The Study of the Association of Polymorphisms in LSP1, GPNMB, PDPN, TAGLN, TSPO, and TUBB6 Genes with the Risk and Outcome of Ischemic Stroke in the Russian Population

Khrunin, Andrey; Khvorykh, Gennady V.; Arapova, Anna S.; Kulinskaya, Anna E.; Koltsova, Evgeniya A.; Petrova, Elizaveta A.; Kimelfeld, Ekaterina I.; Limborska, Svetlana A.

doi:10.3390/ijms24076831

Cited by 3 publications

(2 citation statements)

References 74 publications

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“…34 The GPNMB, although not previously studied in SAH, demonstrates significant upregulation in ischemic stroke. 38,39 Its potential as a therapeutic target, associated with inflammation and neuroinflammation, warrants exploration. 40 Osteopontin (OPN), or SPP1, plays a dual role in early brain injury and delayed cerebral ischemia in aneurysmal SAH.…”

Section: Discussionmentioning

confidence: 99%

Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage‐associated biomarkers by bulk and single‐cell sequencing

Yang,

Hu,

Wang

et al. 2024

J Cellular Molecular Medi

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We investigated subarachnoid haemorrhage (SAH) macrophage subpopulations and identified relevant key genes for improving diagnostic and therapeutic strategies. SAH rat models were established, and brain tissue samples underwent single‐cell transcriptome sequencing and bulk RNA‐seq. Using single‐cell data, distinct macrophage subpopulations, including a unique SAH subset, were identified. The hdWGCNA method revealed 160 key macrophage‐related genes. Univariate analysis and lasso regression selected 10 genes for constructing a diagnostic model. Machine learning algorithms facilitated model development. Cellular infiltration was assessed using the MCPcounter algorithm, and a heatmap integrated cell abundance and gene expression. A 3 × 3 convolutional neural network created an additional diagnostic model, while molecular docking identified potential drugs. The diagnostic model based on the 10 selected genes achieved excellent performance, with an AUC of 1 in both training and validation datasets. The heatmap, combining cell abundance and gene expression, provided insights into SAH cellular composition. The convolutional neural network model exhibited a sensitivity and specificity of 1 in both datasets. Additionally, CD14, GPNMB, SPP1 and PRDX5 were specifically expressed in SAH‐associated macrophages, highlighting its potential as a therapeutic target. Network pharmacology analysis identified some targeting drugs for SAH treatment. Our study characterised SAH macrophage subpopulations and identified key associated genes. We developed a robust diagnostic model and recognised CD14, GPNMB, SPP1 and PRDX5 as potential therapeutic targets. Further experiments and clinical investigations are needed to validate these findings and explore the clinical implications of targets in SAH treatment.

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Section: Discussionmentioning

confidence: 99%

Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage‐associated biomarkers by bulk and single‐cell sequencing

Yang,

Hu,

Wang

et al. 2024

J Cellular Molecular Medi

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“…SERPINA1 [521], LRRK2 [649], TF (transferrin) [650], REN (renin) [651], TLR2 [652], MOBP (myelin associated oligodendrocyte basic protein) [653], SYK (spleen associated tyrosine kinase) [654], MBP (myelin basic protein) [625], SHH (sonic hedgehog signaling molecule) [655], RBP4 [590], SNCB (synuclein beta) [543], MMP3 [656], VWF (von Willebrand factor) [657], EGFR (epidermal growth factor receptor) [658], ABCA1 [659] and GPR39 [618] are associated with the clinical stages of dementia. Altered expression of LRRK2 [660], GPR17 [661], TF (transferrin) [662], NEUROD1 [663], RELN (reelin) [664], LCP1 [665], TFAP2B [666], HOXA5 [667], MSR1 [668], REN (renin) [669], OLIG2 [670], TLR2 [671], WT1 [672], SLC22A3 [673], CADM2 [674], PRKCH (protein kinase C eta) [675], MBP (myelin basic protein) [676], XAF1 [677], GPR37 [678], SHH (sonic hedgehog signaling molecule) [679], VTN (vitronectin) [680], SIRT2 [681], RBP4 [682], BMP2 [683], CDH13 [684], NR3C2 [685], TRPC6 [686], MMP3 [687], FGF19 [688], HLA-DRB1 [689], CD74 [690], VWF (von Willebrand factor) [691], ANGPT2 [692], CHRDL1 [693], AKR1C3 [694], NEDD4L [695], CASP1 [696], PDE2A [697], GAS7 [698], TET1 [699], EFNB2 [700], EGFR (epidermal growth factor receptor) [701], ABCA1 [702], PDPN (podoplanin) [703], SLCO1B1 [704], COMT (catechol-O-methyltransferase) [705] and GPR39 [706] have been reported to be associated with stroke. CHRNA4 [707], RELN (reelin) [708], VEGFC (vascular endothelial growth factor C) [709], FRMPD4 [710], SCN4B [711], SLCO5...…”

Section: Discussionmentioning

confidence: 99%

Identification of key genes and signaling pathway in the pathogenesis of Huntington's disease via bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Huntington's disease (HD) is the primary cause of progressive motor deficits, psychiatric symptoms, and cognitive impairment. The exact molecular mechanisms of HD pathogenesis are largely unknown. This investigation aims to identify the hub genes, miRNA and TFs in HD and explore their potential molecular regulatory network. Next generation sequencing (NGS) dataset (GSE105041) was extracted from the Gene Expression Omnibus (GEO) database. An integrated bioinformatics pipeline including identification of differentially expressed genes (DEGs), Gene ontology and REACTOME pathway enrichment analysis, protein-protein interaction (PPI) network and module analysis, miRNA-hub gene regulatory network analysis and TF-hub gene regulatory network analysis, and receiver operating characteristic curve (ROC) analysis were applied to identify hub genes, miRNA and TFs, and key drivers of HD pathogenesis. We identified 958 DEGs in the discovery phase, consisting of 479 up regulated genes and 479 down regulated genes. GO and REACTOME enrichment analyses of the 479 up regulated genes and 479 down regulated genes showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and MHC class II antigen presentation. Further analysis of the PPI network using Cytoscape and PEWCC plugins identified 10 hub genes, including LRRK2, MTUS2, HOXA1, IL7R, ERBB3, EGFR, TEX101, WDR76, NEDD4L and COMT. Possible target miRNAs and TFs, including hsa-mir-1292-5p, hsa-mir-4521, ESRRB and SREBF1, were predicted by constructing a miRNA-hub gene regulatory network analysis and TF-hub gene regulatory network. This investigation used bioinformatics methods to explore the molecular pathogenesis of HD, and identified potential molecular markers. These molecular markers might provide novel ideas and methods for the early diagnosis, treatment, and monitoring of HD.

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The Identification of Key Genes and Biological Pathways in Cardiac Arrest by Integrated Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad,

Vastrad

2024

Preprint

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Cardiac arrest (CA) is a common cause of death world wide. The disease has lacks effective treatment. Efforts have been made to elucidate the molecular pathogenesis of CA, but the molecular mechanisms remain elusive. To identify key genes and pathways in CA, the next generation sequencing (NGS) GSE200117 dataset was downloaded from the Gene Expression Omnibus (GEO) database. DESeq2 tool was used to recognize differentially expressed genes (DEGs). Gene ontology (GO) and REACTOME pathway enrichment analyses were performed to analyze the DEGs and associated signal pathways in the g:Profiler database. The IID database was used to construct the protein-protein interaction (PPI) network, and modules analysis was performed using Cytoscape. A miRNA-hub gene regulatory network and TF-hub gene regulatory network were then constructed to screen miRNAs, TFs and hub genes by miRNet and NetworkAnalyst database and Cityscape software. Receiver operating characteristic curve (ROC) analysis used to verified the hub genes. In total, 844 DEGs were identified, comprising 414 up regulated genes and 430 down regulated genes. GO and REACTOME pathway enrichment analyses indicated that the DEGs for CA were mainly enriched in organonitrogen compound metabolic process, response to stimulus, translation and immune system. Ten hub genes (up-regulated: HSPA8, HOXA1, INCA1 and TP53; down-regulated: HSPB1, LMNA, SNCA, ADAMTSL4 and PDLIM7) were screened. We also predicted miRNAs (hsa-mir-1914-5p and hsa-mir-598-3p) and TFs (JUN and PRRX2) targeting hub genes. This study uses a series of bioinformatics technologies to obtain hug genes, miRNAs and TFs, and key pathways related to CA. These analysis results provide us with new ideas for finding biomarkers and treatment methods for CA.

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The Study of the Association of Polymorphisms in LSP1, GPNMB, PDPN, TAGLN, TSPO, and TUBB6 Genes with the Risk and Outcome of Ischemic Stroke in the Russian Population

Cited by 3 publications

References 74 publications

Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage‐associated biomarkers by bulk and single‐cell sequencing

Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage‐associated biomarkers by bulk and single‐cell sequencing

Identification of key genes and signaling pathway in the pathogenesis of Huntington's disease via bioinformatics and next generation sequencing data analysis

The Identification of Key Genes and Biological Pathways in Cardiac Arrest by Integrated Bioinformatics and Next Generation Sequencing Data Analysis

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