Mutation status coupled with RNA-sequencing data can efficiently identify important non-significantly mutated genes serving as diagnostic biomarkers of endometrial cancer

Metabolic signatures of hepatolithiasis using ultra-high performance liquid chromatography-tandem mass spectrometry

BACKGROUND & AIMS

A metabolomic study of hepatolithiasis has yet to be performed. The purpose of the present study was to characterize the metabolite profile and identify potential biomarkers of hepatolithiasis using a metabolomic approach.

METHODS

We comprehensively analyzed the serum metabolites from 30 patients with hepatolithiasis and 20 healthy individuals using ultra-high performance liquid chromatography-tandem mass spectrometry operated in negative and positive ionization modes. Statistical analyses were performed using univariate (Student's t-test) and multivariate (orthogonal partial least-squares discriminant analysis) statistics and R language. Receiver operator characteristic (ROC) curve analysis was performed to identify potential predictors of hepatolithiasis.

RESULTS

We identified 277 metabolites that were significantly different between hepatolithiasis serum group and healthy control serum group. These metabolites were principally lipids and lipid-like molecules and amino acid metabolites. The steroid hormone biosynthesis pathway was enriched in hepatolithiasis serum group. In all specific metabolites, 75 metabolites were over-expressed in hepatolithiasis serum group. The AUC values for 60 metabolites exceeded 0.70, 4 metabolites including 18-β-Glycyrrhetinic acid, FMH, Rifampicin and PC (4:0/16:2) exceeded 0.90.

CONCLUSIONS

We have identified serum metabolites that are associated with hepatolithiasis for the first time. 60 potential metabolic biomarkers were identified, 18-β-Glycyrrhetinic acid, FMH, Rifampicin and PC (4:0/16:2) may have the potential clinical utility in hepatolithiasis.

Hyperhomocysteinemia-related serum metabolome alterations not normalized by short-term folic acid treatment

INTRODUCTION

Hyperhomocysteinemia (HHCys) is an independent risk factor for various diseases such as cardiovascular diseases, Alzheimer's, and cancers. Folate deficiency is one of the significant reasons for HHCys. However, it is not known whether folate deficiency with HHCys is associated with any serum metabolites.

OBJECTIVES

Our objective was to identify the metabolic alterations in people having folate deficiency with HHCys and check whether a short-term folic acid therapy could reverse those metabolic changes.

METHODS

The study enrolled 34 participants aged between 18 and 40 years having folate deficiency (< 4.6 ng/mL) with HHCys (> 15 μmol/L) and 21 normal healthy individuals. A short-term intervention of oral folic acid (5 mg/day) was done in the HHCys group for 30 days. Untargeted metabolomics analysis of serum was performed in all study subjects before and after the folic acid treatment. Different univariate methods and the multivariable-adjusted linear regression models were employed to determine an association between homocysteine level and metabolite profile.

RESULTS

Metabolomics analysis data showed that many metabolites involved in the biochemical pathways of lipid metabolisms such as polyunsaturated fatty acids, glycerolipids, and phospholipids were downregulated in the HHCys group. Short-term oral folic acid therapy significantly reduced their serum homocysteine level. However, the metabolic pathway alterations observed in folate-deficient HHCys-condition were unaltered even after the folic acid treatment.

CONCLUSIONS

Our study revealed that people who have a folic acid deficiency with HHCys have an altered metabolite profile related to lipid metabolism, which cannot be reversed by short-term folic acid therapy.

Ultrasensitive Detection of RNA with Single-Base Resolution by Coupling Electrochemical Sensing Strategy with Chimeric DNA Probe-Aided Ligase Chain Reaction

Accurate and sensitive detection of single-base mutations in RNAs is of great value in basic studies of life science and medical diagnostics. However, the current available RNA detection methods are challenged by heterogeneous clinical samples in which trace RNA mutants usually existed in a large pool of normal wild sequences. Thus, there is still great need for developing the highly sensitive and highly specific methods in detecting single-base mutations of RNAs in heterogeneous clinical samples. In the present study, a new chimeric DNA probe-aided ligase chain reaction-based electrochemical method (cmDNA-eLCR) was developed for RNA mutation detection through the BSA-based carrier platform and the horseradish peroxidase-hydrogen peroxide-tetramethylbenzidine (HRP-H₂O₂-TMB) system. The denaturing polyacrylamide gel electrophoresis and a fluorophore-labeled probe was ingeniously designed to demonstrate the advantage of cmDNA in ligation to normal DNA templated by RNA with the catalysis of T4 RNA ligase 2 as well as its higher selectivity than DNA ligase system. Finally, the proposed cmDNA-eLCR, compared with the traditional eLCR, showed excellent performance in discriminating single base-mismatched sequences, where the signal response for mismatched targets at a high concentration could overlap completely with that for the blank control. Besides, this cmDNA-eLCR assay had a wide linear range crossing six orders of magnitude from 1.0 × 10^-15 to1.0 × 10^-10 M with a limit of detection as low as 0.6 fM. Furthermore, this assay was applied to detect RNA in real sample with a satisfactory result, thereby demonstrating its great potential in diagnosis of RNA-related diseases.

A 7-lncRNA signature predict prognosis of Uterine corpus endometrial carcinoma

Current research indicate that long noncoding RNAs (lncRNAs) are associated with the progression of various cancers and can be used as prognostic biomarkers. This study aims to construct a prognostic lncRNA signature for the risk assessment of Uterine corpus endometrial carcinoma (UCEC). The RNA-Seq expression profile and corresponding clinical data of UCEC patients obtained from The Cancer Genome Atlas database. First, some prognosis-related lncRNAs were obtained by univariate Cox analysis. The minimum absolute contraction and selection operator (LASSO) regression and the Cox proportional hazard regression method were used to further identify the lncRNA prognostic model. Finally, seven lncRNAs (AC110491.1, AL451137.1, AC005381.1, AC103563.2, AC007422.2, AC108025.2, and MIR7-3HG) were identified as potential prognostic factors. According to the model constructed by the above analysis, the risk score of each UCEC patient was calculated, and the patients were classified into high and low-risk groups. The low-risk group had significant survival benefits. Moreover, we constructed a nomogram that incorporated independent prognostic factors (age, tumor stage, tumor grade, and risk score). The c-index value for evaluating the predictive nomogram model was 0.801. The area under the curve was 0.797 (3-year survival). The calibration curve also showed that there was a satisfactory agreement between the predicted and observed values in the probability of 1-, 3-, and 5-year overall survival. On the basis of the coexpression relationship, we established a coexpression network of lncRNA-messenger RNA (mRNA) of the 7-lncRNA. The Kyoto Encyclopedia of Genes and Genomes analysis of the coexpressing mRNAs showed that the main pathways related to the 7-lncRNA signature were neuroactive ligand-receptor interaction, serotonergic synapse, and gastric cancer pathway. Therefore, our study revealed that the 7-lncRNA could be used to predict the prognosis of UCEC and for postoperative treatment and follow-up.