN always reveal spontaneous and stimulus‑evoked pain. However, the pathogenesis mechanisms of PDN are not entirely distinct.

Objectives:

In the present study protein-protein interaction (PPI) network for PDN was constructed and analyzed to identify key proteins as potential biomarker candidates.

Methods:

The transcriptomic (genes) and proteomic (proteins) data in articles that focused on PDN with differential expressions were collected. Protein networks were constructed and analyzed using STRING and Cytoscape software, respectively. Further PPI network analysis, gene ontology, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis were performed using MCODE and DAVID tools.

Results:

A total of 147 differentially regulated proteins/genes were identified in painful diabetic neuropathy, including 91 up-regulated and 56 down-regulated proteins/genes. A network analysis of genes/proteins related to PDN identified COX4I1, NDUFS8, UQCRC1, COX7C, and some other NADH dehydrogenases, including NDUFB7, NDUFS7, NDUFS3, NDUFB5, NDUFA2, and NDUFB4 as hub-bottleneck proteins. With functional enrichment analysis of network clustering, COX7C, HP, RPS12, KCNIP2, and CoL4A1 were established as distinct seed proteins in the obtained modules, which could lead to the discovery of biomarker candidates.

Conclusions:

These results could provide new insights into pathology and molecular mechanisms, as well as the identification of pathways and proteins/genes involved in causing PDN in diabetic patients. COX7C, HP, RPS12, KCNIP2, and CoL4A1 are the top 5 seed nodes (hub proteins) and can serve as potential biomarker candidates and targets for PDN management. However, further investigations are needed to evaluate these proteins in detail.

Keywords
Pain
Painful Diabetes
Neuropathy
Protein Interaction Network
Hub Protein
Biomarker

1. Background

Diabetes mellitus (DM) is a serious public health problem and a devastating condition; its prevalence has increased over the past few decades. It is defined as a group of chronic metabolic dysregulation characterized by hyperglycemia resulting from the lack of insulin production, resistance to insulin action, or even both (1). According to the International Diabetes Federation, the number of diabetic people is expected to exceed 640 million by 2040 (2). Diabetic neuropathy is one of the complications of diabetes (2). Diabetic neuropathy is one of the complications of diabetes. Peripheral neuropathy is defined as peripheral nervous system disorders. Many etiological factors have been involved in the development of peripheral neuropathy, including cancer, drug toxicity, and vitamin deficiencies. The number of patients with DM is growing worldwide; it is one of the most common leading causes of neuropathy, resulting in high morbidity and mortality (3). Diabetic neuropathy includes various neuropathies, including mononeuropathy, polyneuropathy, plexopathy, and radiculopathy (4).
Diabetic neuropathy can produce both painful and non-painful forms. Painful diabetic neuropathy has been estimated to occur in 25% of patients with DM (5, 6). In this regard, the most common form of neuropathic pain arises from type 2 diabetes mellitus (T2DM) (7). In addition, diabetic neuropathy pain (DNP) has been observed in 19% of insulin-dependent patients and 49% of those with non-insulin-dependent DM (8). As diabetes increases, DNP continues to rise with the global diabetes epidemic. Pain is the most common distressing symptom in diabetic neuropathy and mainly affects the lower limbs, including hands and feet. Also, there is a lack of safe and effective sedative drugs to control this chronic painful status. The risk factors causing painful diabetic neuropathy are not as well defined; however, the patient's age, duration of diabetes, nephropathy, peripheral vascular disease, and waist circumference are reported as possible predictors for painful neuropathy progression (9). Research on possible mechanisms involved in diabetic neuropathic pain is very complex because diabetes is a multifactorial disorder. According to the literature, diabetic peripheral neuropathy is associated with hyperglycemia and hyperlipidemia pathology (10). Additionally, diabetic peripheral neuropathy is related to demyelination and degeneration of axons, resulting in nerve dysfunction (7).
It is demonstrated that the soma of the primary afferent neurons that innervate the feet are reported to be present in the lumbar dorsal root ganglia. A dysregulated peripheral nociceptor is involved in promoting pain hypersensitivity in patients with diabetic peripheral neuropathy (11). The proposed causes of dysfunction of nociceptive neurons in the dorsal root ganglia are still being investigated. Studying and modeling complex biological systems to describe various human diseases has attracted much attention in recent years (12). Major biological processes and disease pathogenesis are mediated through physical interactions of proteins; hence, there is a requirement to discover the protein interaction network that forms these processes that leads to understanding human diseases (13). The applications of protein interaction networks allow the identification of genes and proteins related to diseases. Several omics-based investigations were performed on differentially expressed genes (DEGs) in painful diabetic peripheral neuropathy models to identify DEGs contributing to pathological processes and neuropathic pain (14-16). Additionally, network-based analysis can explain the critical genes associated with different diseases (13, 17, 18). Since the full mechanism of painful diabetic neuropathy is not clear, the protein interaction network analysis could be a promising way to manage this problem.

2. Objectives

In the present study, we collected known genes/or proteins related to painful diabetic neuropathy and constructed a network. The important proteins are highlighted as critically involved proteins in pain among patients with diabetic neuropathy.

3. Methods

3.1. Collection of Expression Data Associated with Painful Diabetic Neuropathy

The transcriptomic (genes) and proteomic (proteins) data associated with PDN (painful diabetic neuropathy) were extracted from Web of Sciences, PubMed, Google Scholar, and ScienceDirect using “Painful Diabetic Neuropathy “AND" Differential Protein OR Genes and “Expression Profiling" keywords. The differentially expressed proteins or genes were collected after a literature review and selection of related papers (14, 15, 19, 20).

3.2. Functional Annotation and Pathway Enrichment Analysis

Gene ontology (GO) categories were analyzed to identify the function of genes related to PDN. The GO analysis includes biological processes, molecular function, cellular components, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis performed using DAVID tools.

3.3. Construction and Analysis of Protein-Protein Interaction Network Related to Painful Diabetic Neuropathy

The Uniporter accession numbers of the collected data were extracted (https://www.uniprot.org). The construction and analysis of the protein-protein interaction (PPI) network were performed using the STRING online web resource and Cytoscape software platform, respectively. The STRING database contains protein interaction data from different sources, including experimental information, computational prediction methods, and public text collections. Cytoscape is an open-source software project for visualization and integrating biomolecular interaction networks with high-throughput expression data (21). The current study analyzed the network characteristics by the Molecular Complex Detection (MCODE) plugin in Cytoscape.

4. Results

4.1. Identification of Differentially Expressed Genes

After the literature review, 4 studies were selected for this analysis. The characteristics of the included studies are shown in Table 1. According to the systematic search, 147 candidate proteins/genes were identified, which include 91 up-regulated and 56 down-regulated proteins/genes (Table 2). For those studies that were performed on rat and mouse models, only the proteins that were common in humans were selected. The threshold of a P-value < 0.05 and FC ≥ 1.5 were considered significantly differentially expressed proteins (Table 2).
Table 1.Characteristics of Included Studies for Expression Data of Painful Diabetic Neuropathy
Study TitleSamplesReferences
Integrative multi-omic analyses of dorsal root ganglia in diabetic neuropathic pain using proteomics, phosphor-proteomics, and metabolomicsDorsal root ganglia (DRG) (L4, L5, and S1) from humanDoty et al. 2022 (15)
Transcriptomic analysis of human sensory neurons in painful diabetic neuropathy reveals inflammation and neuronal loss. L4 and L5 ganglia from humanHall et al. 2022 (19)
Proteomics analysis of the spinal dorsal horn in diabetic painful neuropathy rats with electroacupuncture treatmentSpinal dorsal horn sample from a rat model Yu et al. 2021 (20)
Diabetic neuropathic pain induced by streptozotocin alters the expression profile of non‑coding RNAs in the spinal cord of mice as determined by sequencing analysis.L4‑5 spinal cord tissues from mice modelHe et al. 2021 (14)
Table 2.List of the Identified Differentially Regulated Proteins/Genes in Painful Diabetic Neuropathy a
NUniProt AC. No.Gene SymbolDirection of RegulationNUniProt AC. No.Gene SymbolDirection of Regulation
1P02766TTRUp75P34910EVI2BUp
2P62140PPP1CBUp76P41236PPP1R2Up
3P31213SRD5A2Up77E1BB50CDK12Up
4Q969M1TOMM40LUp78P08670VIMUp
5Q96RN1SLC26A8Up79Q2TB10ZNF800Up
6Q86Y07VRK2Up80P10451SPP1Up
7P35557GCKUp81Q9C0I1MTMR12Up
8P01042KNG1Up82Q13671RIN1Up
9P00738HPUp83P23588EIF4BUp
10Q8IYT4KATNAL2Up84O14672ADAM10Up
11P0DOY2IGLC2Up85P13639EEF2Up
12Q9H7M9VSIRUp86Q7Z4Q2HEATR3Up
13O14556GAPDHSUp87P10915HAPLN1Up
14P02671FGAUp88Q92752TNRUp
15P34982OR1D2Up89P48539PCP4Up
16P0DP58LYNX1Up90Q7Z4Q2HEATR3Up
17Q99623PHB2Up91O43312MTSS1Up
18P02679FGGUp92Q9UK22FBXO2Down
19P02675FGBUp93Q9UBY5LPAR3Down
20P35232PHB1Up94A5PKU2TUSC5Down
21Q6R327RICTORUp95Q5JUK3KCNT1Down
22O94772LY6HUp96Q9BWW7SCRT1Down
23P21589NT5EUp97Q8N398VWA5B2Down
24P09669COX6CUp98Q53GA4PHLDA2Down
25Q9P2U8SLC17A6Up99O43711TLX3Down
26P24311COX7BUp100Q9UHG2PCSK1NDown
27O00217NDUFS8Up101Q9BQ87TBL1YDown
28P01834IGKCUp102Q99453PHOX2BDown
29P13073COX4I1Up103Q9UHR6ZNHIT2Down
30P03915MT-ND5Up104Q96A47ISL2Down
31P15954COX7CUp105Q9H4Q4PRDM12Down
32P17568NDUFB7Up106Q02575NHLH1Down
33O43674NDUFB5Up107Q9NQ03SCRT2Down
34Q9UI09NDUFA12Up108Q9UIU6SIX4Down
35O14548COX7A2LUp109P12277CKBDown
36Q8NA47CCDC63Up110P12532CKMT1ADown
37Q6ZR08DNAH12Up111C9JSQ1CKMT1BDown
38Q16864ATP6V1FUp112Q12809KCNH2Down
39Q96JX3SERAC1Up113O43526KCNQ2Down
40Q6ZTW0TPGS1Up114Q14654KCNJ11Down
41O75489NDUFS3Up115Q92952KCNN1Down
42Q96ID5IGSF21Up116Q9NS61KCNIP2Down
43Q9ULK4MED23Up117Q9Y2W7KCNIP3Down
44Q9UGC6RGS17Up118Q9UHC3ASIC3Down
45P10606COX5BUp119P30542ADORA1Down
46Q92793CREBBPUp120P18825ADRA2CDown
47Q86UD0SAPCD2Up121P41145OPRK1Down
48O43920NDUFS5Up122A6NFN3RBFOX3Down
49O95168NDUFB4Up123O76070SNCGDown
50Q9UKT6FBXL21Up124P20472PVALBDown
51Q96RD9FCRL5Up125Q8N9F0NAT8LDown
52A0A0C4DH67IGKV1-8Up126Q8N7H5PAF1Down
53A0A0B4J1U7IGHV6-1Up127Q96B36AKT1S1Down
54P01599IGKV1-17Up128P04035HMGCRDown
55A0A0C4DH29IGHV1-3Up129Q9C005DPY30Down
56A0A0C4DH69IGKV1-9Up130Q9UMX0UBQLN1Down
57P15018LIFUp131Q0D2I5IFFO1Down
58P01591JCHAINUp132Q15819UBE2V2Down
59P01857IGHG1Up133Q68D86CCDC102BDown
60P06702S100A9Up134P07910HNRNPCDown
61P05109S100A8Up135P36776LONP1Down
62Q9Y5Y7LYVE1Up136P02461COL3A1Down
63Q86VB7CD163Up137Q13591SEMA5ADown
64Q16649NFIL3Up138Q9Y5H1PCDHGA2Down
65Q16666IFI16Up139P55083MFAP4Down
66P41182BCL6Up140Q8N0U8VKORC1L1Down
67Q6W2J9BCORUp141Q8WUX1SLC38A5Down
68Q08881ITKUp142P22692IGFBP4Down
69P41279MAP3K8Up143P02462COL4A1Down
70P24941CDK2Up144Q9BX70BTBD2Down
71O60462NRP2Up145P15880RPS2Down
72P23588EIF4BUp146Q7Z6Z7HUWE1Down
73O14672ADAM10Up147P02649APOEDown
74P13639EEF2Up

a P-value < 0.05 and fold change > 1.5.

4.2. Functional and Pathway Enrichment Analyses

According to Table 3, in the biological processes-associated category, the DRGs significantly enriched mitochondrial respiratory chain Complex I assembly (GO: 0032981, P-value = 2.1E-4), mitochondrial ATP synthesis coupled proton transport (GO: 0042776, P-value = 2.1E-4), aerobic respiration (GO: 0009060, P-value = 2.1E-4), etc. The molecular function annotation results of DEGs included NADH dehydrogenase (ubiquinone) activity (GO: 0008137, P-value = 1.2E-2), antigen binding (GO: 0003823, P-value = 1.2E-2), receptor binding (GO: 0005102, P-value = 5.9e-01), cytochrome-c oxidase activity (GO: 0004129, P-value = 2.8E-2), etc. In addition, the cellular component annotation indicated that the mitochondrial inner membrane (GO: 0005743, P-value = 3.0E-6), mitochondrial respiratory chain Complex I (GO: 0005747, P-value = 3.6E-6), and blood microparticle (GO: 0072562, P-value = 3.6E-6) were major enriched categories in DEGs.
Table 3.Functional Annotation of Differential Genes Associated with Painful Diabetic Neuropathy
TermCountBenjamini-Corrected P-ValueFold Enrichment
GOTERM: Biological process
GO:0032981~mitochondrial respiratory chain complex I assembly82.1E-417.76
GO:0042776~ mitochondrial ATP synthesis coupled proton transport82.1E-417.22
GO:0009060~ aerobic respiration82.1E-415.99
GO:0006123~ mitochondrial electron transport, cytochrome c to oxygen62.1E-433.57
GO:0006120~ mitochondrial electron transport, NADH to ubiquinone72.1E-420.83
GO:0045907~ positive regulation of vasoconstriction52.7E-218.40
GO:0045333~ cellular respiration53.5E-216.65
GO:0071805~ potassium ion transmembrane transport74.6E-27.53
GOTERM: Molecular function
GO:0008137~ NADH dehydrogenase (ubiquinone) activity86.0E-626.19
GO:0003823~ antigen binding81.2E-27.76
GO:0005102~ receptor binding121.2E-24.25
GO:0004129~ cytochrome-c oxidase activity42.8E-228.16
GO:0034987~ immunoglobulin receptor binding63.7E-28.80
GO:0004111~ creatine kinase activity33.8E-270.40
GOTERM: Cellular component
GO:0005743~ mitochondrial inner membrane183.0E-63.4
GO:0005747~ mitochondrial respiratory chain complex I83.6E-62.9
GO:0072562~ blood microparticle113.6E-62.4
GO:0005751~ mitochondrial respiratory chain Complex IV64.2E-51.6
GO:0045202~ synapse149.2E-41.4
GO:0005739~ mitochondrion259.2E-41.0
GO:0031966~ mitochondrial membrane83.2E-31.1
GO:0070062~ extracellular exosome301.1E-21.1
GO:0043025~ neuronal cell body112.1E-20.8
The KEGG pathway analysis showed that oxidative phosphorylation (P-value = 2.7E-9), diabetic cardiomyopathy (P-value = 3.5E-8), and non-alcoholic fatty liver disease (P-value = 9.1E-7) were the mainly enriched pathways associated with DRGs in painful diabetic neuropathy, as presented in Table 4.
Table 4.KEGG Pathway Enrichment Analysis of Differentially Expressed Genes by the DAVID Tool a
KEGG PathwayCountBenjamini-Corrected P-ValueFold Enrichment
Oxidative phosphorylation152.7E-911.71
Diabetic cardiomyopathy163.5E-88.24
Non-alcoholic fatty liver disease139.1E-78.77
Amyotrophic lateral sclerosis159.7E-76.76
Thermogenesis163.3E-65.47
Huntington disease143.3E-66.57
Chemical carcinogenesis - reactive oxygen species174.3E-64.88
Parkinson's disease102.2E-55.36
Prion disease123.4E-54.36
Alzheimer's disease101.1E-53.51
Pathways of neurodegeneration - multiple diseases174.1E-46.36
Metabolic pathways256.8E-21.69

Abbreviation: KEEG, Kyoto Encyclopedia of Genes and Genomes.

a The count threshold was ≥ 10.

4.3. Identification of Hub Genes via PPI Network Analysis

After removing disconnected nodes, the STRING database yielded a PPI network with 117 nodes. The network was then analyzed in Cytoscape software, shown in Figure 1. Hub proteins were selected according to CytoHubba, and 6 distinct clusters were extracted from the network using the MCODE plugin (Figure 2A - F). The results showed that the most important hub proteins in the network included COX4I1, NDUFS8, UQCRC1, COX7C, and some other NADH dehydrogenases, including NDUFB7, NDUFS7, NDUFS3, NDUFB5, NDUFA2, and NDUFB4. The results of the functional enrichment analysis of the clusters showed that the most important pathways that the clusters (clusters 1 - 5) were involved in oxidative phosphorylation, cell cycle, complement, and coagulation cascades, ribosome, and GnRH secretion (Table 5), while no significant KEGG pathways detected for Cluster 6.
The protein-protein interaction network of painful diabetic neuropathy. Larger circles show nodes with higher degrees.
Figure 1.

The protein-protein interaction network of painful diabetic neuropathy. Larger circles show nodes with higher degrees.

Network clusters extracted from MCODE. Seed nodes in each cluster are colored yellow (A-F). G: the top 10 hub nodes of the network
Figure 2.

Network clusters extracted from MCODE. Seed nodes in each cluster are colored yellow (A-F). G: the top 10 hub nodes of the network

Table 5.Results of the Pathway Enrichment Analysis of the Network Clusters
KEGG PathwayCountBenjamini-Corrected P-Value
Oxidative phosphorylation (cluster 1)216.5E-36
Cell cycle (cluster 2)113.3E-17
Complement and coagulation cascades (cluster 3)41.7E-4
Ribosome (cluster 4)53.2E-5
GnRH secretion (cluster 5)24.2E-2

Abbreviation: KEEG, Kyoto Encyclopedia of Genes and Genomes.

5. Discussion

Since the proteins interact with each other in the cellular pathways, many disorders result from the deregulation of proteins. The protein interaction network-based analysis is beneficial for systematically studying complex and multifactorial diseases such as cancer and DM (22). Protein-protein interactions contribute to all vital biological activities in living organisms. Identifying protein interactions in the cells is essential to reveal the function and cellular and molecular mechanisms in cells. Commonly, PPI can provide a valuable overview for a great comprehension of the functional organization of the proteome. This modern approach is now used as an efficient method to identify potential drug, therapeutic, diagnostic, and prognostic targets in various diseases (17, 23). An important advantage of network analysis is the identification of hub nodes in the protein interaction network. In the present study, the protein interaction network associated with painful diabetic neuropathy was constructed and evaluated. We extracted 147 proteins and genes with differential expression from literature and predicted the main proteins as potential biomarkers related to peripheral PDN. The top 10 nodes (hub proteins), which mostly interact with the other nodes, are represented in the result section, include COX4I1, NDUFS8, UQCRC1, COX7C, NDUFB7, NDUFS7, NDUFS3, NDUFB5, NDUFA2, and NDUFB4. These proteins were identified as the essential proteins that play critical roles in pathophysiology and cellular pathways related to pain in diabetic neuropathy.
In this study, COX4I1 and COX7C were identified as hub proteins with the highest degree. Cytochrome c oxidase (COX) is an indispensable part of mitochondrial machinery needed for ATP production in mammalian cells. In addition to 3 mitochondria-encoded subunits necessary for COX catalytic function, 11 nuclear-encoded subunits build up the COX enzyme and regulate COX enzyme activity. Cytochrome c oxidase is regulated via tissue-, development- or environment-controlled expression of subunit isoforms. The COX4 subunit is thought to optimize respiratory chain function based on the oxygen-controlled expression of its isoforms COX4I1 and COX4I2 (24). Studies show low COX4I1 links mitochondrial dysfunction to obesity and T2DM in humans and mice (25). Dysregulation of the COX complex is related to mitochondrial oxidative stress (26). In addition, the oxidative stress condition in mitochondria is associated with obesity, metabolic syndrome, and T2DM (27). COX4I1 is suggested to be the most important regulatory subunit of COX (28). Van der Schueren et al. (25) conducted a study to investigate the association of mitochondrial oxidative stress with obesity, metabolic syndrome, and T2DM and evaluate COX4I1 in peripheral blood monocytes as well as a potential biomarker for harmful metabolic development in obesity patients. They reported that COX4I1 depression is associated with insulin resistance and T2DM in obesity. Moreover, it is perhaps a helpful diagnostic biomarker in peripheral blood monocytes (25). Another study reported that low cytochrome oxidase1 links mitochondrial dysfunction to atherosclerosis (29). Recently, a study analyzed the proteomics of the spinal dorsal horn in diabetic painful neuropathy rats, and their results indicated that COX (COX, Complex IV) factors, including COX4I1, COX5B, COX6C2, COX7B, and COX7C, were significantly up-regulated in spinal dorsal horn during PDN (20). Besides, the COX7C is not only a hub but also recognized as a seed node, which shows its importance in the pathogenesis of neuropathy as well as a potential drug target.
In our study, NDUFS8 is detected as another hub protein. The NDUFS8 protein is a subunit of NADH dehydrogenase (ubiquinone), also called Complex I, that is located in the inner membrane of mitochondria. Mutations in NDUFS8 have been associated with clinical features, including ptosis, external ophthalmoplegia, proximal myopathy, cardiomyopathy, pigmentary retinopathy, encephalopathy, and neurodegenerative disorders. Type 1 diabetes mellitus (T1DM) is an endocrine disorder characterized by destroying pancreatic β cells. This is attributed to the development of chronic diabetic complications: neurovascular and macrovascular. The development of complications is associated with various risk factors, mainly insulin resistance (30, 31) and hyperglycemia. Flotynska et al. conducted a study to evaluate NDUFS8 serum concentration as a Complex I marker and the relationship with insulin resistance in T1DM. It has been found that a higher serum concentration of NDUFS8 protein is associated with higher insulin sensitivity among adult patients with T1DM (32). In addition, the NDUFS8 gene was expressed at a high level in the skeletal muscle tissue of T2DM patients, which might indicate that increased expression of NDUFS8 can affect the glucose metabolism in the skeletal muscle tissue, causing insulin resistance and then diabetes development. Furthermore, based on bioinformatics analysis, NDUFS8 is a potential therapeutic target (33).
Another hub protein in our analysis is UQCRC1, characterized as a subunit of Complex III in the mitochondrial respiratory chain. The functional effect of UQCRC1 mutations was investigated in several study models to assess their potential pathogenicity in the disease process. In this regard, it is demonstrated that the mitochondrial UQCRC1 mutations cause autosomal dominant Parkinsonism with polyneuropathy (34). Although the important role of mitochondria in the development of diabetes and its complications, especially neuropathy, is evident, there have been fewer studies on the physiology of this organelle in diabetic neuropathy than in other complications such as cardiomyopathy. According to this, in one study of alterations in mitochondrial physiology, the mRNA level of UQCRC1 decreased in the Diabetic Akita mouse model (35).
Interestingly, some other NADH dehydrogenases, including NDUFB7, NDUFS7, NDUFS3, NDUFB5, NDUFA2, and NDUFB4, are detected as hub proteins in our network analysis. Diabetic neuropathy is a main complication of DM that causes significant morbidity among patients with diabetes. Meanwhile, mitochondrial dysfunction and oxidative stress have been suggested as important mediators of neurodegeneration in diabetes (36). It is suggested that high glucose in tissues triggers extreme electron donation to the electron transport chain and an elevated supply of NADH in the mitochondria, leading to increased reactive oxygen species and degeneration of target tissues (37). Considering the importance of mitochondria and their cellular processes in diabetes and its complications, we found that oxidative phosphorylation is a significant pathway involved in painful diabetic neuropathy through KEGG pathway analysis. In agreement with the results obtained from our study, several studies also demonstrated that mitochondrial dysfunction occurred in neuropathy (38, 39). In the current study, 6 clusters and 5 seed nodes were also determined through protein network analysis, including COX7C, HP, RPS12, KCNIP2, and CoL4A1, which can serve as candidate biomarkers for painful diabetic neuropathy. However, further investigations are needed to evaluate these proteins in detail.

5.1. Conclusions

Our study intended to detect the main proteins and genes involved in painful diabetic neuropathy progression and identify potential biomarkers using comprehensive bioinformatics analyses. Collectively, 147 differentially regulated (91 up- and 56 down-regulated) proteins/genes were identified in painful diabetic neuropathy. Our research has provided new points into PDN pathogenesis by analyzing the DEG proteins/genes and their interactions with each other and presenting their hub proteins, pathways, and functional annotation. These proteins, including COX7C, HP, RPS12, KCNIP2, and CoL4A1, can be candidate biomarkers and targets for PDN management and potential treatment.

Acknowledgments

Footnotes

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