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Construction and analysis of single nucleotide polymorphism-single nucleotide polymorphism interaction networks.

ABSTRACT: The study of gene regulatory network and protein-protein interaction network is believed to be fundamental to the understanding of molecular processes and functions in systems biology. In this study, the authors are interested in single nucleotide polymorphism (SNP) level and construct SNP-SNP interaction network to understand genetic characters and pathogenetic mechanisms of complex diseases. The authors employ existing methods to mine, model and evaluate a SNP sub-network from SNP-SNP interactions. In the study, the authors employ the two SNP datasets: Parkinson disease and coronary artery disease to demonstrate the procedure of construction and analysis of SNP-SNP interaction networks. Experimental results are reported to demonstrate the procedure of construction and analysis of such SNP-SNP interaction networks can recover some existing biological results and related disease genes.

SUBMITTER: Liu Y

PROVIDER: S-EPMC8687305 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Construction and analysis of single nucleotide polymorphism-single nucleotide polymorphism interaction networks.

Liu Yang Y Li Xutao X Liu Zhiping Z Chen Luonan L Ng Michael K MK

IET systems biology 20131001 5

The study of gene regulatory network and protein-protein interaction network is believed to be fundamental to the understanding of molecular processes and functions in systems biology. In this study, the authors are interested in single nucleotide polymorphism (SNP) level and construct SNP-SNP interaction network to understand genetic characters and pathogenetic mechanisms of complex diseases. The authors employ existing methods to mine, model and evaluate a SNP sub-network from SNP-SNP interact ...[more]

PMID: 24067417

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Project description:Background: Prostate cancer is one of the most common malignancies among men worldwide currently. However, specific mechanisms of prostate cancer were still not fully understood due to lack of integrated molecular analyses. We performed this study to establish an mRNA-single nucleotide polymorphism (SNP)-microRNA (miRNA) interaction network by comprehensive bioinformatics analysis, and search for novel biomarkers for prostate cancer. Materials and methods: mRNA, miRNA, and SNP data were acquired from Gene Expression Omnibus (GEO) database. Differential expression analysis was performed to identify differentially expressed genes (DEGs) and miRNAs (DEMs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, protein-protein interaction (PPI) analysis and expression quantitative trait loci (eQTL) analysis of DEGs were conducted. SNPs related to DEMs (miRSNPs) were downloaded from the open-source website MirSNP and PolymiRTS 3.0. TargetScan and miRDB databases were used for the target mRNA prediction of miRNA. The mRNA-SNP-miRNA interaction network was then constructed and visualized by Cytoscape 3.9.0. Selected key biomarkers were further validated using the Cancer Genome Atlas (TCGA) database. A nomogram model was constructed to predict the risk of prostate cancer. Results: In our study, 266 DEGs and 11 DEMs were identified. KEGG pathway analysis showed that DEGs were strikingly enriched in focal adhesion and PI3K-Akt signaling pathway. A total of 60 mRNA-SNP-miRNAs trios were identified to establish the mRNA-SNP-miRNA interaction network. Seven mRNAs in mRNA-SNP-miRNA network were consistent with the predicted target mRNAs of miRNA. These results were largely validated by the TCGA database analysis. A nomogram was constructed that contained four variables (ITGB8, hsa-miR-21, hsa-miR-30b and prostate-specific antigen (PSA) value) for predicting the risk of prostate cancer. Conclusion: Our study established the mRNA-SNP-miRNA interaction network in prostate cancer. The interaction network showed that hsa-miR-21, hsa-miR-30b, and ITGB8 may be utilized as new biomarkers for prostate cancer.

Project description:IntroductionInterstitial Cystitis (IC) is a chronic condition diagnosed based on the presence of symptoms, such as suprapubic/ pelvic pain, pressure or discomfort in association with urgency and increased urinary frequency. Confusable diseases must be excluded. However, there is no objective test or marker to establish the presence of the disease. Diagnosis and patient management is often difficult, given the poor understanding of IC pathogenesis and its unknown etiology and genetics. As an attempt to find biomarkers related to IC, we assessed the association between 20 selected single nucleotide polymorphism (SNPs) with IC and pain severity.ObjectivesTo assess the presence of SNPs in IC patients' blood samples and correlate them with the disease and chronic pain condition.MethodsA case-control study was conducted. We selected 34 female patients with IC diagnosed according to NIDDK criteria and 23 patients in the control group (previously healthy women with only stress urinary incontinence). IC patients were allocated into two groups according to reported chronic pain severity. We selected the following SNPs for analysis: rs1800871, rs1800872, rs1800896, rs1800471, rs1800629, rs361525, rs1800497, rs6311, rs6277, rs6276, rs6313, rs2835859, rs11127292, rs2243248, rs6887695, rs3212227, rs1799971, rs12579350, rs3813034, and rs6746030. Genotyping was performed by real-time PCR (q-PCR).ResultsThe polymorphic allele of SNP rs11127292 exhibited a higher frequency in subjects with IC than in controls (p:0.01). The polymorphic allele of SNP rs6311 was more frequent in patients with severe pain (p:0.03). The frequency of the wild-type allele of SNP rs1799971 was higher in patients with mild to moderate pain (p:0.04).ConclusionThe results indicated differences in SNP frequency among subjects, suggesting that SNPs could serve either as a marker of IC or as a marker of pain severity in IC patients. The study showed promising results regarding IC and polymorphism associations. These associations have not been previously reported.

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Construction and analysis of single nucleotide polymorphism-single nucleotide polymorphism interaction networks.

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Construction and analysis of single nucleotide polymorphism-single nucleotide polymorphism interaction networks.

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