Sparcl1 and Atherosclerosis

Xu, Cheng; Chen, Xinyan; Zhang, Min; Wan, Yufeng; Ge, Shenglin; Cheng, Xin

doi:10.2147/jir.s406907

Cited by 5 publications

(3 citation statements)

References 46 publications

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“…As a result, a total of 955 DEGs were identified. [99], AZGP1 [100], CYP1B1 [101], PRRX1 [102], CD34 [103], A2M [104], CDKN2A [105], SERPINE1 [106], CD44 [107], FABP4 [108], ITGB3 [109], ALOX5AP [110], DAND5 [111], SFRP4 [112], RUNX2 [113], TACR3 [114], MYD88 [115], CYBA (cytochrome b-245 alpha chain) [116], STAT6 [117], FOXC1 [118], FN1 [119], TLR6 [120], CAV1 [121], RGS4 [122], TPM2 [123], TNFSF4 [124], LOX (lysyl oxidase) [125], SMOC2 [126], SPHK1 [127], FOLH1 [128], CYP2C8 [129], CD163 [130], DIRAS3 [131], OSMR (oncostatin M receptor) [132], POSTN (periostin) [133], SELL (selectin L) [134], TMPRSS2 [135], FLNC (filamin C) [136], CXCL16 [137], APOBR (apolipoprotein B receptor) [138], COL6A2 [139], LTBP2 [140], SPARCL1 [141], FOSL2 [142], ISL1 [143], HTR2C [144], TNNT2 [145], HGF (hepatocyte growth factor) [146], IL33 [147], SYK (spleen associated tyrosine kinase) [148], ADRB1 [149], CMKLR1 [150], SHOX2 [151], MEG3 [152], SCUBE1 [153...…”

Section: Discussionmentioning

confidence: 99%

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad,

Vastrad

2023

Preprint

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Schizophrenia is thought to be the most prevalent chronic psychiatric disorder. Numerous proteins have been identified that are associated with the occurrence and development of schizophrenia. This study aimed to identify potential core genes and pathways involved in schizophrenia, through exhaustive bioinformatic and next generation sequencing (NGS) data analyses using GSE20966 NGS data of neural progenitor cells and neurons obtained from healthy controls and patients with schizophrenia. The NGS data were downloaded from the Gene Expression Omnibus database. NGS data was processed by the DESeq2 package in R software and the differentially expressed genes (DEGs) were identified. Gene Ontology (GO) enrichment analysis and REACTOME pathway enrichment analysis were carried out to identify potential biological functions and pathways of the DEGs. Protein-protein interaction (PPI) network, module, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to identify the hub genes, miRNA and TFs. Potential hub genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). In this investigation, an overall 955 DEGs were identified: 478 genes were remarkably up regulated and 477 genes were distinctly down regulated. These genes were enriched for GO terms and pathways mainly involved in the multicellular organismal process, GPCR ligand binding, regulation of cellular process and amine ligand-binding receptors. MYC, FN1, CDKN2A, EEF1G, CAV1, ONECUT1, SYK, MAPK13, TFAP2A and BTK were considered the potential hub genes. miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed successfully. On the whole, the findings of this investigation enhance our understanding of the potential molecular mechanisms of schizophrenia and provide potential targets for further investigation.

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Section: Discussionmentioning

confidence: 99%

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad,

Vastrad

2023

Preprint

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“…The secreted protein acidic rich in cysteine-like 1 (SPARCL1), also known as Hevin, MAST9, SC1 and ECM2, is a matricellular protein which is a member of SPARC-related protein family [ 8 ]. SPARC family members could be secreted into the ECM and play different roles in regulating cell adhesion, proliferation, metastasis, and growth factor signaling to affect the extracellular matrix (ECM) and interactions between cells [ 9 ]. Studies have revealed that SPARCL1 is an adhesion molecule mediating the cell–matrix interactions and participates in physiological processes such as cell adhesion, differentiation and proliferation [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

SPARCL1 promotes chondrocytes extracellular matrix degradation and inflammation in osteoarthritis via TNF/NF-κB pathway

Miao,

Wu,

Gong

et al. 2024

Journal of Orthopaedic Translation

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“…[12][13][14] It can regulate cell-matrix interactions and participates in tissue remodeling. [25][26][27] Compelling evidence has confirmed that matricellular proteins are the important regulators of neuronal integrity. [28][29][30] As such, SPARCL1 could impact synaptic stability and may play a pivotal effect in axonal regeneration after acute brain injury.…”

mentioning

confidence: 99%

Serum Secreted Protein Acidic and Rich in Cysteine-Like 1 as a Biochemical Predictor for Prognosticating Clinical Outcomes After Acute Supratentorial Intracerebral Hemorrhage: A Prospective Cohort Study

Huang,

Shao,

Chen

et al. 2023

NDT

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Background Secreted protein acidic and rich in cysteine-like 1 (SPARCL1) regulates synaptic stability and is up-regulated during axonal regeneration. Here, serum SPARCL1 was determined for estimating severity and prognosticating early neurological deterioration (END) and functional outcomes of acute intracerebral hemorrhage (ICH). Methods In this prospective observational cohort study of 156 patients with supratentorial ICH, blood samples of 53 were acquired not only at admission but also ad days 1, 3, 5, 7 and 10. Another group of 53 healthy controls were recruited. The modified Rankin Scale (mRS) scores of 3–6 at poststroke six months were regarded as poor prognosis. Results As opposed to controls, serum SPARCL1 levels were markedly elevated during the early ten days after ICH, with the highest levels at days 1 and 3. Admission serum SPARCL1 levels were independently correlated with National Institutes of Health Stroke Scale scores and hematoma volume, were significantly increased in the order of six-month mRS scores from 0 to 6 and were independently correlated with six-month mRS scores. Serum SPARCL1 levels were linearly related to risks of poor six-month prognosis and END under restricted cubic spline, had significant efficiency under receiver operating characteristic (ROC) curve and were independently associated with END and poor prognosis. Subgroup analysis confirmed that no interactions existed for associations of serum SPARCL1 levels with other variables, such as age, gender and some specific vascular risk factors. END and poor prognosis prediction models integrating serum SPARCL1 were displayed using the two nomograms. The poor prognosis prediction model, but END prediction model not, performed well under calibration curve, decision curve and ROC curve. Conclusion A substantial elevation of serum SPARCL1 levels during the early period after ICH is independently related to illness severity and poor neurological outcomes, thus signifying that serum SPARCL1 may appear as a prognostic biomarker of ICH.

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Sparcl1 and Atherosclerosis

Cited by 5 publications

References 46 publications

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

SPARCL1 promotes chondrocytes extracellular matrix degradation and inflammation in osteoarthritis via TNF/NF-κB pathway

Serum Secreted Protein Acidic and Rich in Cysteine-Like 1 as a Biochemical Predictor for Prognosticating Clinical Outcomes After Acute Supratentorial Intracerebral Hemorrhage: A Prospective Cohort Study

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