Purpose: Animal studies suggested that there is an independent bone-osteocalcin-gonadal axis, except of the hypothalamic-pituitary-gonadal axis. Based on this hypothesis, the higher osteocalcin during the high bone turnover should be followed by higher testosterone formation. Yet such clinical evidence is limited. The patients with uncontrolled hyperthyroidism are proper model with high bone turnover. If this hypothesis is true, there should be high testosterone level in patients with uncontrolled hyperthyroidism. Therefore, Graves? disease patients were recruited to study the correlation between osteocalcin and testosterone. Materials and Methods: 50 male hyperthyroidism patients with Graves? disease and 50 health persons matched by age and gender were enrolled in our cross-section study. Serum markers for thyroid hormone, sex hormone and bone metabolic markers including free triiodothyronine (FT3), free thyroxine (FT4), thyroid-stimulating hormone (TSH), testosterone, luteinizing hormone (LH), follicle-stimulating hormone (FSH) and osteocalcin (OC), C-terminal telopeptide fragments of type I collagen (CTX) were examined. The demographic parameters such as duration of disease were also collected. All data was analyzed by SPSS 20.0. Results: High testosterone and osteocalcin level was observed in the hyperthyroidism patients (T 36.35?10.72?nmol/l and OC 46.79?26.83?ng/ml). In simple Pearson correlation, testosterone was positively associated with OC (r=0.486, P<0.001), and this positive relation still existed after adjusted for age, BMI, smoking, drinking, duration of disease, FT3, FT4, LH, FSH, CTX in multi-linear regression analysis (See Model 1?4). Conclusion: In male hyperthyroidism patients, osteocalcin was positively correlated with serum testosterone, which indirectly supports the hypothesis that serum osteocalcin participates in the regulation of sex hormone.

The interplay among microRNAs (miRNAs) plays an important role in the developments of complex human diseases. Co-expression networks can characterize the interactions among miRNAs. Differential correlation network is a powerful tool to investigate the differences of co-expression networks between cases and controls. To construct a differential correlation network,

Racial/ethnic and region disparities in incidence and mortality are obviously in liver cancer. Mongolia has the highest reported incidence and mortality of hepatocellular Carcinoma (HCC) in the world, while the incidence of HCC is relatively low in the United States, but differences in their molecular characteristics remain largely elusive. Here we report differentially expressed genes(DEGs) in Mongolian hepatocellular carcinoma and in Caucasian HCC and their intersection DEGs, as well as their corresponding signaling pathways in Mongolian and Caucasian hepatocellular carcinoma patients based on the transcriptome sequences from GEO database. We got 908 up-regulated genes and 1946 down-regulated genes in Mongolian HCC, 1244 up-regulated genes and 1912 down-regulated genes in Caucasian HCC, 254 Co-upregulated genes and 1035 co-downregulated genes in Mongolian and Caucasian. The results of GO enrichment analysis showed that most of the genes with altered expression levels in Mongolian HCC participate in biological processes that involve metabolic reprogramming of various substances, accounting for about one-third of all biological processes. In particular, multiple amino acid biosynthesis and metabolic processes appear to be specific in Mongolian HCC compared with Caucasian HCC. The biological processes they share include those in which most immune cells are involved and cell cycle-related biological processes. In addition, we also found the genes UPP2, PCK1, GLYAT, ASPDH, GNMT, ADH1B and HPD, encode for key metabolic enzymes, whose expression level up-regulated or down-regulated more than 5 times in Mongolian HCC and was dramatically correlated with survival in Mongolian HCC (p value < 0.01), More importantly, these molecules are potential targets for some metabolic antitumor drugs. This result may have important implications for the study of the pathogenic factors and molecular mechanisms of hepatocellular carcinoma and the precise therapy of Mongolian HCC.

We propose eight data transformations for RNA-seq data analysis aiming to make the transformed sample mean to be representative of the distribution center since it is not always possible to transform count data to satisfy the normality assumption. Simulation studies showed that limma based on transformed data by using the rv transformation (denoted as limma+rv) performed best compared with limma based on transformed data by using other transformation methods in term of high accuracy and low FNR, while keeping FDR at the nominal level. For large sample size, limma based on transformed data by using the 8 proposed transformation methods had similar performance to limma based on transformed data by using existing transformation methods for equal library size scenarios. Otherwise, limma based on transformed data by using the rv, lv, rv2, or lv2 transformation, or by using the existing voom transformation performed better than limma based on data from other transformation methods. Real data analysis results showed that limma+ l2 performed best, while limma+ rv also had good performance.