Association Study of Genetic Variants in <i>CDKN2A/CDKN2B</i> Genes/Loci with Late‐Onset Alzheimer′s Disease

Tedde, Andrea; Piaceri, Irene; Bagnoli, Silvia; Lucenteforte, Ersilia; Ueberham, Uwe; Arendt, Thomas; Sorbi, Sandro; Nacmias, Benedetta

doi:10.4061/2011/374631

Cited by 6 publications

(8 citation statements)

References 16 publications

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“…The top five out of the predicted results of both diseases were listed in Table 2, where all of the top five predicted associations related to prostate cancer were found evidence in database LncRNADisease, and four out of the top five predicted associations related to Alzheimer's disease were found evidence in database LncRNADisease except CDKN2B-AS10. However, Tedde et al [64] found that CDKN2A/CDKN2B genes/loci associated with late-onset Alzheimer's disease, which proved the credibility of our LDAI-ISPS.…”

Section: Case Study For Potential Associationssupporting

confidence: 61%

LDAI-ISPS: LncRNA–Disease Associations Inference Based on Integrated Space Projection Scores

Zhang

Chen

et al. 2020

IJMS

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Long non-coding RNAs (long ncRNAs, lncRNAs) of all kinds have been implicated in a range of cell developmental processes and diseases, while they are not translated into proteins. Inferring diseases associated lncRNAs by computational methods can be helpful to understand the pathogenesis of diseases, but those current computational methods still have not achieved remarkable predictive performance: such as the inaccurate construction of similarity networks and inadequate numbers of known lncRNA–disease associations. In this research, we proposed a lncRNA–disease associations inference based on integrated space projection scores (LDAI-ISPS) composed of the following key steps: changing the Boolean network of known lncRNA–disease associations into the weighted networks via combining all the global information (e.g., disease semantic similarities, lncRNA functional similarities, and known lncRNA–disease associations); obtaining the space projection scores via vector projections of the weighted networks to form the final prediction scores without biases. The leave-one-out cross validation (LOOCV) results showed that, compared with other methods, LDAI-ISPS had a higher accuracy with area-under-the-curve (AUC) value of 0.9154 for inferring diseases, with AUC value of 0.8865 for inferring new lncRNAs (whose associations related to diseases are unknown), with AUC value of 0.7518 for inferring isolated diseases (whose associations related to lncRNAs are unknown). A case study also confirmed the predictive performance of LDAI-ISPS as a helper for traditional biological experiments in inferring the potential LncRNA–disease associations and isolated diseases.

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Section: Case Study For Potential Associationssupporting

confidence: 61%

LDAI-ISPS: LncRNA–Disease Associations Inference Based on Integrated Space Projection Scores

Zhang

Chen

et al. 2020

IJMS

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“…Zuchner and colleagues identified that CDKN2A at 9p21 are implicated in the susceptibility in the late-onset AD (Zuchner et al, 2008), but the role of CDKN2A genetic variants in AD is not confirmed in late-onset patients based on Tedde and colleagues's (2011). Therefore, further studies are needed to elucidate the role of tumor suppressor protein CDKN2A in the susceptibility of AD.…”

Section: Discussionmentioning

confidence: 99%

Nodes and biological processes identified on the basis of network analysis in the brain of the senescence accelerated mice as an Alzheimer's disease animal model

Cheng¹,

Cui

Zheng³

et al. 2013

Front. Aging Neurosci.

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Harboring the behavioral and histopathological signatures of Alzheimer's disease (AD), senescence accelerated mouse-prone 8 (SAMP8) mice are currently considered a robust model for studying AD. However, the underlying mechanisms, prioritized pathways and genes in SAMP8 mice linked to AD remain unclear. In this study, we provide a biological interpretation of the molecular underpinnings of SAMP8 mice. Our results were derived from differentially expressed genes in the hippocampus and cerebral cortex of SAMP8 mice compared to age-matched SAMR1 mice at 2, 6, and 12 months of age using cDNA microarray analysis. On the basis of PPI, MetaCore and the co-expression network, we constructed a distinct genetic sub-network in the brains of SAMP8 mice. Next, we determined that the regulation of synaptic transmission and apoptosis were disrupted in the brains of SAMP8 mice. We found abnormal gene expression of RAF1, MAPT, PTGS2, CDKN2A, CAMK2A, NTRK2, AGER, ADRBK1, MCM3AP, and STUB1, which may have initiated the dysfunction of biological processes in the brains of SAMP8 mice. Specifically, we found microRNAs, including miR-20a, miR-17, miR-34a, miR-155, miR-18a, miR-22, miR-26a, miR-101, miR-106b, and miR-125b, that might regulate the expression of nodes in the sub-network. Taken together, these results provide new insights into the biological and genetic mechanisms of SAMP8 mice and add an important dimension to our understanding of the neuro-pathogenesis in SAMP8 mice from a systems perspective.

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“…CDKN2A [343] and CD40 [337] are involved in mediating the progression of neurodegenerative diseases. Previous studies report the altered expression of CDKN2A [403], BTK (Bruton tyrosine kinase) [440] and CD40…”

Section: Discussionmentioning

confidence: 99%

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad,

Vastrad

2023

Preprint

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Schizophrenia is thought to be the most prevalent chronic psychiatric disorder. Numerous proteins have been identified that are associated with the occurrence and development of schizophrenia. This study aimed to identify potential core genes and pathways involved in schizophrenia, through exhaustive bioinformatic and next generation sequencing (NGS) data analyses using GSE20966 NGS data of neural progenitor cells and neurons obtained from healthy controls and patients with schizophrenia. The NGS data were downloaded from the Gene Expression Omnibus database. NGS data was processed by the DESeq2 package in R software and the differentially expressed genes (DEGs) were identified. Gene Ontology (GO) enrichment analysis and REACTOME pathway enrichment analysis were carried out to identify potential biological functions and pathways of the DEGs. Protein-protein interaction (PPI) network, module, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to identify the hub genes, miRNA and TFs. Potential hub genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). In this investigation, an overall 955 DEGs were identified: 478 genes were remarkably up regulated and 477 genes were distinctly down regulated. These genes were enriched for GO terms and pathways mainly involved in the multicellular organismal process, GPCR ligand binding, regulation of cellular process and amine ligand-binding receptors. MYC, FN1, CDKN2A, EEF1G, CAV1, ONECUT1, SYK, MAPK13, TFAP2A and BTK were considered the potential hub genes. miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed successfully. On the whole, the findings of this investigation enhance our understanding of the potential molecular mechanisms of schizophrenia and provide potential targets for further investigation.

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Association Study of Genetic Variants in CDKN2A/CDKN2B Genes/Loci with Late‐Onset Alzheimer′s Disease

Cited by 6 publications

References 16 publications

LDAI-ISPS: LncRNA–Disease Associations Inference Based on Integrated Space Projection Scores

LDAI-ISPS: LncRNA–Disease Associations Inference Based on Integrated Space Projection Scores

Nodes and biological processes identified on the basis of network analysis in the brain of the senescence accelerated mice as an Alzheimer's disease animal model

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

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