Numerous studies have shown that mammalian target of rapamycin (mTOR) inhibitor activates Akt signaling pathway via a negative feedback loop while inhibiting mTORC1 signaling. In this report, we focused on studying the role of mTORC1 and mTORC2 in rapamycin-mediated Akt and ERK phosphorylation, and the antitumor effect of rapamycin in cancer cells in combination with Akt and ERK inhibitors. Moreover, we analyzed the effect of mTORC1 and mTORC2 on regulating cell cycle progression. We found that low concentrations rapamycin increased Akt and ERK phosphorylation through a mTORC1-dependent mechanism because knockdowned raptor induced the activation of Akt and ERK, but higher doses of rapamycin inhibited Akt and ERK phosphorylation mainly via the mTORC2 signaling pathway because that the silencing of rictor led to the inhibition of Akt and ERK phosphorylation. We further showed that mTORC2 was tightly associated with the development of cell cycle through an Akt-dependent mechanism. Therefore, we combined PI3K and ERK inhibitors prevent rapamycin-induced Akt activation and enhanced antitumor effects of rapamycin. Collectively, we conclude that mTORC2 plays a much more important role than mTORC1 in rapamycin-mediated phosphorylation of Akt and ERK, and cotargeting AKT and ERK signaling may be a new strategy for enhancing the efficacy of rapamycin-based therapeutic approaches in cancer cells.

Circular RNAs (circRNAs), which are single-stranded closed-loop RNA molecules lacking terminal 5′ caps and 3′ poly(A) tails, are attracting increasing scientific attention for their crucial regulatory roles in the occurrence and development of various diseases. With the rapid development of high-throughput sequencing technologies, increasing numbers of differentially expressed circRNAs have been identified in bladder cancer (BCa) via exploration of the expression profiles of BCa and normal tissues and cell lines. CircRNAs are critically involved in BCa biological behaviours, including cell proliferation, tumour growth suppression, cell cycle arrest, apoptosis, invasion, migration, metastasis, angiogenesis, and cisplatin chemoresistance. Most of the studied circRNAs in BCa regulate cancer biological behaviours via miRNA sponging regulatory mechanisms. CircRNAs have been reported to be significantly associated with many clinicopathologic characteristics of BCa, including tumour size, grade, differentiation, and stage; lymph node metastasis; tumour numbers; distant metastasis; invasion; and recurrence. Moreover, circRNA expression levels can be used to predict BCa patients’ survival parameters, such as overall survival (OS), disease-free survival (DFS), and progression-free survival (PFS). The abundance, conservation, stability, specificity and detectability of circRNAs render them potential diagnostic and prognostic biomarkers for BCa. Additionally, circRNAs play crucial regulatory roles upstream of various signalling pathways related to BCa carcinogenesis and progression, reflecting their potential as therapeutic targets for BCa. Herein, we briefly summarize the expression profiles, biological functions and mechanisms of circRNAs and the potential clinical applications of these molecules for BCa diagnosis, prognosis, and targeted therapy.

The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.