Primary biliary cholangitis (PBC) is an autoimmune liver disease with a strong hereditary component. Here, we report a genome-wide association study that included 1,122 PBC cases and 4,036 controls of Han Chinese descent, with subsequent replication in a separate cohort of 907 PBC cases and 2,127 controls. Our results show genome-wide association of 14 PBC risk loci including previously identified 6p21 (HLA-DRA and DPB1), 17q12 (ORMDL3), 3q13.33 (CD80), 2q32.3 (STAT1/STAT4), 3q25.33 (IL12A), 4q24 (NF-κB) and 22q13.1 (RPL3/SYNGR1). We also identified variants in IL21, IL21R, CD28/CTLA4/ICOS, CD58, ARID3A and IL16 as novel PBC risk loci. These new findings and histochemical studies showing enhanced expression of IL21 and IL21R in PBC livers (particularly in the hepatic portal tracks) support a disease mechanism in which the deregulation of the IL21 signalling pathway, in addition to CD4 T-cell activation and T-cell co-stimulation are critical components in the development of PBC.

Background. L-carnitine has been used for several years as an adjuvant therapy in oxidative stress, blood sugar, high-sensitivity C-reactive protein (CRP), anemia, etc. However, the efficacy of L-carnitine treating insulin resistance (IR) remains controversial. Homeostasis model assessment of Insulin Resistance (HOMA-IR) is widely used in the clinical evaluation of patients with IR. Objectives.A meta-analysis, including randomized controlled trials (RCTs), was performed to assess the effect of L-carnitine on HOMA-IR patients.Material and methods. The Cochrane Library, PubMed, and EMBASE databases were systematically searched to identify RCTs which evaluated the effects of L-carnitine on HOMA-IR patients. We screened relevant studies according to predefined inclusion and exclusion criteria. In the selected articles, we extracted the data: study design, sample size, age, L-carnitine dose and regimen, body mass index (BMI) of patients, mode of administration, study duration and study outcomes.Results. A total of 5 studies were included for the meta-analysis. The result showed L-carnitine was useful in the treatment of IR (WMD -0.724, CI -0.959 -0.488, p < 0.0001). Evaluation at 3, 6, 9, 12 months, the p-values were 0.875, 0.165, 0.031, 0, 007, respectively.Conclusions. L-carnitine was useful in treating patients with IR. L-carnitine can treat IR more effectively with prolonging the medication time. However, more RCTs with long-term L-carnitine treatment of IR are needed to confirm the viewpoint.

AimEarly diagnosis of prostate cancer (PCa), which is a clinically heterogeneous-multifocal disease, is essential to improve the prognosis of patients. However, published PCa diagnostic markers share little overlap and are poorly validated using independent data. Therefore, we here developed an integrative proteomics and interaction network-based classifier by combining the differential protein expression with topological features of human protein interaction networks to enhance the ability of PCa diagnosis.Methods and ResultsBy two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) coupled with MS using PCa and adjacent benign tissues of prostate, a total of 60 proteins with the differential expression in PCa tissues were identified as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and three hub proteins (PTEN, SFPQ and HDAC1) were chosen. After that, a PCa diagnostic classifier was constructed by support vector machine (SVM) modeling based on the microarray gene expression data of the genes which encode the hub proteins mentioned above. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.96∼90.18%) and area under ROC curve (approximating 1.0). Furthermore, the clinical significance of PTEN, SFPQ and HDAC1 proteins in PCa was validated by both ELISA and immunohistochemistry analyses. More interestingly, PTEN protein was identified as an independent prognostic marker for biochemical recurrence-free survival in PCa patients according to the multivariate analysis by Cox Regression.ConclusionsOur data indicated that the integrative proteomics and interaction network-based classifier which combines the differential protein expression and topological features of human protein interaction network may be a powerful tool for the diagnosis of PCa. We also identified PTEN protein as a novel prognostic marker for biochemical recurrence-free survival in PCa patients.