The SELP, CD93, IL2RG, and VAV1 Genes Associated with Atherosclerosis May Be Potential Diagnostic Biomarkers for Psoriasis

Liu, Shougang; Liu, Fanghua; Zhang, Zeqiao; Zhuang, Zhe; Yuan, Xiuqing; Chen, Yongfeng

doi:10.2147/jir.s398862

Cited by 5 publications

(3 citation statements)

References 67 publications

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“…Altered expression of FA2H [441], DSCAM (DS cell adhesion molecule) [442], OLIG2 [328], PCDH15 [443], GRID2 [424], CTTNBP2 [444], AFF2 [445], SYNE2 [446] and EGFR (epidermal growth factor receptor) [447] are associated with autism spectrum disorder. FA2H [111], SERPINA1 [448], HOXB9 [113], TGM2 [449], LRRK2 [450], ROS1 [451], CFC1 [452], GDF3 [453], TF (transferrin) [121], RXFP1 [454], RELN (reelin) [455], HTR2C [456], MYL7 [457], DYSF (dysferlin) [458], GJA5 [459], IRX1 [460], TLL1 [461], SUCNR1 [462], KERA (keratocan) [463], TFAP2B [464], HOXA5 [465], ACSL6 [466], DSCAM (DS cell adhesion molecule) [467], MSR1 [468], REN (renin) [64], VEGFC (vascular endothelial growth factor C) [469], TLR2 [470], PCSK2 [471], FGF12 [472], SLC22A3 [142], HSPB7 [473], CSRP3 [474], KLRD1 [475], PTPRO (protein tyrosine phosphatase receptor type O) [476], IL7R [477], CUX2 [478], ACAN (aggrecan) [479], SHH (sonic hedgehog signaling molecule) [480], MEOX1 [481], AGTR1 [482], VTN (vitronectin) [483], SIRT2 [79], RBP4 [484], IL2RG [485], EPAS1 [486], CDH13 [487], TRPC6 [488], MMP3 [261], PCDHGA3 [489], FGF19 [490], TBX18 [491], HLA-DRB1 [492]. 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 […”

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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“…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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“…Macrophages are generally divided into two different subgroups named inflammatory M1 and anti‐inflammatory M2 macrophages, and the polarization of macrophages is extremely crucial to lymphocyte activation and proliferation, facilitating the inflammatory process in psoriasis. 36 , 37 Studies reported that multiple molecules and drugs, such as PSORI‐CM02, Shikonin combined with methotrexate have therapeutic effects on psoriasis by modulating the polarization of macrophages. 38 , 39 Results revealed by Gong et al 40 and Su et al 41 indicated that macrophages M0/M1 and T cells CD4 memory activated were significantly increased in psoriatic skin, while mast cells resting was significantly decreased.…”

Section: Discussionmentioning

confidence: 99%

Exploration of the biomarkers of comorbidity of psoriasis with inflammatory bowel disease and their association with immune infiltration

Ding,

Zheng,

2023

Skin Research and Technology

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BackgroundThere was evidence that significant bidirectional associations between psoriasis and inflammatory bowel diseases (IBDs), which influences management strategy of the patients, so the investigation on the mechanisms by which these two diseases co‐occur is important.MethodsThe Gene Expression Omnibus (GEO) database was used to download gene expression profiles of psoriasis and IBD. The differentially expressed genes (DEGs) between disease and health control groups for each data set were calculated, and Venn diagram was used to obtain for intersection. We performed Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the intersection, followed by developing a protein–protein interaction network and module construction, and identified hub genes by cytoHubba. Thereafter, least absolute shrinkage and selection operator algorithms was used to identify the co‐biomarkers of psoriasis and IBD from the top 50 hub genes. The biomarkers were used to construct a screening model, the discriminatory capacity of which was verified by receiver operating characteristic (ROC) curves. CIBERSORT algorithm was utilized to estimate the compositional patterns of immune cell infiltration in biomarkers of psoriasis and IBD. Spearman rank correlation analysis was used to further evaluate the correlation between the identified biomarkers and immune cells.ResultsA total of 271 shared DEGs were screened. The GO and KEGG enrichment analysis indicated that the shared DEGs were mainly enriched in response to lipopolysaccharide, secretory granule lumen, cytokine activity, and interleukin (IL)‐17 signaling pathway. Fifty genes such as IL1B, IL6, were identified as hub genes, based on which, FOS, IFI44, MMP9, MNDA, PTGS2, S100A9, and STAT1 were identified as biomarkers of psoriasis. CCL20, CD274, CTGF, CXCL1, CXCL10, CXCL2, CXCL9, FCGR3B, FOS, GBP1, GZMB, IFI27, IFI6, IL1RN, ISG15, ISG20, LCN2, LILRB2, MMP12, MMP7, S100A8, TLR8, and TNFSF13B were identified as biomarkers of IBD. FOS was the common biomarker of psoriasis and IBD. Screening models were validated in the validation data set (Psoriasis: area under the curve (AUC) = 1.000, IBD: AUC = 0.870). Immunocyte infiltration analysis showed the macrophages cells, mast cells resting, and T cells CD4 memory activated have the common characteristics in psoriasis and IBD.ConclusionsFOS may play a key role in the occurrence and development of psoriasis complicated with IBD and macrophages cells may be an entrance for treating this comorbidity.

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The SELP, CD93, IL2RG, and VAV1 Genes Associated with Atherosclerosis May Be Potential Diagnostic Biomarkers for Psoriasis

Cited by 5 publications

References 67 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

Exploration of the biomarkers of comorbidity of psoriasis with inflammatory bowel disease and their association with immune infiltration

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