Identification of core genes and pathways in vacuum sealing drainage for the treatment of diabetic ulcers via bioinformatics and histological DOI Creative Commons

Yongpan Lu,

Guoqi Cao,

Dejie Zhao

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 25, 2023

Abstract Diabetic ulcers are one of the common complications diabetes mellitus and foot is main site diabetic ulcers, which involves small medium-sized arteries, peripheral nerves, microcirculation, etc., with a high rate disability treatment costs. Multidisciplinary treatments spanning medicine material science have been applied for foot, but molecular mechanisms unclear. Bioinformatics was used to evaluate differentially expressed genes when vacuum sealing drainage (VSD) technique histological studies were performed on tissues from six clinical patients before after VSD. Interleukin-6 (IL6) prostaglandin endoperoxide synthase 2 (PTGS2) decreased Epidermal Growth Factor Receptor (EGFR) increased in VSD treatment. Notably, PTGS2 likely facilitates healing by controlling ferroptosis may be both significant prognostic marker potential therapeutic target.

Language: Английский

Deciphering Ferroptosis: From Molecular Pathways to Machine Learning-Guided Therapeutic Innovation DOI
Megha Mete, A. Ojha, Priyanka Dhar

et al.

Molecular Biotechnology, Journal Year: 2024, Volume and Issue: unknown

Published: April 13, 2024

Language: Английский

Citations

2
Integrated bioinformatics, network pharmacology, molecular docking, and molecular dynamics simulation to explore the potential pharmacological mechanism of Erigeron breviscapus (Vant.) Hand-Mazz regulating ferroptosis for the treatment of Alzheimer's disease DOI Creative Commons
Bin Xu,

Guang Sun,

Yundong Zhang

et al.

Journal of Molecular Structure, Journal Year: 2024, Volume and Issue: 1314, P. 138698 - 138698

Published: May 21, 2024

Ferroptosis plays a role in Alzheimer's disease (AD) development. Erigeron breviscapus (Vant.) Hand-Mazz (EBHM) shows promising effects treating cognitive impairment-related diseases. However, the mechanisms by which EBHM regulates ferroptosis AD treatment are not fully understood. This study used bioinformatics, network pharmacology, molecular docking, and dynamics simulation to explore how treatment. The results identified four key genes—HSPA8, GSK3B, CTSB, YWHAG—that involved this regulation, constructed multigene diagnostic model for AD. demonstrated moderate accuracy (area under curve [AUC] = 0.636) distinguishing from non-demented (ND) was further validated with external datasets showing good capabilities (AUC values of 0.898, 0.889, 0.746, 0.712). Additionally, CIBERSORT analysis revealed significant correlation between immune cell infiltration these genes, highlighting their potential immunity. Molecular docking studies indicated that 3,4,5-tricaffeoylquinic acid (TCQA) had highest binding affinity HSPA8, suggesting TCQA HSPA8 components core targets EBHM's regulation therapy. simulations confirmed stability strong TCQA-HSPA8 complex. These findings enhance our understanding underlying may offer new avenues developing effective treatments.

Language: Английский

Citations

2
Identification of CGNL1 as a diagnostic marker in fibroblasts of diabetic foot ulcers: Insights from single cell RNA sequencing and bulk sequencing data DOI Creative Commons
Li Wang, Lulu Tang,

Lingna Zhou

et al.

International Journal of Immunopathology and Pharmacology, Journal Year: 2024, Volume and Issue: 38

Published: Jan. 1, 2024

Objectives This study aimed to explore the unique transcriptional feature of fibroblasts subtypes and role ferroptosis in diabetic foot ulcers (DFUs). Methods The GEO (Gene Expression Omnibus) was searched obtain DFUs single-cell datasets. After identifying cell types by classic marker genes, integrated dataset used run trajectory inference, RNA velocity, ligand-receptor interaction analysis. Next, bulk RNA-seq datasets were analyzed key genes. Results Here, we profile 83529 single transcriptomes from samples utilizing sequencing (scRNA-seq) data DFU database identified 12 types, with exhibiting elevated levels activity substantial cellular heterogeneity. Our results defined six main fibroblast subsets that showed mesenchymal, secretory-reticular, secretory-papillary, pro-inflammatory, myogenesis, healing-enriched functional annotations. Trajectory inference cell-cell communication analysis revealed two major fates subpopulations altered interactions. Bulk CGNL1 as a distinctive diagnostic signature fibroblasts. Notably, positively correlated pro-inflammatory Conclusions Overall, our delineated heterogeneity present populations DFUs, showing distinct characterized their own features enrichment functions. will help us better understand pathogenesis identifies potential target for therapies.

Language: Английский

Citations

2
SDC4 protein action and related key genes in nonhealing diabetic foot ulcers based on bioinformatics analysis and machine learning DOI Creative Commons

Yun-Gang Hu,

Yiwen Wang, Zhi Lin

et al.

International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: 283, P. 137789 - 137789

Published: Nov. 17, 2024

Diabetic foot ulcers (DFU) is a complication associated with diabetes characterised by high morbidity, disability, and mortality, involving chronic inflammation infiltration of multiple immune cells. We aimed to identify the critical genes in nonhealing DFU using single-cell RNA sequencing, transcriptomic analysis machine learning. The GSE165816, GSE134431, GSE143735 datasets were downloaded from GEO database. processed screened datasets, identified cell subsets. Each subtype was annotated, predominant types contributing disease analysed. Key LASSO regression algorithm, followed verification model accuracy stability. investigated molecular mechanisms changes signalling pathways this immunoinfiltration analysis, GSEA, GSVA. Through scRNA-seq we 12 distinct clusters determined that basalKera type important development. A stability prediction constructed incorporating five key (TXN, PHLDA2, RPLP1, MT1G, SDC4). Among these genes, SDC4 has strongest correlation plays an role development DFU. Our study significantly development, potentially serving as new prevention treatment strategies for

Language: Английский

Citations

2
PROS1 is a crucial gene in the macrophage efferocytosis of diabetic foot ulcers: a concerted analytical approach through the prisms of computer analysis DOI Creative Commons
Hongshuo Shi, Zhicheng Zhang, Xin Yuan

et al.

Aging, Journal Year: 2024, Volume and Issue: unknown

Published: April 10, 2024

Background: Diabetic foot ulcers (DFUs) pose a serious long-term threat because of elevated mortality and disability risks. Research on its biomarkers is still, however, very limited. In this paper, we have effectively identified linked with macrophage excretion in diabetic through the application bioinformatics machine learning methodologies. These findings were subsequently validated using external datasets animal experiments. Such discoveries are anticipated to offer novel insights approaches for early diagnosis treatment DFU. Methods: work, used Gene Expression Omnibus (GEO) database's GSE68183 GSE80178 as training dataset build gene model methods. After that, validation sets validate (GSE134431). On genes, performed enrichment analysis both set variant (GSVA) (GSEA). Additionally, genes subjected immunological association immune function analyses. Results: study, PROS1 was potential key target associated efflux DFU by approaches. Subsequently, biomarker status also confirmed datasets. addition, plays role exudation This may be M1, CD4 memory T cells, naïve B M2, affects IL-17, Rap1, hedgehog, JAK-STAT signaling pathways. Conclusions: finding has provide clearance

Language: Английский

Citations

1
Development of novel lysosome-related signatures and their potential target drugs based on bulk RNA-seq and scRNA-seq for diabetic foot ulcers DOI Creative Commons

Longhai Tan,

Junjun Qu,

Junxia Wang

et al.

Human Genomics, Journal Year: 2024, Volume and Issue: 18(1)

Published: June 11, 2024

Abstract Background Diabetic foot ulcers (DFU) is the most serious complication of diabetes mellitus, which has become a global health problem due to its high morbidity and disability rates poor efficacy conventional treatments. Thus, it urgent identify novel molecular targets improve prognosis reduce rate in DFU patients. Results In present study, bulk RNA-seq scRNA-seq associated with were downloaded from GEO database. We identified 1393 DFU-related DEGs by differential analysis WGCNA together, GO/KEGG showed that these genes lysosomal immune/inflammatory responses. Immediately thereafter, we CLU, RABGEF1 ENPEP as DLGs for using three machine learning algorithms (Randomforest, SVM-RFE LASSO) validated their diagnostic performance validation cohort independent this study. Subsequently, constructed artificial neural network model diagnosis based on DLGs, training cohorts was sound. single-cell sequencing, heterogeneous expression also provided favorable evidence them be potential targets. addition, results immune infiltration abundance mainstream cells, including B/T down-regulated DFUs significantly correlated DLGs. Finally, found latamoxef, parthenolide, meclofenoxate, lomustine promising anti-DFU drugs targeting Conclusions can used signatures DFU, them, meclofenoxate drugs. The study provides new perspectives treatment improving

Language: Английский

Citations

1
Key extracellular proteins and TF-miRNA co-regulatory network in diabetic foot ulcer: Bioinformatics and experimental insights DOI Creative Commons
G. Lin, X Liu

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(7), P. e0307205 - e0307205

Published: July 22, 2024

Background Diabetic foot ulcers (DFUs), a serious complication of diabetes, are associated with abnormal extracellular protein (EP) metabolism. The identification key EPs and their regulatory networks is crucial for the understanding DFU formation development effective treatments. In this study, large-scale bioinformatics analysis was conducted to identify potential therapeutic targets experimental validation performed ensure reliability biological relevance findings. Methods Due comprehensive profiling samples provided by GSE80178 dataset, we initially selected it derive differentially expressed genes (DEGs) DFU. Subsequently, utilizing UniProt database annotated EP list from Human Protein Atlas annotation database, screened protein–related (EP-DEGs) due role in pathogenesis healing We examined EP-DEG pathway enrichment protein-protein interaction networks, analyzed paired full-thickness skin tissue 24 patients DFUs healthy controls, polymerase chain reaction (PCR) experiments validate candidate genes. Ultimately, constructed transcription factor (TF)-microRNA (miRNA)–hub gene co-regulatory network explore upstream downstream connections based on validated DEGs. Results Four (FMOD, LUM, VCAN, S100A12) were identified verified via PCR analysis. TF-miRNA-hub contained pivotal TFs TRIM28 STAT3 miRNAs hsa-mir-20a-5p, hsa-miR-21, hsa-miR-203. Conclusion findings study advance our pathology defining roles specific elucidating network. These insights pave way novel approaches improve treatment outcomes.

Language: Английский

Citations

1
Ferroptosis-related gene MAPK3 is associated with the neurological outcome after cardiac arrest DOI Creative Commons

Hong xiang Hou,

Li Pang, Liang Zhao

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(6), P. e0301647 - e0301647

Published: June 17, 2024

Background Neuronal ferroptosis is closely related to the disease of nervous system, and objective present study was recognize verify potential ferroptosis-related genes forecast neurological outcome after cardiac arrest. Methods Cardiac Arrest-related microarray datasets GSE29540 GSE92696 were downloaded from GEO batch normalization expression data performed using “sva” R package. GSE2 9540 analyzed identify DEGs. Venn diagram applied DEGs Subsequently, The Gene Ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG) enrichment analysis performed, PPI network screen hub genes. Receiver operating characteristic (ROC) curves adopted determine predictive value biomarkers, dataset further evaluate diagnostic efficacy biomarkers. We explore transcription factors miRNAs associated with “CIBERSORT” package utilized analyse proportion infiltrating immune cells. Finally, validated by a series experiments at cellular level. Results 112 overlapping obtained via intersecting these GO KEGG demonstrate that are mainly involved in response oxidative stress, ferroptosis, apoptosis, IL-17 signalling pathway, autophagy, toll-like receptor pathway. top 10 selected, including HIF1A, MAPK3, PPARA, IL1B, PTGS2, RELA, TLR4, KEAP1, SREBF1, SIRT6. Only MAPK3 upregulated both GAE92696. AUC values 0.654 0.850 respectively. result indicates hsa-miR-214-3p hsa-miR-483-5p can regulate MAPK3. positively correlated naive B cells, macrophages M0, activated dendritic cells negatively CD4 memory T CD8 Compared OGD4/R24 group, OGD4/R12 group had higher mRNA protein levels more severe ferroptosis. Conclusion In summary, gene could be used as biomarker predict Potential biological pathways provide novel insights into pathogenesis

Language: Английский

Citations

1
Machine learning-driven discovery of novel therapeutic targets in diabetic foot ulcers DOI Creative Commons
Yu Xin, Zhiwei Wu, Nan Zhang

et al.

Molecular Medicine, Journal Year: 2024, Volume and Issue: 30(1)

Published: Nov. 14, 2024

Abstract Background To utilize machine learning for identifying treatment response genes in diabetic foot ulcers (DFU). Methods Transcriptome data from patients with DFU were collected and subjected to comprehensive analysis. Initially, differential expression analysis was conducted identify significant changes levels between healthy controls. Following this, enrichment analyses performed uncover biological pathways processes associated these differentially expressed genes. Machine algorithms, including feature selection classification techniques, then applied the pinpoint key that play crucial roles pathogenesis of DFU. An independent transcriptome dataset used validate identified our study. Further single-cell datasets investigate at level. Results Through this integrated approach, SCUBE1 RNF103-CHMP3 as significantly found be involved immune regulation, playing a role body’s inflammation infection, which are common linked extracellular interactions, suggesting its involvement cellular communication tissue repair mechanisms essential wound healing. The reliability results confirmed dataset. Additionally, examined data, showing downregulated cured patient group, particularly NK cells macrophages. Conclusion identification potential biomarkers marks step forward understanding molecular basis disease. These offer new directions both diagnosis treatment, developing targeted therapies could enhance outcomes. This study underscores value integrating computational methods novel insights into complex diseases like Future research should focus on validating findings larger cohorts exploring therapeutic targeting clinical settings.

Language: Английский

Citations

1
Study of molecular patterns associated with ferroptosis in Parkinson’s disease and its immune signature DOI Creative Commons
Lixia Chen, Guanghao Xin,

Yijie He

et al.

PLoS ONE, Journal Year: 2023, Volume and Issue: 18(12), P. e0295699 - e0295699

Published: Dec. 21, 2023

Parkinson's disease is the second most common neurodegenerative in world. We downloaded data on and Ferroptosis-related genes from GEO FerrDb databases. used WCGAN Random Forest algorithm to screen out five ferroptosis-related hub genes. Two were identified for first time as possibly playing a role Braak staging progression. Unsupervised clustering analysis based yielded ferroptosis isoforms, immune infiltration indicated that these isoforms are associated with cells may represent different patterns. FRHGs scores obtained quantify level of modifications each individual. In addition, differences interleukin expression found between two subtypes. The biological functions involved gene analyzed. ceRNA regulatory network was mapped. classification diagnosis model risk prediction also constructed by applying logistic regression. Multiple external datasets validated diagnostic some accuracy. This study explored their molecular patterns signatures provide new ideas finding targets intervention predictive biomarkers.

Language: Английский

Citations

2