Identification of an miRNA Regulatory Network and Candidate Markers for Ischemic Stroke Related to Diabetes

Zhou, Hui; Liu-jia, Huang; Liang, Lucong; Chen, Liechun; Zou, Chun; Li, Zhenhua; Li, Rongjie; Jian, Chongdong; Zou, Donghua

doi:10.2147/ijgm.s319503

Cited by 4 publications

(4 citation statements)

References 51 publications

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“…For the purpose of identifying subtype associated genes, we adopted WGCNA algorithm through the “WGCNA” R package (Version: 1.71) ( Langfelder et al, 2009 ). At first, genes that exhibited a variance value exceeding 25% were selected for the construction of the coexpression network ( Yang and Xu, 2021 ; Zhou et al, 2021 ; Feng et al, 2022 ). Subsequently, the outlier samples were excluded through the “goodSampleGenes” function, and the soft-thresholding value β = 5 (scale free = 0.85) was applied to ensure a scale-free network.…”

Section: Methodsmentioning

confidence: 99%

Integrating bulk and single-cell RNA sequencing data reveals epithelial-mesenchymal transition molecular subtype and signature to predict prognosis, immunotherapy efficacy, and drug candidates in low-grade gliomas

Wang,

2023

Front. Pharmacol.

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Objective: Epithelial-mesenchymal transition (EMT) is a tightly regulated and dynamic process occurring in both embryonic development and tumor progression. Our study aimed to comprehensively explore the molecular subtypes, immune landscape, and prognostic signature based on EMT-related genes in low-grade gliomas (LGG) in order to facilitate treatment decision-making and drug discovery.Methods: We curated EMT-related genes and performed molecular subtyping with consensus clustering algorithm to determine EMT expression patterns in LGG. The infiltration level of diverse immune cell subsets was evaluated by implementing the single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithms. The distinctions in clinical characteristics, mutation landscape, and immune tumor microenvironment (TME) among the subtypes were subjected to further investigation. Gene Set Variation Analysis (GSVA) was performed to explore the biological pathways that were involved in subtypes. The chemo drug sensitivity and immunotherapy of subtypes were estimated through GDSC database and NTP algorithm. To detect EMT subtype-related prognostic gene modules, the analysis of weighted gene co-expression network (WGCNA) was performed. The LASSO algorithm was utilized to construct a prognostic risk model, and its efficacy was verified through an independent CGGA dataset. Finally, the expression of the hub genes from the prognostic model was evaluated through the single-cell dataset and in-vitro experiment.Results: The TCGA-LGG dataset revealed the creation of two molecular subtypes that presented different prognoses, clinical implications, TME, mutation landscapes, chemotherapy, and immunotherapy. A three-gene signature (SLC39A1, CTSA and CLIC1) based on EMT expression pattern were established through WGCNA analysis. Low-risk patients showed a positive outlook, increased immune cell presence, and higher expression of immune checkpoint proteins. In addition, several promising drugs, including birinapant, fluvastatin, clofarabine, dasatinib, tanespimycin, TAK−733, GDC−0152, AZD8330, trametinib and ingenol-mebutate had great potential to the treatment of high risk patients. Finally, CTSA and CLIC1 were highly expressed in monocyte cell through single-cell RNA sequencing analysis.Conclusion: Our research revealed non-negligible role of EMT in the TME diversity and complexity of LGG. A prognostic signature may contribute to the personalized treatment and prognostic determination.

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Section: Methodsmentioning

confidence: 99%

Integrating bulk and single-cell RNA sequencing data reveals epithelial-mesenchymal transition molecular subtype and signature to predict prognosis, immunotherapy efficacy, and drug candidates in low-grade gliomas

Wang,

2023

Front. Pharmacol.

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“…In the previous studies, the key genes of diabetes mellitus-related coronary heart disease 4 and ischemic stroke related to diabetes were acquired. 5 In that work, a classifier that can diagnose coronary artery disease was developed, 6 but it is not applicable to diabetes mellitus-associated coronary artery disease (DMCAD). How DMCAD arises is poorly understood, although it seems to involve the ubiquitin-proteasome system, 7 inflammatory factors (fibrinogen, C-reactive protein, galectin-3) and metabolic factors such as lipoproteins.…”

Section: Introductionmentioning

confidence: 99%

RUNX1 and CCL3 in Diabetes Mellitus-Related Coronary Artery Disease: A Bioinformatics Analysis

Zhong¹,

Du²,

Liu³

et al. 2022

IJGM

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Background Cardiovascular complications are a major cause of death and disability in patients with diabetes mellitus, but how such complications arise is unclear. Methods Weighted gene correlation network analysis (WGCNA) was performed on gene expression profiles from healthy controls, individuals with diabetes mellitus, and individuals with diabetes mellitus-associated coronary artery disease (DMCAD). Phenotypically related module genes were analyzed for enrichment in Gene Ontology (GO) terms and Kyoto Gene and Genome Encyclopedia (KEGG) pathways. Predicted biological functions were validated using gene set enrichment analysis (GSEA) and ClueGo analysis. Based on the TRRUST v2 database and hypergeometric tests, a global network was built to identify transcription factors (TFs) and downstream target genes potentially involved in DMCAD. Results WGCNA identified three modules associated with progression from diabetes mellitus to DMCAD. The module genes were significantly involved in biological processes related to interferon and viral infection, while GSEA of DMCAD samples suggested involvement in viral myocarditis, chemokine signaling and phagosomes. RUNX1 was identified as a potential TF regulating these module genes. Analysis of the global regulatory network of TFs and their targets suggested that CCL3 may be a key regulator in DMCAD. Conclusion We found bioinformatic evidence that CCL3 may be a key regulator and RUNX1 a key TF in DMCAD.

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“…In this context, many studies have taken advantage of these tools and suggested a panel of miRNAs for AIS diagnosis [20][21][22][23][24]. Nevertheless, there is a lack of consensus on the miRNAs expressed in all of them.…”

Section: Introductionmentioning

confidence: 99%

Structural and Pharmacological Network Analysis of miRNAs Involved in Acute Ischemic Stroke: A Systematic Review

Barrera-Vázquez

Gómez-Verján

Ramírez‐Aldana

et al. 2022

IJMS

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Acute ischemic stroke (AIS) is among the main causes of mortality worldwide. A rapid and opportune diagnosis is crucial to improve a patient’s outcomes; despite the current advanced image technologies for diagnosis, their implementation is challenging. MicroRNAs have been recognized as useful as biomarkers since they are specific and stable for characterization of AIS. However, there is still a lack of consensus over the primary miRNAs implicated in AIS. Here, we performed a systematic review of the literature covering from 2015–2021 regarding miRNAs expression during AIS and built structural networks to analyze and identify the most common miRNAs expressed during AIS and shared pathways, genes, and compounds that seem to influence their expression. We identified two sets of miRNAs: on one side, a set that was independent of geographical location and tissue (miR-124, miR-107, miR-221, miR-223, miR-140, miR-151a, miR-181a, miR-320b, and miR-484); and on the other side, a set that was connected (hubs) in biological networks (miR-27b-3p, miR-26b-5p, miR-124-3p, miR-570-3p, miR-19a-3p, miR-101-3p and miR-25-3p), which altered FOXO3, FOXO4, and EP300 genes. Interestingly, such genes are involved in cell death, FOXO-mediated transcription, and brain-derived neurotrophic factor signaling pathways. Finally, our pharmacological network analysis depicted a set of toxicants and drugs related to AIS for the first time.

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Identification of an miRNA Regulatory Network and Candidate Markers for Ischemic Stroke Related to Diabetes

Cited by 4 publications

References 51 publications

Integrating bulk and single-cell RNA sequencing data reveals epithelial-mesenchymal transition molecular subtype and signature to predict prognosis, immunotherapy efficacy, and drug candidates in low-grade gliomas

Integrating bulk and single-cell RNA sequencing data reveals epithelial-mesenchymal transition molecular subtype and signature to predict prognosis, immunotherapy efficacy, and drug candidates in low-grade gliomas

RUNX1 and CCL3 in Diabetes Mellitus-Related Coronary Artery Disease: A Bioinformatics Analysis

Structural and Pharmacological Network Analysis of miRNAs Involved in Acute Ischemic Stroke: A Systematic Review

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