BackgroundThe E3 ubiquitin ligase Fbxw7 functions as a general tumor suppressor by targeting several well-known oncoproteins for ubiquitination and proteasomal degradation. However, the clinical significance of Fbxw7 and the mechanisms involved in the anti-cancer effect of Fbxw7 in HCC are not clear.MethodThe Fbxw7 and YAP expression in 60 samples of surgical resected HCC and matched normal tumor-adjacent tissues were assessed using IHC or immunoblotting. Flow cytometry, caspase 3/7 activity assay, BrdU cell proliferation assay and MTT assay were used to detect proliferation and apoptosis of HCC cells. The regulatory effect of Fbxw7 on YAP in HCC cells was confirmed by qRT-PCR, immunoblotting and immunofluorescence. Co-immunoprecipitation was used to analyze interaction between YAP and Fbxw7. Nude mice subcutaneous injection, Ki-67 staining and TUNEL assay were used to evaluate tumor growth and apoptosis in vivo.ResultsIn this study, we found that Fbxw7 expression was impaired in HCC tissues and loss of Fbxw7 expression was correlated with poor clinicopathological features including large tumor size, venous infiltration, high pathological grading and advanced TNM stage. Additionally, we demonstrated that patients with positive Fbxw7 expression had a better 5-year survival and Fbxw7 was an independent factor for predicting the prognosis of HCC patients. We confirmed that Fbxw7 inhibited HCC by inducing both apoptosis and growth arrest. Elevated YAP expression was observed in the same cohort of HCC tissues. Pearson's correlation coefficient analysis indicated that Fbxw7 was inversely associated with YAP protein expression in HCC tissues. We also found that Fbxw7 regulated YAP protein abundance by targeting YAP for ubiquitination and proteasomal degradation in HCC. Furthermore, restoring YAP expression partially abrogated Fbxw7 induced HCC cell apoptosis and growth arrest in vitro and in vivo.ConclusionThese results indicate that Fbxw7 may serve as a prognostic marker and that YAP may be a potential target of Fbxw7 in HCC.

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Circular RNAs, as hopeful diagnosis markers and therapeutic molecules, have been studied, probed and applied into several diseases, such as cardiovascular diseases, systemic lupus erythematosus, leukemia, pulmonary tuberculosis, and cancer especially. Recently, mounting evidence has supported that circRNAs play a key role in the tumorigenesis, progress, invasion and metastasis in lung cancer. Its special structure-3′-5′ covalent loop-allow it to execute several special functions in both normal eukaryotic cells and cancer cells. Our review summaries the latest studies on characteristics and biogenesis of circRNAs, and highlight the regulatory functions about miRNA sponge of lungcancer-related circRNAs. In addition, the interaction of the circRNA-miRNA-mRNA regulatory network will also be elaborated in detail in this review. Therefore, this review can provide a new idea or strategy for further development and application in clinical setting in terms of early-diagnosis and better treatment.

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BackgroundCachexia is a multifactorial metabolic syndrome with high morbidity and mortality in patients with advanced cancer. The diagnosis of cancer cachexia depends on objective measures of clinical symptoms and a history of weight loss, which lag behind disease progression and have limited utility for the early diagnosis of cancer cachexia. In this study, we performed a nuclear magnetic resonance‐based metabolomics analysis to reveal the metabolic profile of cancer cachexia and establish a diagnostic model.MethodsEighty‐four cancer cachexia patients, 33 pre‐cachectic patients, 105 weight‐stable cancer patients, and 74 healthy controls were included in the training and validation sets. Comparative analysis was used to elucidate the distinct metabolites of cancer cachexia, while metabolic pathway analysis was employed to elucidate reprogramming pathways. Random forest, logistic regression, and receiver operating characteristic analyses were used to select and validate the biomarker metabolites and establish a diagnostic model.ResultsForty‐six cancer cachexia patients, 22 pre‐cachectic patients, 68 weight‐stable cancer patients, and 48 healthy controls were included in the training set, and 38 cancer cachexia patients, 11 pre‐cachectic patients, 37 weight‐stable cancer patients, and 26 healthy controls were included in the validation set. All four groups were age‐matched and sex‐matched in the training set. Metabolomics analysis showed a clear separation of the four groups. Overall, 45 metabolites and 18 metabolic pathways were associated with cancer cachexia. Using random forest analysis, 15 of these metabolites were identified as highly discriminating between disease states. Logistic regression and receiver operating characteristic analyses were used to create a distinct diagnostic model with an area under the curve of 0.991 based on three metabolites. The diagnostic equation was Logit(P) = −400.53 – 481.88 × log(Carnosine) −239.02 × log(Leucine) + 383.92 × log(Phenyl acetate), and the result showed 94.64% accuracy in the validation set.ConclusionsThis metabolomics study revealed a distinct metabolic profile of cancer cachexia and established and validated a diagnostic model. This research provided a feasible diagnostic tool for identifying at‐risk populations through the detection of serum metabolites.