BACKGROUND: It has been shown that circular RNA (circRNA) is associated with human cancers, however, few studies have been reported in hepatocellular carcinoma (HCC). OBJECTIVE: To estimate clinical values of a circular RNA, Hsa_circ_0001649, in HCC.

Tumor cells with stemness (stem‐cell) features contribute to initiation and progression of hepatocellular carcinoma (HCC), but involvement of long noncoding RNAs (lncRNAs) remains largely unclear. Genome‐wide analyses were applied to identify tumor‐associated lncRNA‐DANCR. DANCR expression level and prognostic values of DANCR were assayed in two HCC cohorts (China and Korea, n = 135 and 223). Artificial modulation of DANCR (down‐ and overexpression) was done to explore the role of DANCR in tumorigenesis and colonization, and tumor‐bearing mice were used to determine therapeutic effects. We found that lncRNA‐DANCR is overexpressed in stem‐like HCC cells, and this can serve as a prognostic biomarker for HCC patients. Experiments showed that DANCR markedly increased stemness features of HCC cells to promote tumorigenesis and intra‐/extrahepatic tumor colonization. Conversely, DANCR knockdown attenuated the stem‐cell properties and in vivo interference with DANCR action led to decreased tumor cell vitality, tumor shrinkage, and improved mouse survival. Additionally, we found that the role of DANCR relied largely on an association with, and regulation of, CTNNB1. Association of DANCR with CTNNB1 blocked the repressing effect of microRNA (miR)−214, miR‐320a, and miR‐199a on CTNNB1. This observation was confirmed in vivo, suggesting a novel mechanism of tumorigenesis involving lncRNAs, messenger RNAs, and microRNAs. Conclusions: These studies reveal a significance and mechanism of DANCR action in increasing stemness features and offer a potential prognostic marker and a therapeutic target for HCC. (Hepatology 2016;63:499–511)

Sarcopenia has become a heavy disease burden among the elderly. Lipid metabolism was reported to be involved in many degenerative diseases. This study aims to investigate the association between dysregulated lipid metabolism and sarcopenia in geriatric inpatients. This cross-sectional study included 303 patients aged ≥ 60, of which 151 were diagnosed with sarcopenia. The level of total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), homocysteine (HCY), BMI, and fat percentage, were compared between sarcopenia and non-sarcopenia patients. The Spearman correlation coefficient was used to estimate the association between sarcopenia and the level of lipid metabolism. To determine risk factors related to sarcopenia, a multivariate logistic regression analysis was carried out. Risk prediction models were constructed based on all possible data through principal component analysis (PCA), Logistic Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbor (KNN), and eXtreme Gradient Boosting (XGboost). We observed rising prevalence of sarcopenia with increasing age, decreasing BMI, and fat percentage (p < 0.001, Cochran Armitage test). Multivariate logistic regression analysis revealed sarcopenia’s risk factors, including older age, male sex, lower levels of BMI, TC, and TG, and higher levels of LDL and HCY (p < 0.05). The sarcopenia risk prediction model showed the risk prediction value of sarcopenia, with the highest area under the receiver operating curve (AUC) of 0.775. Our study provided thorough insight into the risk factors associated with sarcopenia. It demonstrated that an increase in lipid metabolism-related parameters (BMI, TG, TC), within normal reference ranges, may be protective against sarcopenia. The present study can illuminate the direction and significance of lipid metabolism-related factors in preventing sarcopenia.

Potential diagnostic and prognostic marker dimethylglycine dehydrogenase (DMGDH) suppresses hepatocellular carcinoma metastasis in vitro and in vivo

Knowledge Graph (KG) is becoming increasingly important in the biomedical field. Deriving new and reliable knowledge from existing knowledge by KG embedding technology is a cutting-edge method. Some add a variety of additional information to aid reasoning, namely multimodal reasoning. However, few works based on the existing biomedical KGs are focused on specific diseases. This work develops a construction and multimodal reasoning process of Specific Disease Knowledge Graphs (SDKGs). We construct SDKG-11, a SDKG set including five cancers, six non-cancer diseases, a combined Cancer5 and a combined Diseases11, aiming to discover new reliable knowledge and provide universal pre-trained knowledge for that specific disease field. SDKG-11 is obtained through original triplet extraction, standard entity set construction, entity linking and relation linking. We implement multimodal reasoning by reverse-hyperplane projection for SDKGs based on structure, category and description embeddings. Multimodal reasoning improves pre-existing models on all SDKGs using entity prediction task as the evaluation protocol. We verify the model’s reliability in discovering new knowledge by manually proofreading predicted drug–gene, gene–disease and disease–drug pairs. Using embedding results as initialization parameters for the biomolecular interaction classification, we demonstrate the universality of embedding models. The constructed SDKG-11 and the implementation by TensorFlow are available from https://github.com/ZhuChaoY/SDKG-11. Supplementary data are available at Bioinformatics online.