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Schizophrenia is a severe chronic debilitating disorder with millions of affected individuals. Diagnosis is based on clinical presentations, which are made when the progressive disease has appeared. Early diagnosis may help improve the clinical outcomes and response to treatments. Lack of a reliable molecular diagnostic invokes the identification of novel biomarkers. To elucidate the molecular basis of the disease, in this study we used two mRNA expression arrays, including GSE93987 and GSE38485, and one miRNA array, GSE54914, and meta-analysis was conducted for evaluation of mRNA expression arrays via metaDE package. Using WGCNA package, we performed network analysis for both mRNA expression arrays separately. Then, we constructed protein-protein interaction network for significant modules. Limma package was employed to analyze the miRNA array for identification of dysregulated miRNAs (DEMs). Using genes of significant modules and DEMs, a mRNA-miRNA network was constructed and hub genes and miRNAs were identified. To confirm the dysregulated genes, expression values were evaluated through available datasets including GSE62333, GSE93987, and GSE38485. The ability of the detected hub miRNAs to discriminate schizophrenia from healthy controls was evaluated by assessing the receiver-operating curve. Finally, the expression levels of genes and miRNAs were evaluated in 40 schizophrenia patients compared with healthy controls via Real-Time PCR. The results confirmed dysregulation of hsa-miR-574-5P, hsa-miR-1827, hsa-miR-4429, CREBRF, ARPP19, TGFBR2, and YWHAZ in blood samples of schizophrenia patients. In conclusion, three miRNAs including hsa-miR-574-5P, hsa-miR-1827, and hsa-miR-4429 are suggested as potential biomarkers for diagnosis of schizophrenia.

In the current study, Methylation and expression microarray data related to Schizophrenia (SCZ) were analyzed to identify genes whose aberrant promoter methylation could play a crucial role in the dysregulation of gene expression. After data analysis, two genes including KLF9 and MYH11 were identified. Considering CpG sites in the promoter region of the mentioned genes using Methprimer database, Bisulfite specific primer (BSP) was designed, and Methylation specific-high resolution melting (MS-HRM) analysis was performed to assess the promoter methylation status of the genes, and also real-time PCR was used to evaluate mRNA expression level of KLF9 and MYH11 in 50 SCZ and healthy control specimens. The results show 62% hypermethylation and 38% hypomethylation of KLF9 in SCZ samples and 34% hypermethylation and 66% hypomethylation in healthy control samples. MYH11 promoter shows 54% hypermethylation and 46% hypomethylation in SCZ samples while 28% hypermethylation and 72% hypomethylation in healthy controls. The result of real-time PCR shows downregulation of KLF9 and MYH11 in SCZ samples compared with healthy samples. The aberrant methylation has a direct effect on the expression of mentioned genes.

The high mortality rate in Breast Cancer (BC) is largely due to the difficulty of early detection and inadequate accuracy of the current diagnostic approaches. In this study, we investigated serum and tumor miRNA profiles of 1800 breast cancer miRNA expression profiles of GEO (Gene Expression Omnibus) and also more than 2500 patients in TCGA (The Cancer Genome Atlas) database. After data analysis by some packages like Limma, and WGCNA, we selected 6 hub miRNAs. Then by collecting serum samples of 100 breast cancer patients, real-time PCR was performed and the diagnosis performance of hub miRNAs was evaluated by ROC curve. Due to more validation, Kaplan-Meier Plot of hub miRNAs was evaluated by 1262 BC samples using METABRIC database. The target genes of 6 hub miRNAs were investigated using Mirbase, Mirtarbase, Targetscan, and TransmiR and after the identification of target genes, their expression was evaluated in 1600 BC samples using GEO database. Finally, six miRNAs including Hsa-miR-151a-5p, Hsa-miR-34a-5p, Hsa-miR-1307-3p, Hsa-miR-450b-5p, Hsa-miR-501-3p, and Hsa-miR-532-5p were introduced as effective biomarkers of BC.

The high mortality rate in Breast Cancer (BC) is largely due to the difficulty of early detection and inadequate accuracy of the current diagnostic approaches. MicroRNAs (miRNAs) are a class of non-coding RNAs characterized by their short length of 22 nucleotides-length, which have been found with regulatory functions in gene expression and their dysregulation are associated with a wide variety of human diseases such as various types of cancer including BC. In this study, we investigated serum and tumor miRNA profiles from more than 1500 breast cancer miRNA expression profiles from GEO and also more than 2500 patients by TCGA database. After data analysis by some packages like Limma, MetaDE, and WGCNA, we selected 6 hub miRNAs. Then by collecting serum samples of 100 breast cancer patients, real-time PCR was performed and the diagnosis performance of hub miRNAs was evaluated by ROC curve. Due to more validation, Kaplan-Meier Plot of hub miRNAs evaluated by 1262 BC samples using METABRIC database. Therefore by considering internationally and locally data, we succeeded to identify the more prevalent and effective breast cancer biomarkers including Hsa-miR-151a-5p, Hsa-miR-34a-5p, Hsa-miR-1307-3p, Hsa-miR-450b-5p, Hsa-miR-501-3p, and Hsa-miR-532-5p.

Background: Type I bipolar disorder is a chronic mental illness with a recurrent nature. Objectives: This study was done to identify risk factors for relapse in patients with type I bipolar disorder using Poisson regression. Methods: In this retrospective cohort study, data were collected from 331 bipolar type I patients admitted to the largest and the only psychiatric hospital in West of Iran, Kermanshah. Poisson regression was used to identify risk factors for the relapse numbers in STATA software. Results: One hundred sixty six of 331 patients (50.2%) were men. Mean and standard deviation of relapse rates in males and females were 2.93 ± 0.16 and 2.93 ± 0.15, respectively. In multivariate analysis, the results showed that with increasing age of disease onset (RR = 0.98, 95% CI :(0.97 -0.99)) and adherence to medication (RR = 0.86, 95% CI :(0.75-0.98)), the relapse rate ratio decreased, and being divorced or widowed (RR = 1.20,95% CI :( 1.01-1.45)), cigarette smoking (RR = 1.28, 95% CI: (1.52-1.09)), and birth season of winter (RR = 1.18, 95% CI: (1.00-1.41)) increased the rate ratio of relapse. Conclusions: The results of this study showed that the frequency of relapse was lower in bipolar patients younger at the time of disease onset, cigarette smokers, divorced or widowed patients, and patients with no treatment adherent. It is necessary to prevent or reduce the likelihood of relapse, especially in patients who do not have therapeutic adherence through educational and therapeutic measurements.