LncRNA AC005332.7 Inhibited Ferroptosis to Alleviate Acute Myocardial Infarction Through Regulating miR-331-3p/CCND2 Axis

Dai, Rixin; Yang, Xiheng; He, Wujin; Su, Qiang; Deng, Xuexin; Li, Juanfen

doi:10.4070/kcj.2022.0242

Cited by 10 publications

(9 citation statements)

References 29 publications

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“…ROBO2 [760], VCAM1 [761], GRP (gastrin releasing peptide) [762], FABP4 [763], ANO1 [764], SOX6 [765], TFAP2C [766], RAMP3 [767], PLA2G7 [768], MMP12 [769], FAIM2 [770], APOD (apolipoprotein D) [771], LAG3 [772], SOX18 [773], F2RL2 [774], CCR1 [775], FLT1 [776], FABP5 [629], TRPC3 [777]. THSD7A [778], DKK2 [779], PRKCB (protein kinase C beta) [780], DHCR24 [781], PDE3B [782], BMP4 [783], IL1RL1 [784], MYPN (myopalladin) [785], PLCG2 [786], PRL (prolactin) [787], WNT5A [788], MEOX1 [789], TIMP3 [790], FRZB (frizzled related protein) [791], CPE (carboxypeptidase E) [792], ADAMTS9 [793], NDNF (neuron derived neurotrophic factor) [794], PDGFB (platelet derived growth factor subunit B) [795], PIK3CG [796], LDLR (low density lipoprotein receptor) [797], CD4 [798], TRPA1 [799], F2RL3 [800], C1QL1 [801], ADAMTS5 [802], PDE4B [803], NES (nestin) [804], TH (tyrosine hydroxylase) [805], MMP8 [806], KDR (kinase insert domain receptor) [807], ADRB2 [808], ACKR3 [809], PTPRC (protein tyrosine phosphatase receptor type C) [810], KL (klotho) [811], KL (klotho) [812], PLAU (plasminogen activator, urokinase) [813], CCND2 [814], PTGS1 [815], INSIG1 [816], IRX2 [817], SIGLEC1 [818], UCN2 [819], CYP2J2 [820], CYP1A1 [821], ASTN2 [822], NTN1 [823], PDGFD (platelet derived growth factor D) [824], MSTN (myostatin) [663], LEPR (leptin receptor) [664], IL15 [825], CACNA1H [826], BMP2 [827], SYT7 [<...…”

Section: Discussionmentioning

confidence: 99%

“…CCND2 [511], VCAM1 [478], PTCH1 [523], FOXQ1 [492], IGF2BP1 [568], EPB41L3 [560], DAPK1 [609], hsa-mir-17-5p [1669], TCF3 [1670] and TRIM28 [1671] as a biomarkers for cervical cancer. CCND2 [814], VCAM1 [615], PDGFB (platelet derived growth factor subunit B) [795], PRKCB (protein kinase C beta) [780], ACTC1 [924], DAPK1 [947], hsa-mir-17-5p [1672], hsa-mir-2110 [1673], TCF3 [1674] and SMARCA4 [1675] are associated with developing cardiovascular diseases. CCND2 [1083], VCAM1 [761], PDGFB (platelet derived growth factor subunit B) [1059], PTCH1 [1098], FOXQ1 [1044], IGF2BP1 [1162], ACTC1 [1192], DAPK1 [947], hsa-mir-2110 [1676], hsa-mir-10b-5p [1677], TCF3 [1678], NR1I2 [1679] and TRIM28 [1680] have been shown to be associated with inflammation.…”

Section: Discussionmentioning

confidence: 99%

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Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis. Next generation sequencing (NGS) dataset GSE243039 was obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein-protein interaction (PPI) network was constructed and modules were analysed using the Human Integrated Protein-Protein Interaction rEference (HIPIE) database and Cytoscape software, and hub genes were identified. Subsequantely, a network between miRNAs and hub genes, and network between TFss and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve (ROC) analysis was used to validate the hub genes. A total of 958 DEGs, including 479 up regulated genes and 479 down regulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including VCAM1, SNCA, PRKCB, ADRB2, FOXQ1, MDFI, ACTBL2, PRKD1, DAPK1 and ACTC1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including TCF3 and CLOCK, were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network. This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment, and monitoring of endometriosis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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“…Signaling pathways include signaling by GPCR [56] and extracellular matrix organization [57] were responsible for advancement of HD. Research has shown that CBLN2 [58], MOG (myelin oligodendrocyte glycoprotein) [59], ROS1 [60], LGR6 [61], GJA5 [62], SUCNR1 [63], REN (renin) [64], VEGFC (vascular endothelial growth factor C) [65], TLR2 [66], FGF12 [67], WT1 [68], SLC22A3 [69], CSRP3 [70], PAPPA2 [71], ACAN (aggrecan) [72], SHH (sonic hedgehog signaling molecule) [73], PDC (phosducin) [74], AGTR1 [75], XDH (xanthine dehydrogenase) [76], P2RX7 [77], NTSR1 [78], SIRT2 [79], RBP4 [80], BMP2 [81], ERBB3 [82], HAS2 [83], CDH13 [84], CSMD1 [85], NR3C2 [86], EPAS1 [87], TRPC6 [88], MMP3 [89], ERAP2 [90], HLA-DRB1 [91], CD74 [92], VWF (von Willebrand factor) [93], 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], protein) [94], NEDD4L [95], CASP1 [96], BRCA2 [97], EFNB2 [98], PLCE1 [99], EGFR (epidermal growth factor receptor) [100], ABCA1 [101], CRHR2 [102], RND3 [103], COMT (catechol-O-methyltransferase) [104] and SMAD9 [105] plays an important role in the pathogenesis of hypertension. Studies have revealed that NEUROD4 [106], SOX10 [107], MOG (myelin oligodendrocyte glycoprotein) [108], NR5A2 [109], GLRA3 [110], FA2H [111], SERPINA […”

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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“…Recently, lncRNA AC005332.7 was found to impede cardiomyocyte ferroptosis via inhibiting lipid peroxidation in both in vitro and in vivo models. 4) …”

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confidence: 99%

Cardiomyocyte Autophagy: A Novel Therapeutic Target by LncRNA PART1

Kim

2024

Korean Circ J

View full text Add to dashboard Cite

LncRNA AC005332.7 Inhibited Ferroptosis to Alleviate Acute Myocardial Infarction Through Regulating miR-331-3p/CCND2 Axis

Cited by 10 publications

References 29 publications

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Screening and identification of key biomarkers associated with endometriosis using 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

Cardiomyocyte Autophagy: A Novel Therapeutic Target by LncRNA PART1

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