Aerobic glycolysis is a phenomenon by which malignant cells preferentially metabolize glucose through the glycolytic pathway, rather than oxidative phosphorylation to proliferate efficiently. The present study aimed to investigate the expression and functional implications of long non-coding (lnc)RNA Ftx in the aerobic glycolysis and tumorigenesis of hepatocellular carcinoma (HCC). It was identified that lncRNA Ftx was upregulated in human HCC tissues and cell lines and, notably, was associated with aggressive clinicopathological features. lncRNA Ftx overexpression promoted the proliferation, invasion and migration of HCC cells, whereas lncRNA Ftx knockdown resulted in the opposite effects. Furthermore, lncRNA Ftx affected the activity and expression of key enzymes in carbohydrate metabolism, suggesting that lncRNA Ftx may be involved in aerobic glycolysis in HCC. The measurement of glucose consumption, lactate production and glucose transporter expression further supported this assumption. Mechanistically, peroxisome proliferator-activated receptor γ (PPARγ) expression in human HCC tissues and cell lines was positively correlated with lncRNA Ftx. Inhibiting PPARγ in Huh7 cells partially abrogated the alterations in glucose uptake, lactate production and relative glycolytic enzyme expression induced by lncRNA Ftx; similarly, PPARγ activation in Bel-7402 cells partially rescued the lncRNA Ftx-mediated alterations. In conclusion, lncRNA Ftx is a promoter of the Warburg effect and tumor progression, partly via the PPARγ pathway, and may serve as a promising therapeutic target for HCC treatment.

Drug induced nephrotoxicity is a major clinical challenge, and it is always associated with higher costs for the pharmaceutical industry and due to detection during the late stages of drug development. It is desirable for improving the health outcomes for patients to distinguish nephrotoxic structures at an early stage of drug development. In this study, we focused on in silico prediction and insights into the structural basis of drug induced nephrotoxicity, based on reliable data on human nephrotoxicity. We collected 565 diverse chemical structures, including 287 nephrotoxic drugs on humans in the real world, and 278 non-nephrotoxic approved drugs. Several different machine learning and deep learning algorithms were employed for in silico model building. Then, a consensus model was developed based on three best individual models (RFR_QNPR, XGBOOST_QNPR, and CNF). The consensus model performed much better than individual models on internal validation and it achieved prediction accuracy of 86.24% external validation. The results of analysis of molecular properties differences between nephrotoxic and non-nephrotoxic structures indicated that several key molecular properties differ significantly, including molecular weight (MW), molecular polar surface area (MPSA), AlogP, number of hydrogen bond acceptors (nHBA), molecular solubility (LogS), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). These molecular properties may be able to play an important part in the identification of nephrotoxic chemicals. Finally, 87 structural alerts for chemical nephrotoxicity were mined with f-score and positive rate analysis of substructures from Klekota-Roth fingerprint (KRFP). These structural alerts can well identify nephrotoxic drug structures in the data set. The in silico models and the structural alerts could be freely accessed via https://ochem.eu/article/140251 and http://www.sapredictor.cn, respectively. We hope the results should provide useful tools for early nephrotoxicity estimation in drug development.