Evidence from genetic studies among rs2107538 variant in the CCL5 gene and Saudi patients diagnosed with type 2 diabetes mellitus

Alshammary, Amal F.; Alshammari, Abdulrahman M.; Alsobaie, Sarah; Alagee, Arwa A.; Khan, Imran Ali

doi:10.1016/j.sjbs.2023.103658

Cited by 12 publications

(8 citation statements)

References 37 publications

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“…IGF2 [494], IRF7 [495], E2F1 [496], TEAD4 [497], KCNH2 [498], E2F2 [499], SNHG7 [500], FLI1 [501], CYP3A5 [502], HMGCS2 [503], GOT1 [504], PPARGC1A [505], GC (GC vitamin D binding protein) [506], VNN1 [507], NOX4 [508], SLC2A1 [509], BPGM (bisphosphoglyceratemutase) [164], NR4A3 [354], PFKFB2 [510], CDH2 [511], F11 [512], AQP2 [513], CLDN2 [514], EGF (epidermal growth factor) [515], ANGPT1 [516], KNG1 [517], SERPINA5 [518], HRG (histidine rich glycoprotein) [519], KL (klotho) [520], DEFB1 [521], ACE2 [522], AQP3 [523], CADM1 [188], DPP4 [524], STC1 [525], REN (renin) [526], TRPM6 [527], MSR1 [528], CCR1 [529], TNFRSF11B [530], FZD5 [531], ERBB4 [216], F8 [532], VCAM1 [533], PTGER3 [534] and ALB (albumin) [535] have been shown to influence the genetic risk of sepsis. IGF2 [536], IRF7 [100], PRKCB (protein kinase C beta) [101], CCL5 [537], EEF1A2 [538], ACTN3 [264], FCN1 [539], BRSK2 [540], MNX1 [541], AMH (anti-Mullerian hormone) [542], E2F1 [543], HAP1 [544], PF4 [545], AGER (advanced glycosylation end-product specific receptor) [546], E2F2 [547], TYMP (thymidine phosphorylase) [548], PPP1CC [549], NR2E1 [550], GREM1 [436], GRIN1 [551], WNT3A [552], COMP (cartilage oligomeric matrix protein) [553], BHMT (betaine--homocysteine S-methyltransferase) [554], ANGPTL3 [555], PCK1 [556], KMO (kynurenine 3-monooxygenase) [557], HSD11B2 […”

Section: Discussionmentioning

confidence: 99%

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

Vastrad,

Vastrad

2024

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

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

Vastrad,

Vastrad

2024

Preprint

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“…HK2 [269], PNPLA3 [270], CPB2 [271], SEMA4C [272], LRP2 [273], SLC5A11 [274], F11 [275], ANGPTL2 [276] and CYP2A6 [277] genes might be related to the pathophysiology of autoimmune disease. A recent study revealed that SLAMF7 [278], FCRL3 [279], CCR4 [280], GZMA (granzyme A) [281], CCR2 [282], CD48 [283], CD3E [284], CCL4 [285], FASLG (Fas ligand) [286], SLAMF1 [287], CD28 [288], IL7R [289], UBASH3A [290], CD3D [291], ICOS (inducible T cell costimulator) [292], CCL5 [293], CCL3 [294], LTF (lactotransferrin) [295], CTLA4 [296], TNFRSF9 [253], C6 [297], IL5RA [298], IL2RG [299], NPY2R [300], DRD3 [301], CD52 [302], CD163 [303], IQGAP2 [304], PRPH (peripherin) [305], MSR1 [306], HGF (hepatocyte growth factor) [307], TTR (transthyretin) [308], IL9 [309], ADM (adrenomedullin) [310], ANGPTL4 [311], CHI3L1 [312], CDH1 [313], SREBF1 [314], CDKN1C [315], TSC22D4 [316], CFTR (CF transmembrane conductance regulator) [317], INSIG1 [318], HSD17B3 [319], GREM1 [320], ATF3 [321], HK2 [322], TF (transferrin) [323], GLUL (glutamate-ammonia ligase) [324], DMRT2 [325], FN3K [326], TREH (trehalase) [327], ADAMTS4 [328], BMP2 [329], LMNA (lamin A/C) [330], ERBB3 [331], HSD11B1 [332], DLL1 [333], NKX2-2 [334], FGF11 [335], ASPA (aspartoacylase) [336], FASN (fatty acid synthase) [337], PNPLA3 [338], ADORA1 [339], WNT7A [340…”

Section: Discussionmentioning

confidence: 99%

“…SLAMF1 [237], TAB2 [256] and HGF (hepatocyte growth factor) [260] might play an important role in the onset, development, and treatment of autoimmune disease. SLAMF1 [287], CFTR (CF transmembrane conductance regulator) [317], LMNA (lamin A/C) [330], ERBB3 [331], HGF (hepatocyte growth factor) [307] and CCL5 [293] might play an important role in the pathophysiology of diabetes mellitus. TAB2 [131], LMNA (lamin A/C) [168], ERBB3 [169], HGF (hepatocyte growth factor) [135] and CCL5 [124] were abnormally expressed in cardiovascular diseases.…”

Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

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Multiple sclerosis (MS) is the main reason for disability caused by autoimmunity. However, effective biomarkers for MS have not been identified. This study explored the molecular mechanisms and potential therapeutic targets for MS occurrence and progression using bioinformatics and next generation sequencing (NGS) data analysis. NGS data GSE138614, including patients with MS and 25 normal control samples, were obtained from Gene Expression Omnibus (GEO) database Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. Protein–protein interaction (PPI) network and module analyses were performed based on the DEGs. MiRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were built by Cytoscape to predict the underlying microRNAs (miRNAs) and transcription factors (TFs) associated with hub genes. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. A total of 959 DEGs (479 up regulated genes and 480 down regulated genes) were detected. The GO terms and pathways of DEGs involve immune system process, developmental process, immune system and regulation of cholesterol biosynthesis by SREBP (SREBF). The network analysis revealed that hub genes include LCK, PYHIN1, SLAMF1, DOK2, TAB2, CFTR, RHOB, LMNA, EGLN3 and ERBB3 might be involved in the development of MS. The present investigation illustrates a characteristic gene profile in MS, which might contribute to the interpretation of the progression of MS and provide novel biomarkers and therapeutic targets for MS.

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“…However, the key question revolves around the feasibility of employing the "patients as health educators" approach to target high-risk individuals in Saudi Arabia. This approach becomes especially pertinent in light of the high prevalence of familial aggregation of diabetes within Saudi families, making it seemingly appropriate to leverage patients as health educators to educate their family members as a preventive strategy aimed at high-risk individuals [ 3 , 5 , 6 ]. In the context of Saudi Arabia, addressing diabetes takes on added significance due to its high prevalence within families.…”

Section: Introductionmentioning

confidence: 99%

Empowering adult patients with diabetes for health educators’ role within their family members: A cross-sectional study

Alanazi,

Bajmal,

Aseeri

et al. 2024

PLoS ONE

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Background Patient engagement as partners in diabetes prevention for family members/close relatives is a novel and underexplored approach. This paper aims to assess patients’ willingness and confidence in their ability to succeed as health educators for their family members and investigate the influencing factors. Methods A cross-sectional descriptive study was conducted between January 2023 and April 2023. A newly developed and validated self-reported questionnaire, based on the Health Belief Model (HBM) and previous research, was administered to a convenient sample of 134 adult participants diagnosed with diabetes. These participants sought care at primary healthcare clinics at King Abdul-Aziz Medical City, Ministry of National Guard Health Affairs in Riyadh and Jeddah (MNGHA). The data was examined using statistical methods including descriptive analysis, ANOVA, Tukey’s HSD (Honestly Significant Difference) Post Hoc tests, and Pearson’s correlation coefficients. Results The majority of participants expressed a willingness to assume the role of health educators for their family members (n = 117, 87.31%) and reported a high level of willingness and confidence, as indicated by self-efficacy scores ranging from 12.00 to 25.00, with a mean of 21.12 (SD = 2.76). Participants’ willingness to be health educators exhibited positive correlations with their perceptions of diabetes severity and susceptibility (r = .433, p < .01), perceived benefits and barriers (r = .451, p < .01), cues to action (r = .520, p < .01), self-efficacy (r = .789, p < .01), and the total score of the questionnaire (r = .640, p < .01). Conclusions The majority of participants expressed their willingness to assume the role of health educators for their family members, and a significant portion reported confidence in their capacity to accomplish this objective. Healthcare providers should emphasize the importance of equipping patients with the skills and knowledge necessary to effectively convey health messages and serve as health educators within their communities. This expansion of the approach holds the potential to have a significant impact on public health strategies for diabetes prevention.

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Evidence from genetic studies among rs2107538 variant in the CCL5 gene and Saudi patients diagnosed with type 2 diabetes mellitus

Cited by 12 publications

References 37 publications

Identification of novel prognostic targets in acute kidney injury 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

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Empowering adult patients with diabetes for health educators’ role within their family members: A cross-sectional study

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