Association between Genetic Variants of CELSR2-PSRC1-SORT1 and Cardiovascular Diseases: A Systematic Review and Meta-Analysis

Castillo-Ávila, Rosa Giannina; González‐Castro, Thelma Beatriz; Tovilla‐Zárate, Carlos Alfonso; Martínez‐Magaña, José Jaime; López‐Narváez, María Lilia; Juárez‐Rojop, Isela Esther; Arias‐Vázquez, Pedro Iván; Borgonio-Cuadra, Verónica Marusa; Pérez-Hernández, Nonanzit; Rodríguez-Pérez, José Manuel

doi:10.3390/jcdd10030091

Cited by 5 publications

(4 citation statements)

References 34 publications

(47 reference statements)

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“…Such broad distribution suggests their essential roles in cardiac and metabolic functions. Interestingly, all four genes participate in lipid metabolism; furthermore, all but NOC3L have been reported in inflammation 41, 42 .…”

Section: Resultsmentioning

confidence: 99%

“…LRP1 encodes LDL receptor-related protein and plays diverse roles in lipoprotein metabolism, endocytosis, cell growth, cell migration, inflammation, and apoptosis 42 . Furthermore, CELSR2 and PSRC1, together with SORT1, form a PRSC1-CELSR2-SORT1 axis which has been implicated in various CVDs 41, 58 . SORT1 encodes sortilin 1 that functions in lipid metabolism and immune responses, such as V-LDL secretion, LDL-C metabolism, PCSK9 secretion, inflammation, and formation of foam cells 59 .…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, CELSR2 and PSRC1, together with SORT1, form a PRSC1-CELSR2-SORT1 axis which has been implicated in various CVDs 41,58 . SORT1 encodes sortilin 1 that functions in lipid metabolism and immune responses, such as V-LDL secretion, LDL-C metabolism, PCSK9 secretion, inflammation, and formation of foam cells 59 .…”

Section: Discussionmentioning

confidence: 99%

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Shared Genetic Susceptibility between Abdominal Aortic Aneurysm and Cardiometabolic Traits

Zheng,

Tsao,

Pan

2023

Preprint

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Abdominal aortic aneurysm (AAA) presents abnormal metabolism and co-occurs with cardiometabolic disorders, suggesting a shared genetic susceptibility. We investigated this commonality leveraging recent GWAS studies of AAA and 32 cardiometabolic traits (CMTs). Significant genetic correlations are found between AAA and 21 CMTs, among which are causal relationship with coronary artery disease, hypertension, lipid traits, and blood pressure. For each trait pair, we identified shared causal variants, genes, and pathways, which revealed cholesterol metabolism and immune responses were the shared most prominently. Additionally, we uncovered the tissue and cell type specificity in the shared signals, with strong enrichment across traits in liver, arteries, adipose tissues, macrophages, adipocytes, and fibroblasts. Finally, we leveraged drug-gene databases and identified several lipid-lowering drugs and antioxidants with high potential to treat AAA with comorbidities. Our study provides insight into the shared genetic mechanism for AAA and cardiometabolic traits and potential targets for pharmacological intervention.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Shared Genetic Susceptibility between Abdominal Aortic Aneurysm and Cardiometabolic Traits

Zheng,

Tsao,

Pan

2023

Preprint

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“…These genes exhibit clear and consistent expression in adipose tissue, cardiovascular tissue, and brain tissue (Fig. 3), and are closely associated with both CVD and cancer [50][51][52][53] . The pleiotropy genes that are linked to these risk genes are enriched in various cancer-related pathways as well as cell cycle-related pathways ( gure S13-16).…”

Section: Discussionmentioning

confidence: 95%

Shared genetic architecture of Genitourinary Cancer and Heart Failure

Jiang,

Liu,

Xia

et al. 2023

Preprint

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Background Epidemiological and clinical investigations have established a robust correlation between Cancer and Heart Failure, with patients afflicted by either condition being at an elevated risk of developing the other.But the existence of a shared genetic etiology remains elusive.The objective was to ascertain the shared genetic between genitourinary cancer and heart failure. Methods Conducted a genetic association analysis between common genitourinary tumors and heart failure using genome-wide association study data. Identified the corresponding pleiotropic loci and genes through various statistical methods, and performed tissue and immune cell enrichment analyses for these pleiotropic genes. Furthermore, evaluated the causal relationship between them using two-sample Mendelian randomization. Results Identified significant shared genetic bases among Bladder, Endometrial, Kidney cancer and Heart failure. Specifically, discovered 5 shared genetic loci: rs10757277, rs1537372, rs543968, rs653178 and rs56094641. These loci are associated with related genes such as CDKN2B-AS1,ANRIL, ABO, ATXN2, and FTO. Furthermore, the genes CADM2,CELSR2 were identifiedgene. Pleiotropy was found to be significantly enriched in various tissues including prostate, cervix, testis intestinal-terminalileum et cetera. Additionally, a diverse range of immune cells were specifically enriched in these two types of diseases. Mendelian randomization analysis provided suggestive evidence for these findings. Conclusions Bladder, Endometrial,Kidney cancer and Heart failure have underlying mechanisms of shared genetic. Our results also suggest that the immune system plays an important role in these two types of diseases.These findings provide support for a shared genetic basis underlying both disease types and offer novel evidence for targeted therapeutic interventions against them.

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Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Diabetic patients are prone to diabetic kidney disease (DKD), which may cause cardiovascular damage, hypertension and obesity, and reduce quality of life. As a result, the life quality of patients was seriously reduced. However, the pathogenesis of diabetic kidney disease (DKD) has not been fully elucidated, and current treatments remain inadequate. Therefore, it is essential to explore the molecular mechanism of DKD and its complications. Next Generation Sequancing (GSE217709) dataset was obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were picked out by R software. Then Gene ontology (GO) and REACTOME pathway enrichment analysis were performed by g:Profiler database, protein-protein interaction (PPI) of DEGs was constructed by Human Integrated Protein-Protein Interaction rEference (HIPPIE) database . Module analysis was carried out by Cytoscape plug-in PEWCC. Subsequently, miRNA-hub gene regulatory network and TF- hub gene regulatory network were performed by miRNet database and NetworkAnalyst database. Finally, validation of hub genes was performed by receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes. In total, 958 DEGs, including 479 up regulated and 479 down regulated genes, were identified. The GO and pathway enrichment changes of DEGs were mainly enriched in biological regulation, multicellular organismal process, signaling by GPCR and extracellular matrix organization. Ten hub genes (HSPA8, HSP90AA1, HSPA5, SDCBP, HSP90B1, VCAM1, MYH9, FLNA, MDFI and PML) associated with DKD and its complications were identified. Bioinformatics analysis is a useful tool to explore the molecular mechanism and pathogenesis of DKD and its complications. The identified hub genes may participate in the onset and development of DKD and its complications and serve as therapeutic targets.

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Association between Genetic Variants of CELSR2-PSRC1-SORT1 and Cardiovascular Diseases: A Systematic Review and Meta-Analysis

Cited by 5 publications

References 34 publications

Shared Genetic Susceptibility between Abdominal Aortic Aneurysm and Cardiometabolic Traits

Shared Genetic Susceptibility between Abdominal Aortic Aneurysm and Cardiometabolic Traits

Shared genetic architecture of Genitourinary Cancer and Heart Failure

Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

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