The differentially expressed circular ribonucleic acids of primary hepatic carcinoma following liver transplantation as new diagnostic biomarkers for primary hepatic carcinoma

RNMFLP: Predicting circRNA-disease associations based on robust nonnegative matrix factorization and label propagation

Circular RNAs (circRNAs) are a class of structurally stable endogenous noncoding RNA molecules. Increasing studies indicate that circRNAs play vital roles in human diseases. However, validating disease-related circRNAs in vivo is costly and time-consuming. A reliable and effective computational method to identify circRNA-disease associations deserves further studies. In this study, we propose a computational method called RNMFLP that combines robust nonnegative matrix factorization (RNMF) and label propagation algorithm (LP) to predict circRNA-disease associations. First, to reduce the impact of false negative data, the original circRNA-disease adjacency matrix is updated by matrix multiplication using the integrated circRNA similarity and the disease similarity information. Subsequently, the RNMF algorithm is used to obtain the restricted latent space to capture potential circRNA-disease pairs from the association matrix. Finally, the LP algorithm is utilized to predict more accurate circRNA-disease associations from the integrated circRNA similarity network and integrated disease similarity network, respectively. Fivefold cross-validation of four datasets shows that RNMFLP is superior to the state-of-the-art methods. In addition, case studies on lung cancer, hepatocellular carcinoma and colorectal cancer further demonstrate the reliability of our method to discover disease-related circRNAs.

Dynamic Metabolomics Study of the Bile Acid Pathway During Perioperative Primary Hepatic Carcinoma Following Liver Transplantation

Background

There are many situations of abnormal metabolism influencing liver graft function. This study aims to provide data for the development of liver function recovery after liver transplantation by dynamically analyzing metabolites of bile acids pathway in serum.

Material/Methods

A comprehensive metabolomics profiling of serum of 9 liver transplantation patients before transplantation, on the 1^st, 3^rd, and 7^th days after liver transplantation, and healthy individuals were performed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Multivariate data and dynamic analysis were used to search for biomarkers between the metabolomics profiles present in perioperative liver transplantation and normal controls.

Results

Thirty-three differential endogenous metabolites were screened by the threshold of variable importance in the projection (VIP) from an orthogonal partial least square discriminant analysis (OPLS-DA) greater than 1.0, q-value <0.05, and fold change (FC) ≤0.8 or ≥1.2 between the preoperative group and the normal controls in negative mode. The metabolite intensities of taurocholic acid, taurochenodeoxycholic acid, chenodeoxycholic acid glycine conjugate, and glycocholic acid pre-transplantation were significantly higher than those of normal controls. The average metabolite intensities of taurocholic acid and taurochenodesoxycholic acid on the first day after liver transplantation were lower than those observed pre-transplantation. The average metabolite intensities on day 3 after liver transplantation showed a sudden increase and then decreased after 7 postoperative days. The average metabolite intensities of glycocholic acid and chenodeoxycholic acid glycine conjugate showed an increasing trend on the 1^st, 3^rd, and 7^th days after liver transplantation.

Conclusions

Use of taurocholic acid and taurochenodeoxycholic acid-related bile secretion, liver regeneration, and de novo bile acid synthesis may help clinical evaluation and provide data for the development of liver function recovery after liver transplantation.

Circular RNA expression and association with the clinicopathological characteristics in papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Circular RNAs (circRNAs) are a novel class of RNAs, with higher stability and tissue specificity, which may be of value as novel clinical markers. High-throughput RNA sequencing was used to profile the expression of circRNAs in 5 pairs of cancer and normal tissues, and reverse transcription-quantitative PCR (RT-qPCR) analysis was employed to verify the results of the RNA sequencing in 45 cases of PTC. The dysregulated circRNA expression and clinicopathological characteristics were assessed and the potential roles of circRNAs in the cellular miRNA and mRNA network were predicted using bioinformatics analysis. The results demonstrated that, compared with normal tissues, a total of 53 circRNAs were dysregulated in tumour tissues, and 8 circRNAs were validated at the mRNA level (P<0.001 and P<0.01). Among those, the expression of chr5:161330882-161336769- (P=0.015), chr9:22046750-22097364+ (P=0.041) and chr8:18765448-18804898- (P=0.036) were obviously associated with the BRAF^V600E mutation, chr12:129699809-129700698- was associated with capsular invasion (P=0.025) and chr5:38523418-38530666- was associated with pT stage (P=0.037) and lymph node metastasis (P=0.002). Therefore, some dysregulated circRNAs were found to be associated with BRAF^V600E mutation, capsular invasion, advanced pT stage and lymph node metastasis of PTC, indicating that circRNAs may be involved in tumourigenesis and cancer progression, and they may be putative biomarkers for the diagnosis and evaluation of progression of PTC.

iCircDA-MF: identification of circRNA-disease associations based on matrix factorization

Circular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-loop structure, which play critical roles in various biological processes. Identifying associations between circRNAs and diseases is critical for exploring the complex disease mechanism and facilitating disease-targeted therapy. Although several computational predictors have been proposed, their performance is still limited. In this study, a novel computational method called iCircDA-MF is proposed. Because the circRNA-disease associations with experimental validation are very limited, the potential circRNA-disease associations are calculated based on the circRNA similarity and disease similarity extracted from the disease semantic information and the known associations of circRNA-gene, gene-disease and circRNA-disease. The circRNA-disease interaction profiles are then updated by the neighbour interaction profiles so as to correct the false negative associations. Finally, the matrix factorization is performed on the updated circRNA-disease interaction profiles to predict the circRNA-disease associations. The experimental results on a widely used benchmark dataset showed that iCircDA-MF outperforms other state-of-the-art predictors and can identify new circRNA-disease associations effectively.