Identification of Hub Genes Associated With Clinical Characteristics and Potential Therapy of Diabetic Nephropathy by Bioinformatics Analysis.

Xu, Sai; Wu, Sha; Li, Xiaoning

doi:10.21203/rs.3.rs-136161/v1

Cited by 1 publication

(1 citation statement)

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“…By means of LASSO regression analysis, the TXN, PHLDA2, RPLP1, MT1G, and SDC4 genes were identi ed and incorporated into a predictive model that exhibited robust diagnostic e cacy and remarkable stability. Xu et al utilised transcriptome sequencing datasets of DFU for analysis and revealed that TXN is the hub gene associated with the wound-healing process of DFU [31]. This nding aligns with the outcomes of our study.…”

Section: Expression Of Key Genes In Single Cellssupporting

confidence: 91%

Single-cell RNA sequencing and transcriptomic analysis reveal the critical signatures involved in nonhealing diabetic foot ulcers

Hu,

Yu,

et al. 2024

Preprint

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Background Diabetic foot ulcer (DFU) is a prevalent complication associated with diabetes that is characterised by high morbidity, high disability and high mortality and involves chronic inflammation and infiltration of multiple immune cells. However, the molecular mechanisms underlying DFU remain unclear. Here, we aimed to identify the critical signatures in nonhealing DFUs using single-cell RNA sequencing and transcriptomic analysis.Methods The GSE165816, GSE134431, and GSE143735 datasets were downloaded from the GEO database. First, we preliminarily processed and screened the datasets, removed low-quality data and identified the cell subsets. Each cell subtype was annotated, and the predominant cell types contributing to the disease were analysed. Based on this information, a prediction model was constructed with the training set GSE134431 and testing set GSE143735. Key genes were identified using the LASSO regression algorithm, followed by verification of model accuracy and stability. Additionally, we investigated the molecular mechanisms and changes in signalling pathways associated with this disease using immunoinfiltration analysis, GSEA, and GSVA.Results Through scRNA-seq analysis, we identified 12 distinct cell clusters and determined that the basalKera cell type was important in disease development. A prediction model with high accuracy and stability was constructed incorporating five key genes (TXN, PHLDA2, RPLP1, MT1G, and SDC4). Immune cell infiltration analysis, GSEA, and GSVA revealed alterations in immune cells and signalling pathways throughout disease progression, primarily involving CD8+ T cells, T helper cells, the hypoxia-inducible factor signalling pathway, and the interleukin-17 signalling pathway.Conclusions Our study identified six key genes, namely, TXN, PHLDA2, RPLP1, MT1G, and SDC4, which are significantly associated with the development of nonhealing DFU and play a crucial role in immune cell infiltration. The identified genes have the potential to serve as new prevention and treatment strategies for DFU.

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Section: Expression Of Key Genes In Single Cellssupporting

confidence: 91%