Identification of the Relationship between Hub Genes and Immune Cell Infiltration in Vascular Endothelial Cells of Proliferative Diabetic Retinopathy Using Bioinformatics Methods

Jing, Huang; Zhou, Qiong

doi:10.1155/2022/7231046

Cited by 13 publications

(7 citation statements)

References 62 publications

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“…Diabetic patients with PDR are more prone to proteinuria than diabetic patients without PDR, and the level of proteinuria is positively correlated with the severity of DR. It has also been confirmed 17 that PDR is a risk factor for diabetic nephropathy (DN), which in turn has a great predictive effect on PDR. Our results showed that the level of urinary albumin was an independent risk factor for the occurrence of PDR, which is in line with previous reports.…”

Section: Discussionmentioning

confidence: 92%

Correlation analysis of Type-2 diabetes mellitus with proliferative retinopathy and central macular thickness

Huang,

Jiang,

Niu

2024

Pak J Med Sci

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Objective: To investigate the relevant risk factors of proliferative diabetic retinopathy (PDR) in patients with Type-2 diabetes mellitus (T2DM) and their correlations with the central macular thickness (CMT). Methods: This is a retrospective study. The clinical data of 300 patients with T2DM were collected and divided into a PDR group (observation group) and non-PDR group (control group) according to the occurrence of PDR in Aier Eye Hospital (Taiyuan) from February 2019 to February 2022. The relevant risk factors were screened out through the t test and the χ2 test, and analyzed by logistic regression. Results: Logistic regression analysis showed that systolic blood pressure, diastolic blood pressure, course of diabetes, fasting blood glucose (FBG), two hours postprandial blood glucose (two hours PBG) and urinary albumin were independent risk factors for T2DM complicated with PDR. ROC curve revealed that systolic blood pressure, course of diabetes and urinary albumin had the highest diagnostic efficiency. Correlation analysis demonstrated that CMT was positively correlated with systolic blood pressure, course of diabetes, HbA1c level and urinary albumin level. Conclusion: For patients with T2DM, blood pressure, course of diabetes, FBG, 2hPBG and urinary albumin are independent risk factors for PDR, and increased systolic blood pressure, course of diabetes, HbA1c level and urinary albumin level will increase CMT. Combining the above indexes to predict the occurrence of PDR has a synergistic effect, and the increase in systolic blood pressure, course of diabetes, HbA1c level and urinary albumin level will increase the CMT of the patients. doi: https://doi.org/10.12669/pjms.40.4.7726 How to cite this: Huang H, Jiang S, Niu H. Correlation analysis of Type-2 diabetes mellitus with proliferative retinopathy and central macular thickness. Pak J Med Sci. 2024;40(4):642-647. doi: https://doi.org/10.12669/pjms.40.4.7726 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section: Discussionmentioning

confidence: 92%

Correlation analysis of Type-2 diabetes mellitus with proliferative retinopathy and central macular thickness

Huang,

Jiang,

Niu

2024

Pak J Med Sci

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“…Bioinformatics methods have been widely used for the exploration of key pathogenic factors and potential therapeutic targets in diabetic retinopathy (33)(34)(35). To the best of our knowledge, bioinformatics analysis of pyroptosis-related genes in diabetic retinopathy has not been reported.…”

Section: Discussionmentioning

confidence: 99%

Molecular investigation of candidate genes for pyroptosis-induced inflammation in diabetic retinopathy

et al. 2022

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BackgroundDiabetic retinopathy is a diabetic microvascular complication. Pyroptosis, as a way of inflammatory death, plays an important role in the occurrence and development of diabetic retinopathy, but its underlying mechanism has not been fully elucidated. The purpose of this study is to identify the potential pyroptosis-related genes in diabetic retinopathy by bioinformatics analysis and validation in a diabetic retinopathy model and predict the microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) interacting with them. Subsequently, the competing endogenous RNA (ceRNA) regulatory network is structured to explore their potential molecular mechanism.MethodsWe obtained mRNA expression profile dataset GSE60436 from the Gene Expression Omnibus (GEO) database and collected 51 pyroptosis-related genes from the PubMmed database. The differentially expressed pyroptosis-related genes were obtained by bioinformatics analysis with R software, and then eight key genes of interest were identified by correlation analysis, Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein–protein interaction (PPI) network analysis. Then, the expression levels of these key pyroptosis-related genes were validated with quantitative real-time polymerase chain reaction (qRT-PCR) in human retinal endothelial cells with high glucose incubation, which was used as an in vitro model of diabetic retinopathy. Western blot was performed to measure the protein levels of gasdermin D (GSDMD), dasdermin E (GSDME) and cleaved caspase-3 in the cells. Moreover, the aforementioned genes were further confirmed with the validation set. Finally, the ceRNA regulatory network was structured, and the miRNAs and lncRNAs which interacted with CASP3, TLR4, and GBP2 were predicted.ResultsA total of 13 differentially expressed pyroptosis-related genes were screened from six proliferative diabetic retinopathy patients and three RNA samples from human retinas, including one downregulated gene and 12 upregulated genes. A correlation analysis showed that there was a correlation among these genes. Then, KEGG pathway and GO enrichment analyses were performed to explore the functional roles of these genes. The results showed that the mRNA of these genes was mainly related to inflammasome complex, interleukin-1 beta production, and NOD-like receptor signaling pathway. In addition, eight hub genes—CASP3, TLR4, NLRP3, GBP2, CASP1, CASP4, PYCARD, and GBP1—were identified by PPI network analysis using Cytoscape software. High glucose increased the protein level of GSDMD and GSDME, as critical effectors of pyroptosis, in retinal vascular endothelial cells. Verified by qRT-PCR, the expression of all these eight hub genes in the in vitro model of diabetic retinopathy was consistent with the results of the bioinformatics analysis of mRNA chip. Among them, CASP4, GBP1, CASP3, TLR4, and GBP2 were further validated in the GSE179568 dataset. Finally, 20 miRNAs were predicted to target three key genes—CASP3, GBP2, and TLR4, and 22 lncRNAs were predicted to potentially bind to these 20 miRNAs. Then, we constructed a key ceRNA network that is expected to mediate cellular pyroptosis in diabetic retinopathy.ConclusionThrough the data analysis of the GEO database by R software and verification by qRT-PCR and validation set, we successfully identified potential pyroptosis-related genes involved in the occurrence of diabetic retinopathy. The key ceRNA regulatory network associated with these genes was structured. These findings might improve the understanding of molecular mechanisms underlying pyroptosis in diabetic retinopathy.

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“…In blood T cells, EEF1A1 (ENSG00000156508) encodes the same type of alpha subunit of a complex, namely, elongation factor-1, which is responsible for aminoacyl tRNAase delivery to the ribosome; promotes cell growth and proliferation; and inhibits apoptosis (Mills and Gago, 2021). Huang et al found that the expression of EEF1A1 was positively correlated with the number of initial CD4 + T cells (Huang and Zhou, 2022), indicating that EEF1A1 may be associated with cellular immunity. In addition, EEF1A1 could inhibit viral growth (Zhang et al, 2015), and it is associated with inflammatory responses (Maruyama et al, 2007).…”

Section: Figure 10mentioning

confidence: 99%

Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods

Ren

et al. 2023

Front. Genet.

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Multiple types of COVID-19 vaccines have been shown to be highly effective in preventing SARS-CoV-2 infection and in reducing post-infection symptoms. Almost all of these vaccines induce systemic immune responses, but differences in immune responses induced by different vaccination regimens are evident. This study aimed to reveal the differences in immune gene expression levels of different target cells under different vaccine strategies after SARS-CoV-2 infection in hamsters. A machine learning based process was designed to analyze single-cell transcriptomic data of different cell types from the blood, lung, and nasal mucosa of hamsters infected with SARS-CoV-2, including B and T cells from the blood and nasal cavity, macrophages from the lung and nasal cavity, alveolar epithelial and lung endothelial cells. The cohort was divided into five groups: non-vaccinated (control), 2*adenovirus (two doses of adenovirus vaccine), 2*attenuated (two doses of attenuated virus vaccine), 2*mRNA (two doses of mRNA vaccine), and mRNA/attenuated (primed by mRNA vaccine, boosted by attenuated vaccine). All genes were ranked using five signature ranking methods (LASSO, LightGBM, Monte Carlo feature selection, mRMR, and permutation feature importance). Some key genes that contributed to the analysis of immune changes, such as RPS23, DDX5, PFN1 in immune cells, and IRF9 and MX1 in tissue cells, were screened. Afterward, the five feature sorting lists were fed into the feature incremental selection framework, which contained two classification algorithms (decision tree [DT] and random forest [RF]), to construct optimal classifiers and generate quantitative rules. Results showed that random forest classifiers could provide relative higher performance than decision tree classifiers, whereas the DT classifiers provided quantitative rules that indicated special gene expression levels under different vaccine strategies. These findings may help us to develop better protective vaccination programs and new vaccines.

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Identification of the Relationship between Hub Genes and Immune Cell Infiltration in Vascular Endothelial Cells of Proliferative Diabetic Retinopathy Using Bioinformatics Methods

Cited by 13 publications

References 62 publications

Correlation analysis of Type-2 diabetes mellitus with proliferative retinopathy and central macular thickness

Correlation analysis of Type-2 diabetes mellitus with proliferative retinopathy and central macular thickness

Molecular investigation of candidate genes for pyroptosis-induced inflammation in diabetic retinopathy

Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods

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