Significant association between lncRNA H19 polymorphisms and cancer susceptibility: a meta-analysis.

Previous epidemiological research suggests polymorphisms in long non-coding RNA (lncRNA) H19 are associated with an increased risk of cancer, but the results are inconsistent. We therefore conducted a meta-analysis to more accurately determine the association between lncRNA H19 polymorphisms and cancer risk. The PubMed, Embase, and Science Citation Index online databases were searched and 11 relevant studies involving a total of 33,209 participants were identified. Odds ratios (ORs) and corresponding 95% confidence interval (CIs) from these studies were used to detect associations between H19 polymorphisms and cancer risk using five genetic models. The pooled result suggested that the rs2839698 G>A polymorphism was associated with digestive cancer risk in all five models. Moreover, a protective effect against cancer development was observed for the T allele variant of the rs2107425 C>T polymorphism, especially in Caucasian patient populations. No significant associations were found between lncRNA H19 rs217727 G>A polymorphism and cancer risk. In summary, the rs2839698 G>A and rs2107425 C>T polymorphisms in lncRNA H19 may therefore play opposing roles during cancer development, and their effects may vary depending on cancer type and patient ethnicity.

A novel urinary metabolite signature for diagnosing major depressive disorder.

Major depressive disorder (MDD) is a prevalent and debilitating mental disorder. Yet, there are no objective biomarkers available to support diagnostic laboratory testing for this disease. Here, gas chromatography-mass spectrometry was applied to urine metabolic profiling of 126 MDD and 134 control subjects. Orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to identify the differential metabolites in MDD subjects relative to healthy controls. The OPLS-DA analysis of data from training samples (82 first-episode, drug-naïve MDD subjects and 82 well-matched healthy controls) showed that the depressed group was significantly distinguishable from the control group. Totally, 23 differential urinary metabolites responsible for the discrimination between the two groups were identified. Postanalysis, 6 of the 23 metabolites (sorbitol, uric acid, azelaic acid, quinolinic acid, hippuric acid, and tyrosine) were defined as candidate diagnostic biomarkers for MDD. Receiver operating characteristic analysis of combined levels of these six biomarkers yielded an area under the receiver operating characteristic curve (AUC) of 0.905 in distinguishing training samples; this simplified metabolite signature classified blinded test samples (44 MDD subjects and 52 healthy controls) with an AUC of 0.837. Furthermore, a composite panel by the addition of previously identified urine biomarker (N-methylnicotinamide) to this biomarker panel achieved a more satisfactory accuracy, yielding an AUC of 0.909 in the training samples and 0.917 in the test samples. Taken together, these results suggest this composite urinary metabolite signature should facilitate development of a urine-based diagnostic test for MDD.