Overexpression of <i>SNTG2, TRAF3IP2,</i> and <i>ITGA6</i> transcripts is associated with osteoporotic vertebral fracture in elderly women from community

Neto, Levi H. Jales; Wicik, Zofia; Torres, Geórgea Hermógenes Fernandes; Takayama, Liliam; Caparbo, Valéria F.; Lopes, Neuza; Pereira, Alexandre C.; Pereira, Rosa Maria Rodrigues

doi:10.1002/mgg3.1391

Cited by 10 publications

(8 citation statements)

References 49 publications

(47 reference statements)

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“…The SNTG2 protein binds to components of mechanosensitive sodium channels and to the C termini of dystrophin, α-dystrobrevin, and β-dystrobrevin [ 52 ]. The SNTG2 gene is expressed in various tissues in humans and was reported to be associated with osteoporotic vertebral fracture [ 53 ] and autism [ 54 ]. These give the gene an unlikely role in the development of TC.…”

Section: Discussionmentioning

confidence: 99%

Deleterious Mutations in the TPO Gene Associated with Familial Thyroid Follicular Cell Carcinoma in Dutch German Longhaired Pointers

Bovenhuis

et al. 2021

Genes

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Familial thyroid cancer originating from follicular cells accounts for 5–15% of all the thyroid carcinoma cases in humans. Previously, we described thyroid follicular cell carcinomas in a large number of the Dutch German longhaired pointers (GLPs) with a likely autosomal recessive inheritance pattern. Here, we investigated the genetic causes of the disease using a combined approach of genome-wide association study and runs of homozygosity (ROH) analysis based on 170k SNP array genotype data and whole-genome sequences. A region 0–5 Mb on chromosome 17 was identified to be associated with the disease. Whole-genome sequencing revealed many mutations fitting the recessive inheritance pattern in this region including two deleterious mutations in the TPO gene, chr17:800788G>A (686F>V) and chr17:805276C>T (845T>M). These two SNP were subsequently genotyped in 186 GLPs (59 affected and 127 unaffected) and confirmed to be highly associated with the disease. The recessive genotypes had higher relative risks of 16.94 and 16.64 compared to homozygous genotypes for the reference alleles, respectively. This study provides novel insight into the genetic causes leading to the familial thyroid follicular cell carcinoma, and we were able to develop a genetic test to screen susceptible dogs.

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Section: Discussionmentioning

confidence: 99%

Deleterious Mutations in the TPO Gene Associated with Familial Thyroid Follicular Cell Carcinoma in Dutch German Longhaired Pointers

Bovenhuis

et al. 2021

Genes

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“…The subjects of this study were selected from the population-based survey (São Paulo Ageing and Health [SPAH] study) followed by the bone metabolism outpatient clinic of the Faculty of Medicine of the University of Sao Paulo (FMUSP). Transcriptome analysis of a total of 90 elderly women from this population were performed and described in our previous studies [ 33 , 34 ] using microarray technology. From this microarray dataset publicly deposited in the GEO NCBI database under the accession number GSE152073, we selected 40 elderly women, of which 20 were with LMM (defined by a Newman's residual <−1.32) and 20 age and race-matched controls (residual >−1.32).…”

Section: Methodsmentioning

confidence: 99%

“…Microarray dataset was extracted from the repository São Paulo Aging and Health Study (SPAH), our previously published database [ 33 , 34 ]. Raw microarray datasets were deposited in GEO NCBI repository at the accession number GSE152073.…”

Section: Methodsmentioning

confidence: 99%

“…Then probe sets were annotated using biomaRt package [ 38 ] in R environment. Considering the binary variable Low of Muscle Mass (LMM), we adjusted a logistic regression model with age as a covariate in R to discriminate genes that affect this dependent variable [ 34 ]. Genes with regression coefficients β ≥ 1 or β ≤ −1 and p ≤ 0.01 were considered significant.…”

Section: Methodsmentioning

confidence: 99%

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Transcriptomic analysis of elderly women with low muscle mass: association with immune system pathway

Neto

Hounkpe

Fernandes

et al. 2021

Aging

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Despite the well-established association of gene expression deregulation with low muscle mass (LMM), the associated biological mechanisms remain unclear. Transcriptomic studies are capable to identify key mediators in complex diseases. We aimed to identify relevant mediators and biological mechanisms associated with age-related LMM. LMM-associated genes were detected by logistic regression using microarray data of 20 elderly women with LMM and 20 age and race-matched controls extracted from our SPAH Study (GSE152073). We performed weighted gene co-expression analysis (WGCNA) that correlated the identified gene modules with laboratorial characteristics. Gene enrichment analysis was performed and an LMM predictive model was constructed using Support Vector Machine (SVM). Overall, 821 discriminating transcripts clusters were identified (|beta coefficient| >1; p -value <0.01). From this list, 45 predictors of LMM were detected by SVM and validated with 0.7 of accuracy. Our results revealed that the well-described association of inflammation, immunity and metabolic alterations is also relevant at transcriptomic level. WGCNA highlighted a correlation of genes modules involved in immunity pathways with vitamin D level (R = 0.63, p = 0.004) and the Agatston score (R = 0.51, p = 0.02). Our study generated a predicted regulatory network and revealed significant metabolic pathways related to aging processes, showing key mediators that warrant further investigation.

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“…Therefore, in this work, a variety of public gene expression datasets based on peripheral blood samples containing metabolic syndrome-associated clinical diagnosis information were collected from the Gene Expression Omnibus database (GEO database, http://www.ncbi.nlm.nih.gov/geo/). Training set of this study consisted of randomly selected 70% of the samples in GSE152073 (n = 90) and GSE98895 (n = 40) combined dataset (gene expression microarray data of peripheral blood), and the remaining 30% was used as internal validation data 27,28 . GSE124534 (n = 17, gene expression microarray data of peripheral blood) was used to achieve the external validation 29 .…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

Identification of Novel Biomarkers for Metabolic Syndrome Based on Machine Learning Algorithms and Integrated Bioinformatics Analysis

Liu¹,

Chen²,

Kong³

et al. 2021

Preprint

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Background: Metabolic syndrome is a common and complicated metabolic disorder and defined as a clustering of metabolic risk factors such as insulin resistance or diabetes, obesity, hypertension, and hyperlipidemia. However, its early diagnosis is limited because the lack of definitive clinical diagnostic biomarkers. In present study, we aim to select several candidate gene as a blood-based clinically applicable transcriptomics signature for metabolic syndrome. Method: We collected so far the largest MetS-associated peripheral blood high-throughput transcriptomics data and put forward a novel feature selection strategy by combining weighted gene co-expression network analysis, protein-protein interaction network analysis, LASSO regression and random forest approaches. Then, based on selected hub gene signature, we performed logistic regression analysis and subsequently established a web nomogram calculator for metabolic syndrome risk to detect the diagnostic value of this hub gene signature. Finally, Receiver Operating Characteristic curve analysis, calibration curve analysis, Hosmer-Lemeshow good of fit test and decision curve analysis showed the classification and calibration performance as well as potential clinical benefit of this hub gene signature. Results: Through weighted gene co-expression network analysis, protein-protein interaction network analysis, we identified 2 gene modules and 51 hub genes associated with metabolic syndrome. Then, we subsequently performed further feature selection via LASSO regression and random forest method. Finally, a 9-hub-gene signature with high diagnostic value and a web nomogram calculator for metabolic syndrome risk (https://xjtulgz.shinyapps.io/DynNomapp/) were developed. This 9-hub-gene signature showed excellent classification and calibration performance (AUC= 0.968 in training set, AUC= 0.883 in internal validation set, AUC= 0.861 in external validation set) as well as ideal potential clinical benefit. Conclusions: The blood-based 9-hub-gene signature identified in present study and the web nomogram calculator for metabolic syndrome risk are possible to accurately achieve the noninvasive screening or diagnosis of MetS considering the excellent classification ability, calibration and potential clinical benefits.

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Overexpression of SNTG2, TRAF3IP2, and ITGA6 transcripts is associated with osteoporotic vertebral fracture in elderly women from community

Cited by 10 publications

References 49 publications

Deleterious Mutations in the TPO Gene Associated with Familial Thyroid Follicular Cell Carcinoma in Dutch German Longhaired Pointers

Deleterious Mutations in the TPO Gene Associated with Familial Thyroid Follicular Cell Carcinoma in Dutch German Longhaired Pointers

Transcriptomic analysis of elderly women with low muscle mass: association with immune system pathway

Identification of Novel Biomarkers for Metabolic Syndrome Based on Machine Learning Algorithms and Integrated Bioinformatics Analysis

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