Journal of Clinical & Medical Genomics

Introduction: Idiopathic Pulmonary Arterial Hypertension (IPAH) is a subset of a heterogeneous group of diseases called Pulmonary Arterial Hypertension (PAH), characterized by elevated pulmonary arterial pressure (PAP) and is associated with severe arteriopathy, vascular lesions, and right heart failure. The role of prostacyclin synthase, an enzyme responsible for production of prostacyclin, and peroxisome proliferator-activated receptor gamma, involved in many cellular activities, is studied here.

Objectives: The objective of the study is to determine any association of prostacyclin synthase and Peroxisome proliferator-activated receptor gamma with Idiopathic Pulmonary Arterial Hypertension

Materials and methods: A total of 77 IPAH patients and 100 controls were genotyped using PCR SSCP and RFLP, and appropriate statistical tests were employed to determine the significance and to interpret the results.

Results and conclusion: This study has attempted to correlate promoter/gene polymorphisms of PGIS to its activity, and it can be concluded that the VNTR polymorphism and the polymorphism found in exon 6 may not have an effect on the levels of PGIS. The alanine variant of P12A polymorphism of PPARγ was found to be significantly associated with a reduced risk of IPAH.

Objective: To investigate P2X purinoceptor 7 (P2X7R) expression changes on peripheral blood mononuclear cell (PBMC) surfaces of systemic lupus erythematosus (SLE) patients as well as their association with serum cytokine levels and patients’ clinical features. In addition, the effects of different P2X7R single nucleotide polymorphisms (SNPs) on P2X7R expression and cytokine production were investigated.

Method: Twenty-nine new-onset SLE patients and twenty-eight healthy controls were enrolled, while P2X7R expression levels of lymphocytes, CD4+ cells and CD19+ cells were analyzed by flow cytometry. Serum IL-1β, IL-6 and TNF-α levels were analyzed by ELISA and three P2X7R SNPs (1068G>A, 1096C>G and 1513A>C) were PCR genotyped in 14 SLE patients and 14 healthy controls.

Results: P2X7R surface expression levels of lymphocytes, CD4+ and CD19+ cells from new-onset SLE patients were significantly higher than that of controls and significantly elevated on lymphocytes from patients with concurrent arthritis or leukopenia as well as on CD19+ cells of NP-SLE patients. Moreover, P2X7R expression levels on lymphocytes and CD19+ cells of SLE patients were positively correlated with serum TNF-α/IL-6 and TNF-α level, respectively. SLE patients with the P2X7R 1068GG genotype had significantly higher expression level ratios of TNF-α, IL-6 and IL-1β to P2X7R on lymphocytes compared to patients harboring the 1068GA genotype.

Conclusion: P2X7R, as an important cell surface regulator of several key cytokines, is involved in the pathogenesis of SLE and also associated with organ injuries like arthritis, leukopenia and neuropsychiatric damage. P2X7R 1068GG maybe a SLE susceptibility genotype since it enhances cytokine secretion in SLE patients.

Although the etiology of autism spectrum disorder (ASD) is still unclear, multiple factors have been suggested to be involved in its pathogenesis. There is a strong evidence for multiple interacting genetic factors as the main cause of autism. Hundreds of genes and genetic mutations that are involved in autism development were identified, and thus serve as useful genetic markers for identification of autism. Those genes play a key role in brain development, or may code for immune proteins. Numerous studies have suggested that autism can be inherited based on earlier twins studies which showed that monozygotic (MZ) twins had higher concordance rates than dizygotic (DZ) twins for ASDs. In this review we consider the genetic factors that underlie the pathogenesis of autism and their contribution to the disease outcome. In conclusion, candidate genes studies will highlight the role of interaction between innate immunity and neuronal activity in the etiology of autism, and it may lead to earlier diagnosis and behavioral intervention which improves ASD subjects outcomes.

Background: Recurrent cutaneous squamous cell cancer (CSCC) is associated with poor outcomes with perineural invasion reported as a frequent finding in such lesions. Given the morbidity associated with late recurrence, identifying aggressive subtypes of CSCC at the time of primary excision is all the more necessary. This project sought to identify differentially expressed genes (DEGs) between perineural invasion-positive (PP) and perineural invasion-negative (PN) CSCC. Gene-based classification models for diagnosis of perineural invasion in CSCC were also developed.

Method: Forty fresh-frozen surgical specimens of CSCC with presence or absence of histopathological perineural invasion were processed for RNA isolation and hybridization to Affymetrix-U219 DNA microarrays. Raw gene expression data were normalized by Robust Multi-array Averaging (RMA) and log2 transformed. DEGs were identified by empirical Bayes statistics using the Bioconductor limma package. BRB-ArrayTools software was used to develop gene expression-based sample classification models. Using leave-one-out cross-validation, the resulting accuracies of eight different classification algorithms were evaluated.

Results: Twenty-one PP and 19 PN samples were analyzed. At a stringent limma p-value (p<0.001), 24 genes were differentially expressed between specimens. The cross-validated performance of the eight classification models exhibited a mean accuracy of 85-95%. Diagonal linear discriminant was most accurate at 95%, followed by Bayesian compound covariate at 94%. The poorest accuracy (85%) was observed for 1-Nearest neighbor and Support vector machines. For all eight methods, the sensitivities and specificities ranged from 79%-95%.

Conclusion: Gene expression distinguishes between PP and PN CSCC. Classification models based on these gene patterns distinguish PP and PN cancers with strong statistical accuracy and may potentiate more timely and objective diagnosis of perineural invasion that could guide more comprehensive adjuvant therapies.

Gene expression levels are important for disease, such as, Cancer diagnosis. This paper proposed a SVM-based ensemble classifier to classify the control and cancer groups based on gene expression levels from microarray data. A combinational Recursive Feature Elimination in conjunction with the Adaboost algorithm was developed to select significant features and design the proper classifier. The method is applied to microarray data of cancer patients, and the results show improvements on the success rate. By AUC calculation, the SVM-based ensemble classifier shows predominate performance. Furthermore, the characteristics and different effect issues to classification performance is discussed. If a single SVM can obtain satisfactory classification performance, an ensemble SVM is hardly capable to improve it. Otherwise, an ensemble of SVM is superior to the best single SVM. We also investigated the effect of kernel functions, feature selections and type of classifiers on the classification.

Background: Type 2 diabetes (T2D) is a heterogeneous disorder that results from a combination of environmental and genetic factors. Insulin resistance (IR) is the core defect in T2D. The molecular mechanisms underlying IR are poorly understood. Protein tyrosine kinases and Protein tyrosine phosphatase 1B (PTPN1) are important regulators of insulin signal transduction. The association of PTPN1single-nucleotide polymorphisms (SNPs) with traits related to T2D has been investigated. The aim of this study was to determine the association of 1023C>A and 467T>C gene polymorphisms with Type 2 diabetes and its related metabolic traits.

Method: Polymerase chain reaction/restriction fragment length polymorphism (PCR-RFLP) analyses were carried out to detect the 1023C>A and 467T>C variants of PTPN1 gene in 100 Egyptian patients with T2D as compared to controls (n=80).

Results: The 1023C>A PTPN1genotype was significantly associated with T2DM (X2=7.816, P=0.02). The C allele was more frequent in the T2DM as compared to controls (p=0.001), odds ratio (OR) and 95% CI= 0.282 (0.131-0.606). We did not observe any significant difference in 467T>C PTPN1 genotypes between patients and control groups (X2=2.205, P=0.332). 1023C>A and 467T>C PTPN1 variants showed non-significant association with diabetic metabolic traits in both groups; plasma insulin levels, fasting blood glucose levels (FBG), HOMA-IR, the lipid profile parameters , diastolic blood pressure (DBP), systolic blood pressure (SBP), Waist circumference (WC) and body mass index (BMI).

Conclusion: The PTPN1 promoter variant 1023C>A was associated with presence of T2D, but it had no correlation with any of neither metabolic traits nor obesity in this study but we could not detect any association between 467T>C variants of PTPN1 gene with T2D Egyptian patients nor related traits in this study. Further studies must be done on a larger population to detect any potential metabolic association.