Hsa-miR-584-5p as a novel candidate biomarker in Turkish men with severe coronary artery disease

Coronary artery disease (CAD) is still the preliminary cause of mortality and morbidity in the developed world. Identification of novel predictive and therapeutic biomarkers is crucial for accurate diagnosis, prognosis and treatment of the CAD. The aim of this study was to detect novel candidate miRNA biomarker that may be used in the management of CAD. We performed miRNA profiling in whole blood samples of angiographically confirmed Turkish men with CAD and non-CAD controls with insignificant coronary stenosis. Validation of microarray results was performed by qRT-PCR in a larger cohort of 62 samples. We subsequently assessed the diagnostic value of the miRNA and correlations of miRNA with clinical parameters. miRNA-target identification and network analyses were conducted by Ingenuity Pathway Analysis (IPA) software. Hsa-miR-584-5p was one of the top significantly dysregulated miRNA observed in miRNA microarray. Men-specific down-regulation (p = 0.040) of hsa-miR-584-5p was confirmed by qRT-PCR. ROC curve analysis highlighted the potential diagnostic value of hsa-miR-584-5p with a power area under the curve (AUC) of 0.714 and 0.643 in men and in total sample, respectively. The expression levels of hsa-miR-584-5p showed inverse correlation with stenosis and Gensini scores. IPA revealed CDH13 as the only CAD related predicted target for the miRNA with biological evidence of its involvement in CAD. This study suggests that hsa-miR-584-5p, known to be tumor suppressor miRNA, as a candidate biomarker for CAD and highlighted its putative role in the CAD pathogenesis. The validation of results in larger samples incorporating functional studies warrant further research

Different perspectives on translational genomics in personalized medicine.

Personalized medicine is a relatively new and interesting concept in the medical and healthcare industries. New approaches in current research have supported the search for biomarkers, based on the genomic, epigenomic and proteomic profile of individuals, using new technological tools. This perspective involves the potential to determine optimal medical interventions and provide the optimal benefit-risk balance for treatment, whilst it also takes a patient’s personal situation into consideration. Translational genomics, a subfield of personalized medicine, is changing medical practice, by facilitating clinical or non-clinical screening tests, informing diagnoses and therapeutics, and routinely offering personalized health-risk assessments and personalized treatments. Further research into translational genomics will play a critical role in creating a new approach to cancer, pharmacogenomics, and women’s health. Our current knowledge may be used to develop new solutions that can be used to minimize, improve, manage, and delay the symptoms of diseases in real-time and maintain a healthy lifestyle. In this review, we define and discuss the current status of translational genomics in some special areas including integration into research and health care

Selection of biomarker from candidate miRNAs for early detection of ovarian cancer

Ovarian Ovarian cancer cancercancer (OC)(OC) is one of the most most common common gynaecological gynaecological malignancies malignancies malignanciesamong among womenwomen women.In ovarian malignancies malignancies malignancies,approximately approximately approximately approximately approximately90 %of cases cases are epithelial epithelial ovarian cancer cancer(EOC), (EOC), which which is the most lethal ovarianovarian cancer cancercancer type type becausebecause becausebecause of alack of screening screening screening tests and asymptomatic asymptomatic asymptomatic asymptomatic [1].Early detection detection of EOC has agreat great importance importancein the clinic [2]. Since observation observation of cancer cancer massmass and andbiopsy biopsyareare difficult difficult in early early stages stages ,diagnosis by non -invasiveinvasive methods methods areare becomingbecoming becoming more more important important .Therefore Therefore ThereforeTherefore Therefore ,the thedetectiondetection detection of tumor tumor tumormaterials materials materials passed to body body fluids in the first stage stage of cancer cancercancer can make possible possible possible possiblethe early early diagnosis diagnosisdiagnosis of cancer cancercancer .MicroRNAs MicroRNAs (miRNAs miRNAs ), oneone type type of specific specificspecific biomarkers biomarkersbiomarkers forfor the pathogenesis pathogenesis pathogenesis pathogenesis of cancer cancercancer ,areare usedused for fordetection detection detection detection,prognosis prognosis prognosis ,diagnosis diagnosis diagnosis ,and therapy therapyin several several diseases diseases diseases [3]. The purpose purposepurpose purpose of this study was wasto identify identify identifynovel circulating circulating miRNAs miRNAsmiRNAsto be usedused usedas early early diagnosis diagnosis diagnosis andand predictionprediction prediction prediction toolstools forfor EOC .Detection Detection Detection of thethe differences differences differences differencesdifferences in miRNAs miRNAs expression expression havehave been validated validated by RT -qPCR .Different Different Different expressions expressions expressions expressions of hsa -miR -19091909 -5p, hsa hsa-miR -885885 -5p,and andhsa -letlet -7d-3pwerewere statistically statistically statistically significant significant (p <0.05 ).It has been detecteddetected detected detectedwhich which genesgenes genesand related related related pathways pathwayspathwaysaffected affected by the validated validated miRNAs miRNAs using using "Pathway "Pathway Studio" Studio" database database

Potential biomarker of circulating hsa-miR-1273g-3p level for detection of recurrent epithelial ovarian cancer

Çevik, Nazife (Arel Author)PurposeOvarian cancer (OC) is first gynaecologic cancer that causes women death and epithelial ovarian cancer (EOC) is the most lethal ovarian cancer type. While treatment is commonly successful, some cases (10-20%) show resistance to chemotherapy which is followed by recurrence. MicroRNA (miRNA) based diagnosis methods are slightly important for recurrent ovarian cancer diagnosis. We aimed to detect novel circulating miRNAs to be used as an early diagnosis and prediction tools for recurrent EOC.MethodsIn this study, recurrent EOC serum samples and healthy control serum samples were compared for miRNA expression analysis by microarray. Microarray results were analyzed by bioinformatics tools and differentially expressed hsa-miR-1273g-3p was obtained. After microarray analysis, differentially expressed hsa-miR-1273g-3p was validated by Real-Time PCR (RT-qPCR). The relation between target genes of hsa-miR-1273g-3p and ovarian cancer were examined by Pathway Studio((R)) (v.11.4.0.8).ResultsThe expression of hsa-miR-1273g-3p was found to be significantly down-regulated by t test Bonferroni FWER corrected p2, in recurrence EOC compare with healthy controls groups. The RT-qPCR results confirmed that relative expressions of the serum hsa-miR-1273g-3p were significantly down-regulated in patients with recurrent EOC (p=0.0275). Serum hsa-miR-1273g-3p levels could discriminate patients with recurrent EOC from healthy controls, with a power area under the curve (AUC) of 0.7.ConclusionThis study suggested that hsa-miR-1273g-3p plays a significant role in regulation of related genes, which are TNF-alfa, COL1A1, MMP-2, MMP-9, with recurrent EOC outcome. hsa-miR-1273g-3p may be used as a prognostic marker for recurrent EOC after chemotherapy

BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients' data were used to train the system using Mamdani's Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network's overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations' risk assessment in breast cancers as well as a unique tool for personalized medicine software

Germline landscape of BRCAs by 7-site collaborations as a BRCA consortium in Turkey

BRCA1/2 mutations play a significant role in cancer pathogenesis and predisposition particularly in breast, ovarian and prostate cancers. Thus, germline analysis of BRCA1 and BRCA2 is essential for clinical management strategies aiming at the identification of recurrent and novel mutations that could be used as a first screening approach. We analyzed germline variants of BRCA1/2 genes for 2168 individuals who had cancer diagnosis or high risk assessment due to BRCAs related cancers, referred to 10 health care centers distributed across 7 regions covering the Turkish landscape. Overall, 68 and 157 distinct mutations were identified in BRCA1 and BRCA2, respectively. Twenty-two novel variants were reported from both genes while BRCA2 showed higher mutational heterogeneity. We herein report the collective data as BRCA Turkish consortium that confirm the molecular heterogeneity in BRCAs among Turkish population, and also as the first study presenting the both geographical, demographical and gene based landscape of all recurrent and novel mutations which some might be a founder effect in comparison to global databases. This wider perspective leads to the most accurate variant interpretations which pave the way for the more precise and efficient management affecting the clinical and molecular aspects

Clinical and molecular evaluation of MEFV gene variants in the Turkish population: a study by the National Genetics Consortium

Familial Mediterranean fever (FMF) is a monogenic autoinflammatory disorder with recurrent fever, abdominal pain, serositis, articular manifestations, erysipelas-like erythema, and renal complications as its main features. Caused by the mutations in the MEditerranean FeVer (MEFV) gene, it mainly affects people of Mediterranean descent with a higher incidence in the Turkish, Jewish, Arabic, and Armenian populations. As our understanding of FMF improves, it becomes clearer that we are facing with a more complex picture of FMF with respect to its pathogenesis, penetrance, variant type (gain-of-function vs. loss-of-function), and inheritance. In this study, MEFV gene analysis results and clinical findings of 27,504 patients from 35 universities and institutions in Turkey and Northern Cyprus are combined in an effort to provide a better insight into the genotype-phenotype correlation and how a specific variant contributes to certain clinical findings in FMF patients. Our results may help better understand this complex disease and how the genotype may sometimes contribute to phenotype. Unlike many studies in the literature, our study investigated a broader symptomatic spectrum and the relationship between the genotype and phenotype data. In this sense, we aimed to guide all clinicians and academicians who work in this field to better establish a comprehensive data set for the patients. One of the biggest messages of our study is that lack of uniformity in some clinical and demographic data of participants may become an obstacle in approaching FMF patients and understanding this complex disease