Publications by authors named "Emir Sehovic"

Background: Puberty is a highly heritable and variable trait, with environmental factors having a role in its eventual timing and development. Early and late pubertal onset are both associated with various diseases developing later in life, and epigenetic characterisation of pubertal timing and development could lead to important insights. Blood DNA methylation, reacting to both genotype and environment, has been associated with puberty; however, such studies are relatively scarce.

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Mammographic breast cancer screening is effective in reducing breast cancer mortality. Nevertheless, several limitations are known. Therefore, developing an alternative or complementary non-invasive tool capable of increasing the accuracy of the screening process is highly desirable.

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Background: Breast cancer (BC) is the most frequently diagnosed cancer among women. Numerous studies explored cell-free circulating microRNAs as diagnostic biomarkers of BC. As inconsistent and rarely intersecting microRNA panels have been reported thus far, we aim to evaluate the overall diagnostic performance as well as the sources of heterogeneity between studies.

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Overall lower incidence and better prognosis are observed in female melanoma patients compared to males. As sex and stage differences in the context of melanoma gene expression are understudied, we aim to highlight them through statistical analysis of melanoma gene expression datasets. Data from seven online datasets, including normal skin, commonly acquired nevi, and melanomas, were collected and analyzed.

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by major social, communication and behavioural challenges. The cause of ASD is still unclear and it is assumed that environmental, genetic and epigenetic factors influence the risk of ASD occurrence. MicroRNAs (miRNAs) are short 21-25 nucleotide long RNA molecules which post-transcriptionally regulate gene expression.

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Comparison and detection of stable cancer genes across cancer types is of interest. The gene expression data of 6 different cancer types (colon, breast, lung, ovarian, brain and renal) and a control group from The Cancer Genome Atlas (TCGA) database were used in this study. The comparison of gene expression data together with the calculation standard deviations of such data was completed using a statistical model for the detection of stable genes.

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