Plasma Exosomal S1PR5 and CARNS1 as Potential Non-invasive Screening Biomarkers of Coronary Heart Disease

Xiong, Feng; Mao, Rui; Zhao, Ruohan; Zhang, Lijuan; Tan, Kunyue; Liu, Chunxia; Wang, Shuzhen; Xu, Min; Li, Yang; Zhang, Tongtong

doi:10.3389/fcvm.2022.845673

Cited by 11 publications

(9 citation statements)

References 31 publications

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“…For a more in-depth understanding of these DEGs, we analyzed the selected genes for GO and REACTOME pathway enrichment analyses and found that GO terms and signaling pathways were significant. [106], FCN1 [107], CARNS1 [108], AMH (anti-Mullerian hormone) [109], E2F1 [110], PF4 [111], RPL3L [112], TRIM72 [113], HOXB4 [114], TP73 [115], KCNH2 [116], (advanced glycosylation end-product specific receptor) [117], SMPD3 [118], TYMP (thymidine phosphorylase) [119], RIPPLY3 [120], GREM1 [121], CYP11B2 [122], MYLK2 [123], WNT3A [124], MSX1 [125], COMP (cartilage oligomeric matrix protein) [126], FLI1 [127], ACTA1 [128], TCAP (titincap) [129], TUBB1 [130], TNNI3 [131], HSPB7 [132], DES (desmin) [133], RAP1B [134], TNNT1 [135], BHMT (betaine--homocysteine S-methyltransferase) [136], ANGPTL3 [137], CYP3A5 [138], KMO (kynurenine 3-monooxygenase) [139], HMGCS2 [140], AGXT2 [141], FABP1 [142], SLC22A12 [143], CUBN (cubilin) [144], MIOX (myo-inositol oxygenase) [145], FBP1 [146], ARG2 [147], FGF1 [148], CRY1 [149], PPARGC1A [150], UGT1A6 [151], ECHDC3 [152], CYP2C8 [153], ACOX2…”

Section: Discussionmentioning

confidence: 99%

“…Signaling pathways include neuronal system [90], GPCR ligand binding [91], metabolism [92] and metabolism of lipids [93] were responsible for progression of AKI. MYH7 [94], BMI1 [95], IGF2 [96], ADORA2A [97], KLK10 [98], MEIS2 [99], IRF7 [100], PRKCB (protein kinase C beta) [101], CCL5 [102], ADAM33 [103], EEF1A2 [104], ACTN3 [105], TRIM65 [106], FCN1 [107], CARNS1 [108], AMH (anti-Mullerian hormone) [109], E2F1 [110], PF4 [111], RPL3L [112], TRIM72 [113], HOXB4 [114], TP73 [115], KCNH2 [116], (advanced glycosylation end-product specific receptor) [117], SMPD3 [118], TYMP (thymidine phosphorylase) [119], RIPPLY3 [120], GREM1 [121], CYP11B2 [122], MYLK2 [123], WNT3A [124], MSX1 [125], COMP (cartilage oligomeric matrix protein) [126], FLI1 [127], ACTA1 [128], TCAP (titin-cap) [129], TUBB1 [130], TNNI3 [131], HSPB7 [132], DES (desmin) [133], RAP1B [134], TNNT1 [135], BHMT (betaine--homocysteine S-methyltransferase) [136], ANGPTL3 [137], CYP3A5 [138], KMO (kynurenine 3-monooxygenase) [139], HMGCS2 [140], AGXT2 [141], FABP1 [142], SLC22A12 [143], CUBN (cubilin) [144], MIOX (myo-inositol oxygenase) [145], FBP1 [146], ARG2 [147], FGF1 [148], CRY1 [149], PPARGC1A [150], UGT1A6 [151], ECHDC3 [152], CYP2C8 [153], ACOX2 [154], SLC2A9 [155], MSRA (methionine sulfoxidereductase A) [156], GC (GC vitamin D binding protein) [157], VNN1 [158], NOX4 [159], GOT2 [160], EPHX2 […”

Section: Discussionmentioning

confidence: 99%

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Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Acute kidney injury (AKI) is a type of renal disease occurs frequently in hospitalized patients, which may cause abnormal renal function and structure with increase in serum creatinine level with or without reduced urine output. With the incidence of AKI is increasing. However, the molecular mechanisms of AKI have not been elucidated. It is significant to further explore the molecular mechanisms of AKI. We downloaded the GSE139061 next generation sequencing (NGS) dataset from the Gene Expression Omnibus (GEO) database. Limma R bioconductor package was used to screen the differentially expressed genes (DEGs). Then, the enrichment analysis of DEGs in Gene Ontology (GO) function and REACTOME pathways was analyzed by g:Profiler. Next, the protein-protein interaction (PPI) network and modules was constructed and analyzed, and the hub genes were identified. Next, the miRNA-hub gene regulatory network and TF-hub gene regulatory network were built. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. Overall, 956 DEGs were identified, including 478 up regulated and 478 down regulated genes. The enrichment functions and pathways of DEGs involve primary metabolic process, small molecule metabolic process, striated muscle contraction and metabolism. Topological analysis of the PPI network and module revealed that hub genes, including PPP1CC, RPS2, MDFI, BMI1, RPL23A, VCAM1, ALB, SNCA, DPP4 and RPL26L1, might be involved in the development of AKI. miRNA-hub gene and TF-hub gene regulatory networks revealed that miRNAs and TFs including hsa-mir-510-3p, hsa-mir-6086 and mir-146a-5p, MAX and PAX2, might be involved in the development of AKI. Various known and newtherapeutic targets were obtained via network analysis. The results of the current investigation might be beneficial for the diagnosis and treatment of AKI.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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“…3 CAG and CCTA are effective methods for diagnosing CHD, but there are limitations to their application due to patient conditions, technical requirements, and cost issues. 8,9,20 Subsequently, the AHA/ACC and ESC proposed the DCS and UDFM models to calculate PTPs for guidance in CAG selection. Still, the above models overestimated the prevalence of CHD.…”

Section: Discussionmentioning

confidence: 99%

Clinical Value of the Diagonal Earlobe Crease in Patients with Chest Pain for Diagnosing Coronary Heart Disease

Gao,

Dou,

Yang

et al. 2024

IJGM

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To investigate the clinical application value of diagonal earlobe crease (DELC) in patients with chest pain for the diagnosis of coronary heart disease (CHD) and to construct a risk model by multivariate logistic regression. Patients and Methods: Our trial enrolled prospectively and consecutively 706 chest pain patients with suspected CHD between January 2021 to June 2023 from Chengde Central Hospital. According to coronary angiography results, they were categorized into the CHD (n=457) and non-CHD groups (n=249). Results:The trial demonstrated a significant positive relationship between DELC and CHD. Independent risk factors were sex, age, hypertension, diabetes mellitus, LP (a), Cys C, and DELC, whilst HDL-C was a protective factor, for CHD. Patients with-DELC were older than those in the without-DELC arm (P<0.001) and had a higher proportion of males than females (61.6% vs 50.0%, P=0.026).

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“…The expression of CCL18 [109], SLAMF7 [110], GPR174 [111], CCR4 [112], POU4F2 [113]. CCR2 [114], IL2RB [115], CCL4 [116], CCL24 [117], FASLG (Fas ligand) [118], CD24 [119], TDGF1 [120], CD28 [121], IL7R [122], CYP11B1 [123], CCL5 [124], CCL3 [125], LTF (lactotransferrin) [126], GPNMB (glycoprotein nmb) [127], CD209 [128], IL2RG [129], CHIT1 [130], TAB2 [131], CD163 [132], ALOX15B [133], NMRK2 [134], HGF (hepatocyte growth factor) [135], TRPM8 [136], DIO3 [137], SIGLEC1 [138], TTR (transthyretin) [139], IL24 [140], F13A1 [141], IL9 [142], VEGFA (vascular endothelial growth factor A) [143], RASAL1 [144], ADM (adrenomedullin) [145], ANGPTL4 [146], CHI3L1 [147], LDB3 [148], CNP (2’,3’-cyclic nucleotide 3’ phosphodiesterase) [149], HES6 [150], CMTM5 [151], PLXNB3 [152], KLK8 [153], CDKN1C [154], INSIG1 [155], GREM1 [156], ATF3 [157], HK2 [158], MCAM (melanoma cell adhesion molecule) [159], SEMA4D [160], GLUL (glutamate-ammonia ligase) [161], S1PR5 [162], FN3K [163], MEIS1 [164], ADAMTS4 [165], BIN1 [166], BMP2 [167], LMNA (lamin A/C) [168], ERBB3 [169], DLL1 [170], THBS2 [171], GADD45B [172], MYH6 [173]. PNPLA3 [174], ACTN2 [175], MMP15 [176], SVEP1 [177], CPB2 [178], DYSF (dysferlin) [179], ADAMTSL2 [180], NINJ2 [181], LRP2 [106], PHLDA3 […”

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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Plasma Exosomal S1PR5 and CARNS1 as Potential Non-invasive Screening Biomarkers of Coronary Heart Disease

Cited by 11 publications

References 31 publications

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Clinical Value of the Diagonal Earlobe Crease in Patients with Chest Pain for Diagnosing Coronary Heart Disease

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

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