Novel Haplotype Indicator for End-Stage Renal Disease Progression among Saudi Patients

Cyrus, Cyril; Chathoth, Shahanas; Vatte, Chittibabu; AlRubaish, Nafie A.; Almuhanna, Othman; Borgio, J. Francis; Al-Mueilo, Samir; Muhanna, Fahd A. Al; Ali, Amein K. Al

doi:10.1155/2019/1095215

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…In normal kidney cells, CST3 (or cystatin C) was mainly found to be expressed in vascular smooth muscle cells and mesangial cells. It mapped in the genome near polymorphisms associated with an increased risk of developing end-stage renal disease, possibly followed by FSGS complications (49). The link between the expression of fibronectin 1, the TGF-beta signaling pathway, and chronic progressive kidney disease (50) was confirmed as being restricted to the vascular smooth muscle cells and mesangial cell subpopulation.…”

Section: Online Results Visualization and The Development Of An Interamentioning

confidence: 95%

Text Mining Gene Selection to Understand Pathological Phenotype Using Biological Big Data

2021

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Whole transcriptome omics experiments allow for the study of gene regulation at the cellular level. During analysis and interpretation of omics data, false discovery can occur. To minimize false discovery and identify true significant cases, multi-test correction has been introduced to bioinformatics algorithms. The scientific literature offers a huge collection of information that can be parsed using a web Application Programming Interface. Gene selection by text mining can rank information according to its importance while taking into account the most recent updates in scientific literature. The integration of text mining selection in biological big data, such as transcriptome experiments including single cell transcriptome, can achieve an important dimensional reduction of the data without any statistical hypothesis. This avoids false discoveries regarding the molecules of interest. Hydatidiform moles and focal segmental glomerulosclerosis (FSGS) nephropathy are the two examples presented in this chapter, which demonstrate the considerable value of these analytical methods to prove the concept. The best FSGS markers expressed can be displayed by building an interactive online web interface as a web resource based on the glomerular cell transcriptome.

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Section: Online Results Visualization and The Development Of An Interamentioning

confidence: 95%

Text Mining Gene Selection to Understand Pathological Phenotype Using Biological Big Data

2021

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Diabetic kidney disease: Are the reported associations with single-nucleotide polymorphisms disease-specific?

Saracyn¹,

Kisiel²,

Franaszczyk³

et al. 2021

WJD

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BACKGROUND The genetic backgrounds of diabetic kidney disease (DKD) and end-stage kidney disease (ESKD) have not been fully elucidated. AIM To examine the individual and cumulative effects of single-nucleotide polymorphisms (SNPs) previously associated with DKD on the risk for ESKD of diabetic etiology and to determine if any associations observed were specific for DKD. METHODS Fourteen SNPs were genotyped in hemodialyzed 136 patients with diabetic ESKD (DKD group) and 121 patients with non-diabetic ESKD (NDKD group). Patients were also re-classified on the basis of the primary cause of chronic kidney disease (CKD). The distribution of alleles was compared between diabetic and non-diabetic groups as well as between different sub-phenotypes. The weighted multilocus genetic risk score (GRS) was calculated to estimate the cumulative risk conferred by all SNPs. The GRS distribution was then compared between the DKD and NDKD groups as well as in the groups according to the primary cause of CKD. RESULTS One SNP (rs841853; SLC2A1) showed a nominal association with DKD ( P = 0.048; P > 0.05 after Bonferroni correction). The GRS was higher in the DKD group (0.615 ± 0.260) than in the NDKD group (0.590 ± 0.253), but the difference was not significant ( P = 0.46). The analysis of associations between GRS and individual factors did not show any significant correlation. However, the GRS was significantly higher in patients with glomerular disease than in those with tubulointerstitial disease ( P = 0.014) and in those with a combined group (tubulointerstitial, vascular, and cystic and congenital disease) ( P = 0.018). CONCLUSION Our results suggest that selected SNPs that were previously associated with DKD may not be specific for DKD and may confer risk for CKD of different etiology, particularly those affecting renal glomeruli.

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Novel Haplotype Indicator for End-Stage Renal Disease Progression among Saudi Patients

Cited by 2 publications

References 32 publications

Text Mining Gene Selection to Understand Pathological Phenotype Using Biological Big Data

Text Mining Gene Selection to Understand Pathological Phenotype Using Biological Big Data

Diabetic kidney disease: Are the reported associations with single-nucleotide polymorphisms disease-specific?

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