Integrated Bioinformatics-Based Identification of Potential Diagnostic Biomarkers Associated with Diabetic Foot Ulcer Development.
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ABSTRACT:
SUBMITTER: Qian L
PROVIDER: S-EPMC8426639 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Diabetic foot ulcer (DFU) is a common and serious complication of diabetes mellitus, which influences patients' quality of life. Recently, circRNA regulated the mRNA levels by functioning as miRNA sponge in various disease, including diabetes mellitus. Nevertheless, the circRNA-miRNA-mRNA regulatory network involved in DFU remains obscure. The aim of this study is to construct a competing endogenous RNA (ceRNA) network and screen biological indicators as diagnostic factors in DFU. All the di?erentially expressed circRNAs, miRNAs and mRNAs were derived from Gene Expression Omnibus database. Furthermore, circRNAs identified by cytoHubba analysis and miRNAs obtained by human miRNA-disease database were used to construct DFU-specific ceRNA network with intersection of mRNAs. Functional enrichment analysis displayed the function and pathway of dysregulated mRNAs. Hub genes with high diagnostic value were screened by ClusterONE, GO semantic similarity and receiver operating characteristic (ROC) curve. Here, the ceRNA network consisted of 8 circRNAs, 11 miRNAs and 91 mRNAs. Functional enrichment analysis demonstrated diabetic complications-related pathway including TGF-beta, FoxO and Wnt signaling pathway. GO semantic similarity and ROC curve analysis showed 6 hub genes with high diagnostic value (the area under the ROC curve ? 0.8) in patients with DFU, including BCL2, CCND1, IRAK4, SMAD4, SP1 and SUFU, which were identified as potential target genes for DFU diagnosis. In conclusion, the present study looked at a circRNA-miRNA-mRNA regulatory network with DFU and screened the potential function of mRNA, then identified novel diagnostic biomarkers and therapeutic targets for patients with DFU.
| S-EPMC7452191 | biostudies-literature
Project description:We aimed to explore the mechanism of circular RNAs (circRNAs) and provide potential biomarkers for molecular therapy of diabetic foot ulcers (DFU). Gene expression profile of GSE114248, including five normal samples and five DFU samples, was downloaded from GEO database. Differentially expressed circRNAs (DEcircRNAs) between two groups were identified. Then, DEcircRNA-miRNA and miRNA-mRNA interaction was revealed, followed by the circRNA-miRNA-mRNA network construction. Moreover, functional and pathway analysis were performed based on mRNAs, followed by the DM-related pathway exploration. Specific binding sites for key circRNAs and associated miRNAs were under investigation. Finally, RT-qPCR was used to verify the candidate the relative expression level of circRNA between normal tissues and DFU. Totally, 65 DEcircRNAs were revealed between two groups, followed by 113 circRNA-miRNA-mRNA interactions explored. The mRNAs in these interactions were mainly assembled in functions like cell proliferation and pathways. Moreover, a total of 11 DM-related pathways were revealed. Finally, circRNA-miRNA specific binding-site analysis revealed two key circRNAs, for example, circRNA_072697 and circRNA_405463, corresponding to their miRNAs. These two circRNAs were novel biomarkers for DFU. circRNA_072697 acted as a sponge of miR-3150a-3p in the progression of DFU via regulating KRAS. MAPK signaling pathway might contribute to the development of DFU.
| S-EPMC8244070 | biostudies-literature
Project description: Not available
| S-EPMC10659692 | biostudies-literature
Project description:ObjectiveTo explore the hub genes associated with the pathogenesis and healing of diabetic foot ulcer (DFU) and their biological functions through bioinformatics analysis of transcriptome sequencing data.MethodsThe transcriptome sequencing datasets of DFU were selected from Gene Expression Omnibus (GEO) database, and the data were regrouped and normalized for bioinformatics analysis. The skin transcriptome sequencing datasets of DFU patients were compared with those of normal controls and the transcriptome sequencing datasets of skin from ulcerous wound edge of DFU patients were compared with those of non-ulcerous skin of DFU patients so that differentially expressed genes were identified, pathway enrichment and protein-to-protein interaction (PPI) analyses were performed, hub genes were found through nodal analysis, and receiver operating characteristic (ROC) curve was applied to a testing dataset to validate the diagnostic efficiency of the hub genes related to DFU. The intersecting genes from the two sets of analyses were again subjected to pathway enrichment and PPI analyses to screen for hub genes associated with DFU wound healing. What's more, gene set enrichment analysis (GSEA) was carried out on relevant samples to probe for the possible functions and pathway of non-significant genes in DFU.ResultsA total of 620 up-regulated differentially expressed genes and 196 down-regulated differentially expressed genes were identified in the training dataset which compared DFU patients with non-diabetic patients. The functions of these genes were enriched in the metabolism of terpenoids and polyketides, signaling molecules and interaction, phospholipase D signaling pathway, propanoate metabolism, PI3K-Akt signaling pathway, Toll-like receptor signaling pathway, pyrimidine metabolism, IL-17 signaling pathway, Rap1 signaling pathway, etc. A total of 10 hub genes were identified with the PPI network. Among them, BGN's value of the area under the curve of ROC analysis was 0.714 and CCND1's was 0.712. In the sequencing analysis of ulcerous wound edge of DFU patients and non-ulcerous skin of DFU patients, 4072 up-regulated genes and 911 down-regulated genes were identified, of which, 372 genes were also detected in the differentially expressed genes of DFU. The functions of these differentially expressed genes were enriched in phospholipase D signaling pathway, xenobiotics biodegradation and energy metabolism, glutathione metabolism, pyrimidine metabolism, ErbB signaling pathway, melanin production, etc. A total of 7 hub genes were identified from PPI network. In GSEA analysis, pathways including pentose and glucuronate interconversions and homologous recombination, nicotinate and nicotinamide metabolism, neuroactive ligand receptor interaction, maturity-onset diabetes of the young, butanoate metabolism, lysine degradation, pantothenate and coenzyme A biosynthesis, riboflavin metabolism, steroid hormone biosynthesis, and valine, leucine and isoleucine degradation showed significant expression differences between DFU patients and normal controls.ConclusionBioinformatics analysis results suggest that BGN and CCND1 are potential biomarkers for predicting DFU; CXCL12, TLR4, JAK2, PPARA, UBC, DCN, KDR, and ARNTL are the hub genes of DFU, while CXCL8, CXCL12, TXN, SLIT3, KRT14, KIT, and NEO1 are the hub genes related to wound healing of DFU.
| S-EPMC10408990 | biostudies-literature
Project description:Foot ulceration remains a serious health concern for diabetic patients and has a major impact on the cost of diabetes treatment. Early detection and preventive care, such as offloading or improved hygiene, can greatly reduce the risk of further complications. We aim to assess the use of hyperspectral tissue oximetry in predicting the risk of diabetic foot ulcer formation. Tissue oximetry measurements are performed during several visits with hyperspectral imaging of the feet in type 1 and 2 diabetes mellitus subjects that are at risk for foot ulceration. The data are retrospectively analyzed at 21 sites that ulcerated during the course of our study and an ulceration prediction index is developed. Then, an image processing algorithm based on this index is implemented. This algorithm is able to predict tissue at risk of ulceration with a sensitivity and specificity of 95 and 80%, respectively, for images taken, on average, 58 days before tissue damage is apparent to the naked eye. Receiver operating characteristic analysis is also performed to give a range of sensitivity/specificity values resulting in a Q-value of 89%.
| S-EPMC3048880 | biostudies-other
Project description:PurposeWe aimed to investigate the prevalence of diabetic retinopathy (DR) in patients with diabetic foot ulcer (DFU) and elucidate the association between DR and DFU severities and their shared risk factors.MethodsA retrospective review was conducted on DFU patients who underwent ophthalmic and vascular examinations within 6 months; 100 type 2 diabetic patients with DFU were included. The medical records of 2496 type 2 diabetic patients without DFU served as control data. DR prevalence and severity were assessed in DFU patients. DFU patients were compared with the control group regarding each clinical variable. Additionally, DFU patients were divided into two groups according to DR severity and compared.ResultsOut of 100 DFU patients, 90 patients (90%) had DR and 55 (55%) had proliferative DR (PDR). There was no significant association between DR and DFU severities (R = 0.034, p = 0.734). A multivariable analysis comparing type 2 diabetic patients with and without DFUs showed that the presence of DR [OR, 226.12; 95% confidence interval (CI), 58.07-880.49; p < 0.001] and proliferative DR [OR, 306.27; 95% CI, 64.35-1457.80; p < 0.001), higher HbA1c (%, OR, 1.97, 95% CI, 1.46-2.67; p < 0.001), higher serum creatinine (mg/dL, OR, 1.62, 95% CI, 1.06-2.50; p = 0.027), older age (years, OR, 1.12; 95% CI, 1.06-1.17; p < 0.001), higher pulse pressure (mmHg, OR, 1.03; 95% CI, 1.00-1.06; p = 0.025), lower cholesterol (mg/dL, OR, 0.94; 95% CI, 0.92-0.97; p < 0.001), lower BMI (kg/m2, OR, 0.87, 95% CI, 0.75-1.00; p = 0.044) and lower hematocrit (%, OR, 0.80, 95% CI, 0.74-0.87; p < 0.001) were associated with DFUs. In a subgroup analysis of DFU patients, the PDR group had a longer duration of diabetes mellitus, higher serum BUN, and higher serum creatinine than the non-PDR group. In the multivariable analysis, only higher serum creatinine was associated with PDR in DFU patients (OR, 1.37; 95% CI, 1.05-1.78; p = 0.021).ConclusionsDiabetic retinopathy is prevalent in patients with DFU and about half of DFU patients had PDR. No significant association was found in terms of the severity of these two diabetic complications. To prevent blindness, patients with DFU, and especially those with high serum creatinine, should undergo retinal examinations for timely PDR diagnosis and management.
| S-EPMC5384753 | biostudies-literature
Project description:IntroductionAccurate identification of pathogens, rather than colonising bacteria, is a prerequisite for targeted antibiotic therapy to ensure optimal patient outcome in wounds, such as diabetic foot ulcers. Wound swabs are the easiest and most commonly used sampling technique but most published guidelines recommend instead removal of a tissue sample from the wound bed, which is a more complex process. The aim of this study was to assess the concordance between culture results from wound swabs and tissue samples in patients with suspected diabetic foot infection.Methods and analysisPatients with a diabetic foot ulcer that is thought to be infected are being recruited from 25 sites across England in a cross-sectional study. The coprimary endpoints for the study are agreement between the two sampling techniques for three microbiological parameters: reported presence of likely isolates identified by the UK Health Protection Agency; resistance of isolates to usual antibiotic agents; and, the number of isolates reported per specimen. Secondary endpoints include appropriateness of the empiric antibiotic therapy prescribed and adverse events. Enrolling 400 patients will provide 80% power to detect a difference of 3% in the reported presence of an organism, assuming organism prevalence of 10%, discordance of 5% and a two-sided test at the 5% level of significance. Assumed overall prevalence is based on relatively uncommon organisms such as Pseudomonas. We will define acceptable agreement as ?>0.6.Ethics and disseminationConcordance in diabetic foot ulcer infection (CODIFI) will produce robust data to evaluate the two most commonly used sampling techniques employed for patients with a diabetic foot infection. This will help determine whether or not it is important that clinicians take tissue samples rather than swabs in infected ulcers. This study has been approved by the Sheffield NRES Committee (Ref: 11/YH/0078) and all sites have obtained local approvals prior starting recruitment.Study registrationNRES Ref: 11/YH/0078, UKCRN ID: 10440, ISRCTN: 52608451.
| S-EPMC3549255 | biostudies-literature
Project description:Diabetic foot ulcer (DFU) complications involve autophagy dysregulation. This study aimed to identify autophagy-related bioindicators in DFU. Differentially expressed genes (DEGs) between DFU and healthy samples were analysed from the Gene Expression Omnibus (GEO) datasets, GSE7014 and GSE29221. The roles of autophagy-related DEGs were investigated using protein-protein interaction (PPI) networks, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Gene Ontology (GO) enrichment, and Gene Set Enrichment Analysis (GSEA). Immune cell infiltration's correlation with these DEGs was also assessed. From the Human Autophagy Database (HADB), 232 autophagy-related genes (ARGs) were identified, with an intersection of 17 key DEGs between GSE7014 and GSE29221. These genes are involved in pathways like autophagy-animal, NOD-like receptor signalling, and apoptosis. In the protein network, epidermal growth factor receptor (EGFR) and phosphatase and tensin homologue (PTEN) showed significant interactions with ARGs. Survival analysis indicated the prognostic importance of calpain 2 (CAPN2), integrin subunit beta 1 (ITGB1), and vesicle-associated membrane protein 3 (VAMP3). Lower immune scores were observed in the type 2 diabetes mellitus (DM2) group than in controls. Autophagy and ARGs significantly influence DFU pathophysiology.
| S-EPMC10898398 | biostudies-literature
Project description:Background: Globally, the most common form of arrhythmias is atrial fibrillation (AF), which causes severe morbidity, mortality, and socioeconomic burden. The application of machine learning algorithms in combination with weighted gene co-expression network analysis (WGCNA) can be used to screen genes, therefore, we aimed to screen for potential biomarkers associated with AF development using this integrated bioinformatics approach. Methods: On the basis of the AF endocardium gene expression profiles GSE79768 and GSE115574 from the Gene Expression Omnibus database, differentially expressed genes (DEGs) between AF and sinus rhythm samples were identified. DEGs enrichment analysis and transcription factor screening were then performed. Hub genes for AF were screened using WGCNA and machine learning algorithms, and the diagnostic accuracy was assessed by the receiver operating characteristic (ROC) curves. GSE41177 was used as the validation set for verification. Subsequently, we identified the specific signaling pathways in which the key biomarkers were involved, using gene set enrichment analysis and reverse prediction of mRNA-miRNA interaction pairs. Finally, we explored the associations between the hub genes and immune microenvironment and immune regulation. Results: Fifty-seven DEGs were identified, and the two hub genes, hypoxia inducible factor 1 subunit alpha inhibitor (HIF1AN) and mitochondrial inner membrane protein MPV17 (MPV17), were screened using WGCNA combined with machine learning algorithms. The areas under the receiver operating characteristic curves for MPV17 and HIF1AN validated that two genes predicted AF development, and the differential expression of the hub genes was verified in the external validation dataset. Enrichment analysis showed that MPV17 and HIF1AN affect mitochondrial dysfunction, oxidative stress, gap junctions, and other signaling pathway functions. Immune cell infiltration and immunomodulatory correlation analyses showed that MPV17 and HIF1AN are strongly correlated with the content of immune cells and significantly correlated with HLA expression. Conclusion: The identification of hub genes associated with AF using WGCNA combined with machine learning algorithms and their correlation with immune cells and immune gene expression can elucidate the molecular mechanisms underlying AF occurrence. This may further identify more accurate and effective biomarkers and therapeutic targets for the diagnosis and treatment of AF.
| S-EPMC10808641 | biostudies-literature
Project description:We investigate the changes between diabetic and non-diabetic wound healing process in chronic foot ulcer.
2023-02-01 | GSE223964 | GEO