Association Analysis of WNT3, HLA-DRB5 and IL1R2 Polymorphisms in Chinese Patients With Parkinson’s Disease and Multiple System Atrophy

Su, Wei‐Ming; Gu, Xiaojing; Hou, Yanbing; Zhang, Lingyu; Cao, Bei; Ou, Ruwei; Wu, Ying; Chen, Xueping; Song, Wei; Zhao, Bi; Shang, Huifang; Chen, Yongping

doi:10.3389/fgene.2021.765833

Cited by 6 publications

(5 citation statements)

References 54 publications

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“…CD74 [493], VWF (von Willebrand factor) [267], MTTP (microsomal triglyceride transfer protein) [494], ANGPT2 [184], AKR1C3 [495], NEDD4L [496], CASP1 [497], LDB2 [498], CHRNA5 [499], CCND2 [500], BRCA2 [97], ZBTB20 [501], EFNB2 [502], CYP7B1 [503], DCLK1 [504], RBM20 [505], PLCE1 [197], EGFR (epidermal growth factor receptor) [276], ABCA1 [506], PDPN (podoplanin) [507], SCN4B [508], SLCO1B1 [509], CRHR2 [510], RND3 [511], COMT (catechol-O-methyltransferase) [512], GPR39 [513], GRB14 [514], HDC (histidine decarboxylase) [515], DAPK2 [516], SMAD9 [517], TXNRD2 [518] and KIF15 [519] are altered expressed in cardiovascular diseases. Previous studies have proven that the FGF8 [520], SERPINA1 [521], LRRK2 [522], CHRNA4 [523], TF (transferrin) [524], TRDN (triadin) [525], NR0B1 [526], RELN (reelin) [527], HTR2C [528], RIT2 [529], REN (renin) [530], TLR2 [531], MOBP (myelin associated oligodendrocyte basic protein) [532], TNR (tenascin R) [533], ADGRB1 [534], GPR37 [535], SHH (sonic hedgehog signaling molecule) [536], XDH (xanthine dehydrogenase) [537], SIRT2 [538], BMP2 [539], HAS2 [540], CSMD1 [541], NTF3 [542], SNCB (synuclein beta) [543], MMP3 [544], HLA-DRB5 [545], HLA-DRB1 [546], HLA-DRA [547], VWF (von Willebrand factor) [548], AKR1C3 [549], CASP1 [550], CHRNA5 [551], TET1 [552], LRRN3 [553], EGFR (epidermal growth factor receptor) [554], PLXNA4 [555], FOXG1 [556], MAP1B [557], ANK1 […”

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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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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“…FASLG (Fas ligand) [574], GJC2 [575] and GJB1 [576] have been identified to be involved in the development of spasticity. CD24 [577], CD28 [578], HLA-DRB5 [579], LTF (lactotransferrin) [580], GPNMB (glycoprotein nmb) [581], TNFRSF9 [582], LRRC37A2 [583], DRD3 [584], CD163 [369], HGF (hepatocyte growth factor) [585], TRPM8 [446], TTR (transthyretin) [586], VEGFA (vascular endothelial growth factor A) [587], CHI3L1 [588], MAG (myelin associated glycoprotein) [589], SREBF1 [217], HIP1R [590], HK2 [591], GPR37 [592], NGFR (nerve growth factor receptor) [593], TF (transferrin) [594], HAPLN2 [451], MOG (myelin oligodendrocyte glycoprotein) [595], BIN1 [596], BMP2 [597], GADD45B [598], UNC5B [599], ADORA1 [600], SEPTIN4 [601], DHCR7 [602], SCD (stearoyl-CoA desaturase) [603], GIPC1 [604], ALDH1A1 [605] and CTNNA3 [606] participate in pathogenic processes of Parkinson’s disease. At present, investigation on these key regulatory genes and related molecular pathways is lacking.…”

Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

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Multiple sclerosis (MS) is the main reason for disability caused by autoimmunity. However, effective biomarkers for MS have not been identified. This study explored the molecular mechanisms and potential therapeutic targets for MS occurrence and progression using bioinformatics and next generation sequencing (NGS) data analysis. NGS data GSE138614, including patients with MS and 25 normal control samples, were obtained from Gene Expression Omnibus (GEO) database Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. Protein–protein interaction (PPI) network and module analyses were performed based on the DEGs. MiRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were built by Cytoscape to predict the underlying microRNAs (miRNAs) and transcription factors (TFs) associated with hub genes. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. A total of 959 DEGs (479 up regulated genes and 480 down regulated genes) were detected. The GO terms and pathways of DEGs involve immune system process, developmental process, immune system and regulation of cholesterol biosynthesis by SREBP (SREBF). The network analysis revealed that hub genes include LCK, PYHIN1, SLAMF1, DOK2, TAB2, CFTR, RHOB, LMNA, EGLN3 and ERBB3 might be involved in the development of MS. The present investigation illustrates a characteristic gene profile in MS, which might contribute to the interpretation of the progression of MS and provide novel biomarkers and therapeutic targets for MS.

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“…The expression of plasma ceruloplasmin was also significantly upregulated in high-grade clear cell renal cell carcinoma samples ( Thibodeau et al, 2016 ). HLA-DRB5, whose expression products play a central role in the immune system by presenting peptides derived from extracellular proteins ( Su et al, 2021 ). Studies have shown that HLA-DRB5 is associated with risk factors for cervical cancer ( Bao et al, 2018 ).…”

Section: Discussionunclassified

Single-cell RNA sequencing analysis to explore immune cell heterogeneity and novel biomarkers for the prognosis of lung adenocarcinoma

Wang

Liang

et al. 2022

Front. Genet.

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Background: Single-cell RNA sequencing is necessary to understand tumor heterogeneity, and the cell type heterogeneity of lung adenocarcinoma (LUAD) has not been fully studied.Method: We first reduced the dimensionality of the GSE149655 single-cell data. Then, we statistically analysed the subpopulations obtained by cell annotation to find the subpopulations highly enriched in tumor tissues. Monocle was used to predict the development trajectory of five subpopulations; beam was used to find the regulatory genes of five branches; qval was used to screen the key genes; and cellchart was used to analyse cell communication. Next, we used the differentially expressed genes of TCGA-LUAD to screen for overlapping genes and established a prognostic risk model through univariate and multivariate analyses. To identify the independence of the model in clinical application, univariate and multivariate Cox regression were used to analyse the relevant HR, 95% CI of HR and p value. Finally, the novel biomarker genes were verified by qPCR and immunohistochemistry.Results: The single-cell dataset GSE149655 was subjected to quality control, filtration and dimensionality reduction. Finally, 23 subpopulations were screened, and 11-cell subgroups were annotated in 23 subpopulations. Through the statistical analysis of 11 subgroups, five important subgroups were selected, including lung epithelial cells, macrophages, neuroendocrine cells, secret cells and T cells. From the analysis of cell trajectory and cell communication, it is found that the interaction of five subpopulations is very complex and that the communication between them is dense. We believe that these five subpopulations play a very important role in the occurrence and development of LUAD. Downloading the TCGA data, we screened the marker genes of these five subpopulations, which are also the differentially expressed genes in tumorigenesis, with a total of 462 genes, and constructed 10 gene prognostic risk models based on related genes. The 10-gene signature has strong robustness and can achieve stable prediction efficiency in datasets from different platforms. Two new molecular markers related to LUAD, HLA-DRB5 and CCDC50, were verified by qPCR and immunohistochemistry. The results showed that HLA-DRB5 expression was negatively correlated with the risk of LUAD, and CCDC50 expression was positively correlated with the risk of LUAD.Conclusion: Therefore, we identified a prognostic risk model including CCL20, CP, HLA-DRB5, RHOV, CYP4B1, BASP1, ACSL4, GNG7, CCDC50 and SPATS2 as risk biomarkers and verified their predictive value for the prognosis of LUAD, which could serve as a new therapeutic target.

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Association Analysis of WNT3, HLA-DRB5 and IL1R2 Polymorphisms in Chinese Patients With Parkinson’s Disease and Multiple System Atrophy

Cited by 6 publications

References 54 publications

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

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

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Single-cell RNA sequencing analysis to explore immune cell heterogeneity and novel biomarkers for the prognosis of lung adenocarcinoma

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