The impact of SELP gene Thr715Pro polymorphism on sP-selectin level and association with cardiovascular disease in Saudi diabetic patients: A cross-sectional case-control study

Alzahrani, Faisal; Alhassan, Jinan A.; Alshehri, Abdullah; Farooqi, Faraz Ahmed; Aldossary, Maryam A.; Abdelghany, Magdy K.; Ibrahim, Hafiz; El‐Masry, Omar S.

doi:10.1016/j.sjbs.2023.103579

Cited by 3 publications

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Identification of key genes and biological pathways in chronic obstructive pulmonary disease using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

View full text Add to dashboard Cite

Identification of accurate biomarkers is still particularly urgent for improving the poor survival of chronic obstructive pulmonary disease (COPD) patients. In this investigation, we aimed to identity the potential biomarkers in COPD via bioinformatics and next generation sequencing (NGS) data analysis. In this investigation, the differentially expressed genes (DEGs) in COPD were identified using NGS dataset (GSE239897) from Gene Expression Omnibus (GEO) database. Subsequently, gene ontology (GO) and pathway enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of COPD. Protein-protein interaction (PPI), modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to determine the hub genes, miRNAs and TFs. The receiver operating characteristic (ROC) analysis was performed to determine the diagnostic value of hub genes. A total of 956 overlapping DEGs (478 up regulated and 478 down regulated genes) were identified in the NGS dataset. DEGs were mainly associated with GO functional terms and pathways in cellular response to stimulus. response to stimulus, immune system and neutrophil degranulation. There were 10 hub genes (MYC, LMNA, VCAM1, MAPK6, DDX3X, SHMT2, PHGDH, S100A9, FKBP5 and RPS6KA2) identified by PPI, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis. In conclusion, the DEGs, relative GO terms, pathways and hub genes identified in the present investigation might aid in understanding of the molecular mechanisms underlying COPD progression and provide potential molecular targets and biomarkers for COPD.

show abstract

Identification of key genes and biological pathways in chronic obstructive pulmonary disease using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

View full text Add to dashboard Cite

show abstract

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

View full text Add to dashboard Cite

Acute kidney injury (AKI) is a type of renal disease occurs frequently in hospitalized patients, which may cause abnormal renal function and structure with increase in serum creatinine level with or without reduced urine output. With the incidence of AKI is increasing. However, the molecular mechanisms of AKI have not been elucidated. It is significant to further explore the molecular mechanisms of AKI. We downloaded the GSE139061 next generation sequencing (NGS) dataset from the Gene Expression Omnibus (GEO) database. Limma R bioconductor package was used to screen the differentially expressed genes (DEGs). Then, the enrichment analysis of DEGs in Gene Ontology (GO) function and REACTOME pathways was analyzed by g:Profiler. Next, the protein-protein interaction (PPI) network and modules was constructed and analyzed, and the hub genes were identified. Next, the miRNA-hub gene regulatory network and TF-hub gene regulatory network were built. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. Overall, 956 DEGs were identified, including 478 up regulated and 478 down regulated genes. The enrichment functions and pathways of DEGs involve primary metabolic process, small molecule metabolic process, striated muscle contraction and metabolism. Topological analysis of the PPI network and module revealed that hub genes, including PPP1CC, RPS2, MDFI, BMI1, RPL23A, VCAM1, ALB, SNCA, DPP4 and RPL26L1, might be involved in the development of AKI. miRNA-hub gene and TF-hub gene regulatory networks revealed that miRNAs and TFs including hsa-mir-510-3p, hsa-mir-6086 and mir-146a-5p, MAX and PAX2, might be involved in the development of AKI. Various known and newtherapeutic targets were obtained via network analysis. The results of the current investigation might be beneficial for the diagnosis and treatment of AKI.

show abstract

Association of serine racemase gene polymorphism with type 2 diabetes mellitus

Al-Nbaheen

2024

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by insulin resistance and β-cell dysfunction, with a significant global impact. Genome-wide association studies (GWAS) have identified several genetic polymorphisms linked to T2DM, including the rs391300 polymorphism in the SRR gene. This study aimed to evaluate the association between the rs391300 polymorphism and T2DM in the Saudi population. A total of 160 participants, comprising 80 T2DM patients and 80 healthy controls, were genotyped using quantitative PCR with VIC and FAM probes. The results revealed a significant association between T2DM and age, body mass index (BMI), glucose levels, and cholesterol levels. Genotype and allele frequency analysis demonstrated that the rs391300 polymorphism was linked to a higher risk of T2DM (GA vs. AA: OR = 4.75, 95% CI: 1.52–14.94, p = 0.04; A vs. G: OR = 4.33, 95% CI: 1.42–13.27, p = 0.005). Additionally, ANOVA analysis indicated a significant association with weight and BMI (p = 0.01). This study provides evidence of a positive association between the rs391300 polymorphism in the SRR gene and T2DM in the Saudi population.

show abstract

The impact of SELP gene Thr715Pro polymorphism on sP-selectin level and association with cardiovascular disease in Saudi diabetic patients: A cross-sectional case-control study

Cited by 3 publications

References 40 publications

Identification of key genes and biological pathways in chronic obstructive pulmonary disease using bioinformatics and next generation sequencing data analysis

Identification of key genes and biological pathways in chronic obstructive pulmonary disease using bioinformatics and next generation sequencing data analysis

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Association of serine racemase gene polymorphism with type 2 diabetes mellitus

Contact Info

Product

Resources

About