Enrichment of rare variants of <i>BIN1</i> but not <i>APOE</i> genes in Chinese patients with Parkinson's disease

Li, Chunyu; Ou, Ruwei; Chen, Yongping; Gu, Xiaojing; Wei, Qianqian; Hou, Yanbing; Cao, Bei; Zhang, Lingyu; Lin, Junyu; Liu, Kuncheng; Song, Wei; Zhao, Bi; Wu, Ying; Shang, Huifang

doi:10.1111/joim.13444

Cited by 1 publication

(1 citation statement)

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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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