Hepatocellular carcinoma (HCC), a common liver malignancy worldwide, has high morbidity and mortality. β-Thujaplicin, a tropolone derivative, has been used in some health-care products and clinical adjuvant drugs, but its use for HCC is unknown. In this study, we found that β-Thujaplicin inhibits the growth of HCC cells, but not normal liver cells, with nanomolar potency. Mechanistically, we found that β-Thujaplicin could induce autophagy, as judged by western blot, confocal microscopy, and transmission electron microscopy. Further using β-Thujaplicin combined with an autophagy blocker or agonist treatment HepG2 cells, we found that β-Thujaplicin induced autophagic cell death (ACD) mediated by ROS caused inhibition of the Akt-mTOR signaling pathway. Moreover, β-Thujaplicin triggered HepG2 apoptosis and increased cleaved PARP1, cleaved caspase-3, and Bax/Bcl-2 ratio, which indicated that β-Thujaplicin induced apoptosis mediated by the mitochondrial-dependent pathway. We also found that increased expression of p21 and decreased expression of CDK7, Cyclin D1, and Cyclin A2 participating in β-Thujaplicin caused the S-phase arrest. It seems that β-Thujaplicin exerts these functions by ROS-mediated p38/ERK MAPK but not by JNK signaling pathway activation. Consistent with in vitro findings, our in vivo study verified that β-Thujaplicin treatment significantly reduced HepG2 tumor xenograft growth. Taken together these findings suggest that β-Thujaplicin have an ability of anti-HCC cells and may conducively promote the development of novel anti-cancer agents.

BackgroundPancreatic adenocarcinoma (PAC) is one of the most fatal cancers due to its high degree of malignancy, increasing incidence, high mortality, and unsatisfactory treatment efficacy. Evidence has suggested that numerous microRNAs (miRNAs), including miR-126 and miR-34a, have potent tumor-suppressing effects on PAC, implicating a possible application of miRNA in tumor therapy. However, the therapeutic effect of a single miRNA on pancreatic cancer is limited.MethodsWe simultaneously delivered miR-126 and miR-34a into PAC cells by a carcinoembryonic antigen promoter-driven oncolytic adenovirus (AdCEAp-miR126/34a), and examined the antitumor efficacy of the therapeutic system in in vitro and in vivo experiments.ResultsIn vitro cytological experiments found that the expression levels of miR-126 and miR-34a were specifically increased in the AdCEAp-miR126/34a-infected PAC cells, and the antitumor efficacy was enhanced in aspects of cancer cell viability, migration, invasion, and apoptosis, by synergistically combining the antitumor effects of overexpressed miR-126 and miR-34a and the oncolytic effect of viral replication specifically in PAC cells. The expression levels of miR-126 target genes (vascular endothelial growth factor-A and SOX2) and miR-34a target genes (cyclin D1, E2F1, and Bcl-2) were markedly decreased in the PAC cells after being infected with AdCEAp-miR126/34a. Notable suppression of the therapeutic system on tumor growth was also proven in established PAC xenograft tumor models in nude mice, which demonstrated that the combination of miR-126 and miR-34a exerts more effective antitumor outcomes than a single miRNA.ConclusionThe therapeutic system co-expressing miR-126 and miR-34a mediated by oncolytic adenovirus is a promising system for PAC target therapy.

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Existing table question answering datasets contain abundant factual questions that primarily evaluate a QA system’s comprehension of query and tabular data. However, restricted by their short-form answers, these datasets fail to include question–answer interactions that represent more advanced and naturally occurring information needs: questions that ask for reasoning and integration of information pieces retrieved from a structured knowledge source. To complement the existing datasets and to reveal the challenging nature of the table-based question answering task, we introduce FeTaQA, a new dataset with 10K Wikipedia-based {table, question, free-form answer, supporting table cells} pairs. FeTaQA is collected from noteworthy descriptions of Wikipedia tables that contain information people tend to seek; generation of these descriptions requires advanced processing that humans perform on a daily basis: Understand the question and table, retrieve, integrate, infer, and conduct text planning and surface realization to generate an answer. We provide two benchmark methods for the proposed task: a pipeline method based on semantic parsing-based QA systems and an end-to-end method based on large pretrained text generation models, and show that FeTaQA poses a challenge for both methods.

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