<i>SORCS1</i>
            polymorphism and insulin secretion in obese women with polycystic ovary syndrome

Hrovat, Alenka; Kravos, Nika Aleksandra; Goričar, Katja; Sever, Mojca Jensterle; Dolžan, Vita

doi:10.3109/09513590.2015.1126818

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…In our work, SORCS1 were negatively regulated by mir-335 and CNGA3 was negatively regulated by mir-182 and mir-19b-2 (Fig 3). SORCS1 encodes sortilin related VPS10 domain containing receptor 1,is associated with the Alzheimer's disease, type 2 diabetes and obese women with polycystic ovary syndrome[38–40]. CNGA3 encodes cyclic nucleotide gated channel alpha 3, a member of cyclic nucleotide-gated cation channel protein family, is required for normal vision.…”

Section: Discussionmentioning

confidence: 99%

Abnormal expression of mRNA, microRNA alteration and aberrant DNA methylation patterns in rectal adenocarcinoma

Yang

Liu

et al. 2017

PLoS ONE

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AimRectal adenocarcinoma (READ) is a malignancy cancer with the high morbidity and motility worldwide. Our study aimed to explore the potential pathogenesis of READ through integrated analysis of gene expression profiling and DNA methylation data.MethodsThe miRNA, mRNA expression profiling and corresponding DNA methylation data were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs/ miRNAs/methylated regions (DEmRNA/DEmiRNAs) were identified in READ. The negatively correlation of DEmiRNA-DEmRNAs and DNA methylation-DEmRNAs were obtained. DEmRNAs expression was validated through quantitative real-time polymerase chain reaction (qRT-PCR) and microarray expression profiling analyses.Results1192 dysregulated DEmRNAs, 27 dysregulated DEmiRNAs and 6403 aberrant methylation CpG sites were screened in READ compared to normal controls. 1987 negative interaction pairs among 27 DEmiRNAs and 668 DEmRNAs were predicted. 446 genes with aberrant methylation were annotated. Eventually, 50 DEmRNAs (39 down- and 11 up-regulated DEmRNAs) with hypermethylation, synchronously negatively targeted by DEmiRNAs, were identified through the correlation analysis among 446 genes with aberrant methylation and 668 DEmRNAs. 50 DEmRNAs were significantly enriched in cAMP signaling pathway, circadian entrainment and glutamatergic synapse. The validation results of expression levels of DEmRNAs through qRT-PCR and microarray analyses were compatible with our study.Conclusion7 genes of SORCS1, PDZRN4, LONRF2, CNGA3, HAND2, RSPO2 and GNAO1 with hypermethylation and negatively regulation by DEmiRNAs might contribute to the tumorigenesis of READ. Our work might provide valuable foundation for the READ in mechanism elucidation, early diagnosis and therapeutic target identification.

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Section: Discussionmentioning

confidence: 99%

Abnormal expression of mRNA, microRNA alteration and aberrant DNA methylation patterns in rectal adenocarcinoma

Yang

Liu

et al. 2017

PLoS ONE

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“…VCAM1 [1232], AQP8 [1233], FABP4 [1234], FABP5 [1235], BMP4 [1236], PRL (prolactin) [960], WNT5A [1237], ADAMTS9 [1238], NDNF (neuron derived neurotrophic factor) [1239], LHCGR (luteinizing hormone/choriogonadotropin receptor) [1240], LDLR (low density lipoprotein receptor) [1241], CD4 [1242], ADAMTS5 [1243], MAP2K6 [1244], ADAMTS1 [1245], PDE4B [1246], TH (tyrosine hydroxylase) [1247], MMP8 [1248], ADRB2 [969], KL (klotho) [1249], EPHA7 [1250], UCN2 [1251], CYP1A1 [970], LEPR (leptin receptor) [1252], IL15 [1253], BMP2 [1254], APOE (apolipoprotein E) [1255], CASP1 [1256], ACE (angiotensin I converting enzyme) [1257], PGR (progesterone receptor) [1258], GREM2 [1259], SORCS1 [1260], HKDC1 [1261], FADS1 [1262], S100A4 [1263], IL33 [1243], NGF (nerve growth factor) [1264], COMP (cartilage oligomeric matrix protein) [1265], FST (follistatin) [1266], GATA6 [1267], ACAN (aggrecan) [1268], AKR1C3 [1269], BDNF (brain derived neurotrophic factor) [1270], ANGPTL4 [1271], NOX4 [1272], VDR (vitamin D receptor) [1273], GPC4 [1274], TLR2 [1275], IL6 [1276], IGFBP3 [1277], TNIK (TRAF2 and NCK interacting kinase) [1278], APLN (apelin) [1279], PGF (placental growth factor) [1280], NRG1 [1281], LIF (LIF interleukin 6 family cytokine) [1282], ANGPTL1 [1283], KISS1 [1284], SORBS1 [1285] and TRH (thyrotropin releasing hormone) [1286] are a potential targets for polycystic ovarian syndrome. VCAM1 [1287], STRA6 [1288], AQP8 [1289], FABP4 [1290], SOX6 [1291], RAMP3 [1292], MMP12 [1293], FAIM2 […”

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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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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“…Özcan et al [526], Emokpae et al [527], Batista et al [528], Darbari et al [529], ElAlfy et al [530], Hounkpe et al [531], Vogel et al [532], Afifi et al [533], Shmukler et al [534], El Sissy et al [535], Cavalcante et al [536], Sadler et al [537], Kalai et al [538], Silva et al [539], Jhun et al [540] and Cai et al [541] that ALB (albumin), LPL (lipoprotein lipase), KL (klotho), UGT2B7, IFNG (interferon gamma), MPO (myeloperoxidase), BTK (Bruton tyrosine kinase), CD209, KCNN4, CCR5, TNF (tumor necrosis factor), CCR2, CCL5, NOS3, S100B and HBB (hemoglobin subunit beta) are associated with progression of sickle cell disease. Studies showed that altered expression of MTNR1A [542], GATA6 [543], EGF (epidermal growth factor) [544], LPL (lipoprotein lipase) [545], PPARGC1A [546], ERBB4 [547], KL (klotho) [548], GCLC (glutamate-cysteine ligase catalytic subunit) [549], NOX4 [550], SORCS1 [551], FKBP5 [552], CCNL1 [553], USP25 [554], SAA1 [555], MPO (myeloperoxidase) [556], GATA1 [557], LCN2 [558], IL1RN [559], IL11 [560], PDCD1 [561], TNF (tumor necrosis factor) [562], TNFRSF1B [563], APLNR (apelin receptor) [564], COMP (cartilage oligomeric matrix protein) [565], RETN (resistin) [566] and IGFBP1 [567] were associated with the progression of polycystic ovarian syndrome. Combined with the results of GO and REACTOME pathway enrichment analysis, these results imply these genes might participate in FSGS.…”

Section: Discussionmentioning

confidence: 99%

Identification of biomarkers and pathways for focal segmental glomerulosclerosis using next generation sequencing data and bioinformatics analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Focal segmental glomerulosclerosis (FSGS) is a renal disease leading threat to human health around the world. Here we aimed to explore novel molecular and potential therapeutic targets in FSGS through adopting integrated bioinformatics tools. Next generation sequencing (NGS) data of GSE197307 was available from Gene Expression Omnibus (GEO) database. Furthermore, differentially expressed genes (DEGs) were screened using the DESeq2 package in R software. Gene ontology (GO) and REACTOME pathway enrichment analyses of DEGs were performed via g:Profiler. Then, the protein-protein interaction (PPI) network, miRNA- hub gene regulatory network and TF-hub gene regulatory network were constructed using the HIPPIE, miRNet and NetworkAnalyst databases. Hub genes were validated using the receiver operating characteristic curve (ROC) analysis. By performing DEGs analysis, 488 up regulated genes and 488 down regulated genes were successfully identified from GSE197307, respectively. And they were mainly enriched in the terms of multicellular organismal process, cell periphery, protein binding, metabolism, immune system process, signaling receptor binding and immune system. Based on the data of protein-protein interaction (PPI), miRNA-hub gene regulatory network and TF-hub gene regulatory network, the top hub genes were ranked, including ILK, DDX5, MATR3, ALB, FOS, MKI67, PLK1, TNF, LCK and GTSE1. Bioinformatics analysis of NGS data identified signaling pathways and hub genes, potentially representing molecular mechanisms for the occurrence, progression, and risk prediction in FSGS.

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SORCS1 polymorphism and insulin secretion in obese women with polycystic ovary syndrome

Cited by 8 publications

References 16 publications

Abnormal expression of mRNA, microRNA alteration and aberrant DNA methylation patterns in rectal adenocarcinoma

Abnormal expression of mRNA, microRNA alteration and aberrant DNA methylation patterns in rectal adenocarcinoma

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

Identification of biomarkers and pathways for focal segmental glomerulosclerosis using next generation sequencing data and bioinformatics analysis

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