Identification of Transcription Factors related to Diabetic Tubulointerstitial Injury

Liu, Jing; Duan, Guangjie; Yang, Wenxia; Zhang, S; F, Liu; Peng, Yong; Sun, Liya; Y, Liu; Xiao, Liang

doi:10.21203/rs.2.12236/v1

Cited by 3 publications

(4 citation statements)

References 32 publications

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“…Enrichment analysis indicated that these genes are signi cantly overrepresented in pathways such as PI3K-Akt signaling, ECM-receptor interaction, AGE-RAGE signaling in diabetic complications, the Renin-angiotensin system, and the Complement and coagulation cascades. These pathways are intimately related to the pathological alterations observed in DN, corroborating ndings from preceding research [38,39].…”

Section: Discussionsupporting

confidence: 91%

Identification of diagnostic biomarkers for diabetes nephropathy by multi-chip integrated bioinformatics combining machine-learning strategies and mendelian randomization

Su,

Guo,

et al. 2024

Preprint

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Background Diabetic nephropathy (DN) represents a significant complication of diabetes, progressively contributing to the global incidence of end-stage renal disease (ESRD). Currently, the diagnosis of DN primarily relies on traditional clinical indicators, which may not adequately reflect the progression and prognosis of all DN patients, posing challenges in developing diagnostic and therapeutic strategies. This study aims to identify new molecular and genetic biomarkers closely associated with the onset and progression of DN through bioinformatics and genetic epidemiology analysis methods. Methods Gene expression profiles were obtained from gene expression omnibus (GEO) database. The R software was employed to filter differentially expressed genes (DEGs) and to conduct enrichment analyses of these genes. It is remarkable that five distinct machine learning classifiers were utilized to identify diagnostic biomarkers and to formulate a diagnostic model. These biomarkers were further validated in an external validation dataset generated by Nephroseq V5, whereupon a clinical characteristic correlation analysis ensued. In light of the machine learning results, immune infiltration analysis and single-cell RNA sequencing were undertaken. Ultimately, the mendelian randomization method was used to examine the causal relationship between the identified biomarkers and DN. Results Five biomarkers implicated in DN, including AFM, DUSP1, KRT19, TGFBI, and ZFP36, were subjected to external testing and validation, utilizing various machine learning models with high diagnostic efficacy. Correlation analysis demonstrated that the expression of these biomarkers correlated with the deterioration of kidney function, and single-cell RNA sequencing results indicated that the biomarkers were predominantly localized in granulocytes, macrophages, and monocytes. The results from the inverse variance weighted (IVW) analysis elucidated that DUSP1 (OR = 0.664) serves as a protective factor for DN, whereas TGFBI (OR = 1.114) constitutes a risk factor for DN, in agreement with bioinformatics analysis. Conclusions The present study may provide new insights into the mechanisms underlying DN onset and progression, as well as the selection of DN diagnostic markers and therapeutic targets.

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Section: Discussionsupporting

confidence: 91%

Identification of diagnostic biomarkers for diabetes nephropathy by multi-chip integrated bioinformatics combining machine-learning strategies and mendelian randomization

Su,

Guo,

et al. 2024

Preprint

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“…EPHA3 has been proven to play an important role in cancer 25 and energy metabolism, 26 and it might participate in the currency of OA 27 . The expression of CEBPB decreased in patients with DN, 28 and the overexpression of CEBPB could reverse the proliferation of OA‐FLS cells induced by hypoxia 29 . UBAP1 played an important role in NF‐kB signal transduction 30 .…”

Section: Discussionmentioning

confidence: 99%

Identification of the shared genes in type 2 diabetes mellitus and osteoarthritis and the role of quercetin

Song,

2024

J Cellular Molecular Medi

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This study investigated the underlying comorbidity mechanism between type 2 diabetes mellitus (T2DM) and osteoarthritis (OA), while also assessing the therapeutic potential of quercetin for early intervention and treatment of these two diseases. The shared genes were obtained through GEO2R, limma and weighted gene co‐expression network analysis (WGCNA), and validated using clinical databases and the area under the curves (ROC). Functional enrichment analysis was conducted to elucidate the underlying mechanisms of comorbidity between T2DM and OA. The infiltration of immune cells was analysed using the CIBERSORT algorithm in conjunction with ESTIMATE algorithm. Subsequently, transcriptional regulation analysis, potential chemical prediction, gene‐disease association, relationships between the shared genes and ferroptosis as well as immunity‐related genes were investigated along with molecular docking. We identified the 12 shared genes (EPHA3, RASIP1, PENK, LRRC17, CEBPB, EFEMP2, UBAP1, PPP1R15A, SPEN, MAFF, GADD45B and KLF4) across the four datasets. Our predictions suggested that targeting these shared genes could potentially serve as therapeutic interventions for both T2DM and OA. Specifically, they are involved in key signalling pathways such as p53, IL‐17, NF‐kB and MAPK signalling pathways. Furthermore, the regulation of ferroptosis and immunity appears to be interconnected in both diseases. Notably, in this context quercetin emerges as a promising drug candidate for treating T2DM and OA by specifically targeting the shared genes. We conducted a bioinformatics analysis to identify potential therapeutic targets, mechanisms and drugs for T2DM and OA, thereby offering novel insights into molecular therapy for these two diseases.

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“…However, despite advancements in treatment options [1,3,4], effective prevention and management of DKD remain limited, and reliable biomarkers to predict disease progression are lacking. Although recent research has mainly concentrated on glomerular diseases, emerging evidence indicates that tubulointerstitial injury may independently contribute to renal disease progression [5,6]. More importantly, tubular damage mediated by inflammation seems to predominate over glomerular injury in early DKD [7].…”

Section: Introductionmentioning

confidence: 99%

“…Bioinformatics approaches have been increasingly utilized to identify differentially expressed genes (DEGs) from gene microarrays and high-throughput sequencing data. Recent efforts [6,16] have identified DEGs associated with tubulointerstitial injury in DKD, potentially obtaining a novel, effective approach to elucidate the pathogenic mechanisms of DKD and uncover new therapeutic targets. This study is aimed at comprehensively analyzing the association between NET-related genes (NRGs) [17] and the DEGs to discern DKD patients effectively.…”

Section: Introductionmentioning

confidence: 99%

NET‐Related Gene as Potential Diagnostic Biomarkers for Diabetic Tubulointerstitial Injury

Liang,

Lin,

Huang

et al. 2024

Journal of Diabetes Research

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Background: Tubulointerstitial injury plays a pivotal role in the progression of diabetic kidney disease (DKD), yet the link between neutrophil extracellular traps (NETs) and diabetic tubulointerstitial injury is still unclear.Methods: We analyzed microarray data (GSE30122) from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) associated with DKD’s tubulointerstitial injury. Functional and pathway enrichment analyses were conducted to elucidate the involved biological processes (BP) and pathways. Weighted gene coexpression network analysis (WGCNA) identified modules associated with DKD. LASSO regression and random forest selected NET-related characteristic genes (NRGs) related to DKD tubulointerstitial injury.Results: Eight hundred ninety-eight DEGs were identified from the GSE30122 dataset. A significant module associated with diabetic tubulointerstitial injury overlapped with 15 NRGs. The hub genes, CASP1 and LYZ, were identified as potential biomarkers. Functional enrichment linked these genes with immune cell trafficking, metabolic alterations, and inflammatory responses. NRGs negatively correlated with glomerular filtration rate (GFR) in the Neph v5 database. Immunohistochemistry (IHC) validated increased NRGs in DKD tubulointerstitial injury.Conclusion: Our findings suggest that the CASP1 and LYZ genes may serve as potential diagnostic biomarkers for diabetic tubulointerstitial injury. Furthermore, NRGs involved in diabetic tubulointerstitial injury could emerge as prospective targets for the diagnosis and treatment of DKD.

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Identification of Transcription Factors related to Diabetic Tubulointerstitial Injury

Cited by 3 publications

References 32 publications

Identification of diagnostic biomarkers for diabetes nephropathy by multi-chip integrated bioinformatics combining machine-learning strategies and mendelian randomization

Identification of diagnostic biomarkers for diabetes nephropathy by multi-chip integrated bioinformatics combining machine-learning strategies and mendelian randomization

Identification of the shared genes in type 2 diabetes mellitus and osteoarthritis and the role of quercetin

NET‐Related Gene as Potential Diagnostic Biomarkers for Diabetic Tubulointerstitial Injury

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