BACKGROUNDPolycystic ovary syndrome (PCOS), a common endocrine disorder in reproductive-aged women, is correlated with obesity and insulin resistance (IR), androgens excess, chronic anovulation, and infertility. MicroRNAs (miRNAs) are small, single-stranded, noncoding RNA molecules that participate in inflammation, reproduction and metabolism, may contribute to PCOS. Current study aiming to manifest the correlation of body mass index (BMI) and testosterone (T) with miR-103 expression before and after fat loss.METHODS46 controls (N = 23 with BMI < 24 kg/m², N = 23 with BMI ≥ 28 kg/m²) and 46 patients with PCOS (N = 23 with BMI < 24 kg/m², N = 23 with BMI ≥ 28 kg/m²) aged between 20 and 30 were recruited. Waist-to-hip (WHR) and Body fat% (BF%) was measured and calculated. Serum hormones, serum lipid, metabolism parameters, and serum miR-103 were measured. All the assessments were measured before and after fat loss in a three-month intervention period.RESULTSmiR-103 was correlated with BMI rather than testosterone (T), and there was a significant difference between the non-obese and obese groups in miR-103 expression. Compared to before fat loss, miR-103 expression showed a slight downward trend.CONCLUSIONSSerum miR-103 differentially expressed between controls and PCOS subjects, miR-103 was positively correlated with BMI. There was significant difference between the non-obese and obese groups in miR-103 expression.

2016-12-01·Biomarker Research3区 · 医学

Plasma microRNA biomarker detection for mild cognitive impairment using differential correlation analysis

3区 · 医学

ArticleOA

作者: Shumpei Niida ; Mitsunori Kayano ; Jun Ichi Satoh ; Sayuri Higaki ; Etsuro Matsubara ; Kenji Matsumoto ; Osamu Takikawa

BACKGROUNDMild cognitive impairment (MCI) is an intermediate state between normal aging and dementia including Alzheimer's disease. Early detection of dementia, and MCI, is a crucial issue in terms of secondary prevention. Blood biomarker detection is a possible way for early detection of MCI. Although disease biomarkers are detected by, in general, using single molecular analysis such as t-test, another possible approach is based on interaction between molecules.RESULTSDifferential correlation analysis, which detects difference on correlation of two variables in case/control study, was carried out to plasma microRNA (miRNA) expression profiles of 30 age- and race-matched controls and 23 Japanese MCI patients. The 20 pairs of miRNAs, which consist of 20 miRNAs, were selected as MCI markers. Two pairs of miRNAs (hsa-miR-191 and hsa-miR-101, and hsa-miR-103 and hsa-miR-222) out of 20 attained the highest area under the curve (AUC) value of 0.962 for MCI detection. Other two miRNA pairs that include hsa-miR-191 and hsa-miR-125b also attained high AUC value of ≥ 0.95. Pathway analysis was performed to the MCI markers for further understanding of biological implications. As a result, collapsed correlation on hsa-miR-191 and emerged correlation on hsa-miR-125b might have key role in MCI and dementia progression.CONCLUSIONDifferential correlation analysis, a bioinformatics tool to elucidate complicated and interdependent biological systems behind diseases, detects effective MCI markers that cannot be found by single molecule analysis such as t-test.

PLoS ONE3区 · 综合性期刊

Determination of Reference microRNAs for Relative Quantification in Porcine Tissues

3区 · 综合性期刊

ArticleOA

作者: Ingrid Balcells ; Armand Sánchez ; Anna Castelló ; Sarai Córdoba ; Oriol Timoneda

Relative quantification is the strategy of choice for processing RT-qPCR data in microRNAs (miRNAs) expression studies. Normalisation of relative quantification data is performed by using reference genes. In livestock species, such as pigs, the determination of reference miRNAs and the optimal number of them has not been widely studied. In this study, the stability of ten miRNAs (Ssc-let-7a, Ssc-miR-103, Ssc-miR-17-3p, Hsa-miR-25, Hsa-miR-93, Ssc-miR-106a, Ssc-miR-191, Ssc-miR-16, Ssc-miR-26a and Ssc-miR-17-5p) was investigated by RT-qPCR in different tissues (skeletal muscle, kidney, liver, ovary and uterus) and in different pig breeds (Iberian, Landrace, Large White, Meishan and Vietnamese) as variation factors. Stability values were calculated with geNorm and NormFinder algorithms obtaining high correlation between them (r(2) = 0.99). The analyses showed that tissue is an important variability factor in miRNAs expression stability whereas breed is not a determinant factor. All ten miRNAs analysed had good stability values and, therefore, can be used as reference miRNAs. When all tissues were considered, miR-93 was the most stable miRNA. Dividing data set by tissues, let-7a was the most stable in skeletal muscle and ovary, miR-17-5p in kidney, miR-26a in liver and miR-103 in uterus. Moreover, the optimal number of reference miRNAs to be used for proper normalisation data was determined. It is suggested the use of five reference miRNAs (miR-93, miR-25, miR-106a, miR-17-5p and miR-26a) in multi-tissue experimental designs and the use of three reference miRNAs as the optimal number in single tissues studies (let-7a, miR-17-5p and miR-25 in skeletal muscle; miR-17-5p, miR-93 and miR-26a in kidney, miR-26a, miR-103 and let-7a in liver, let-7a, miR-25 and miR-106a in ovary and miR-103, let-7a and miR-93 in uterus). Overall, this study provides valuable information about the porcine reference miRNAs that can be used in order to perform a proper normalisation when relative quantification by RT-qPCR studies is undertaken.

100 项与 FIT-103 相关的药物交易

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