Romidepsin (FK228), a histone deacetylase inhibitor (HDACi), has anti-tumor effects against several types of solid tumors. Studies have suggested that HDACi could upregulate PD-L1 expression in tumor cells and change the state of anti-tumor immune responses in vivo. However, the influence of enhanced PD-L1 expression in tumor cells induced by romidepsin on anti-tumor immune responses is still under debate. So, the purpose of this study was to explore the anti-tumor effects and influence on immune responses of romidepsin in colon cancer. The results indicated that romidepsin inhibited proliferation, induced G0/G1 cell cycle arrest and increased apoptosis in CT26 and MC38 cells. Romidepsin treatment increased PD-L1 expression in vivo and in vitro via increasing the acetylation levels of histones H3 and H4 and regulating the transcription factor BRD4. In subcutaneous transplant tumor mice and colitis-associated cancer (CAC) mice, romidepsin increased the percentage of FOXP3+ regulatory T cells (Tregs), decreased the ratio of Th1/Th2 cells and the percentage of IFN-γ+ CD8+ T cells in the peripheral blood and the tumor microenvironment. Upon combination with an anti-PD-1 antibody, the anti-tumor effects of romidepsin were enhanced and the influence on CD4+ and CD8+ T cells was partially reversed. Therefore, the combination of romidepsin and anti-PD-1 immunotherapy provides a more potential treatment for colon cancer.

Background Accurately predicting the survival rate of breast cancer patients is a major issue for cancer researchers. Machine learning (ML) has attracted much attention with the hope that it could provide accurate results, but its modeling methods and prediction performance remain controversial. The aim of this systematic review is to identify and critically appraise current studies regarding the application of ML in predicting the 5-year survival rate of breast cancer. Methods In accordance with the PRISMA guidelines, two researchers independently searched the PubMed (including MEDLINE), Embase, and Web of Science Core databases from inception to November 30, 2020. The search terms included breast neoplasms, survival, machine learning, and specific algorithm names. The included studies related to the use of ML to build a breast cancer survival prediction model and model performance that can be measured with the value of said verification results. The excluded studies in which the modeling process were not explained clearly and had incomplete information. The extracted information included literature information, database information, data preparation and modeling process information, model construction and performance evaluation information, and candidate predictor information. Results Thirty-one studies that met the inclusion criteria were included, most of which were published after 2013. The most frequently used ML methods were decision trees (19 studies, 61.3%), artificial neural networks (18 studies, 58.1%), support vector machines (16 studies, 51.6%), and ensemble learning (10 studies, 32.3%). The median sample size was 37256 (range 200 to 659820) patients, and the median predictor was 16 (range 3 to 625). The accuracy of 29 studies ranged from 0.510 to 0.971. The sensitivity of 25 studies ranged from 0.037 to 1. The specificity of 24 studies ranged from 0.008 to 0.993. The AUC of 20 studies ranged from 0.500 to 0.972. The precision of 6 studies ranged from 0.549 to 1. All of the models were internally validated, and only one was externally validated. Conclusions Overall, compared with traditional statistical methods, the performance of ML models does not necessarily show any improvement, and this area of research still faces limitations related to a lack of data preprocessing steps, the excessive differences of sample feature selection, and issues related to validation. Further optimization of the performance of the proposed model is also needed in the future, which requires more standardization and subsequent validation.

Currently, tumor-targeted nanocarriers self-assembled from amphiphilic polymer–drug conjugates are of great demand. The appeal of these carriers arises mainly through their excellent loading efficiency of homologous drug molecules with microenvironment-triggered drug release. Herein, doxorubicin (DOX) was constructed to a hyaluronic acid (HA) backbone through hydrazone and disulfide linkages to construct pH and reduction coresponsive prodrug conjugates (HA-ss-DOX). During formulation, the amphipathic HA-ss-DOX spontaneously assembled into distinct core/shell micelles in aqueous media and showed conspicuous physical DOX loading capabilities (29.1%, DOX/HA-ss-DOX) based on homologous compatibility. DOX/HA-ss-DOX micelles were shown to be stable in normal physiological environments, while accomplishing selective, rapid DOX release at acidic pH and/or highly reducing conditions. The efficacy of DOX/HA-ss-DOX micelles was tested on A549 human lung cancer cells, wherein flow cytometry and confocal microscopy analysis revealed their HA receptor-mediated endocytosis mechanism. In comparison, DOX-loaded redox-insensitive micelles (DOX/HA-DOX) still demonstrated pH-dependent drug release. However, a more rapid intracellular DOX release profile was achieved in DOX/HA-ss-DOX micelles because of their sensitivity to both acidic and reducing environments. Resultantly, DOX/HA-ss-DOX exhibited the strongest cytotoxicity and apoptosis-inducing ability among all tested groups when tested on an A549 cell line and xenograft model.

Adiponectin and miR-133a are key regulators in cardiac hypertrophy. However, whether APN has a potential effect on miR-133a remains unclear. In this study, we aimed to investigate whether APN could regulate miR-133a expression in Angiotensin II (Ang II) induced cardiac hypertrophy in vivo and in vitro. Lentiviral-mediated adiponectin treatment attenuated cardiac hypertrophy induced by Ang II infusion in male wistar rats as determined by reduced cell surface area and mRNA levels of atrial natriuretic peptide (ANF) and brain natriuretic peptide (BNP), also the reduced left ventricular end-diastolic posterior wall thickness (LVPWd) and end-diastolic interventricular septal thickness (IVSd). Meanwhile, APN elevated miR-133a level which was downregulated by Ang II. To further investigate the underlying molecular mechanisms, we treated neonatal rat ventricular myocytes (NRVMs) with recombinant rat APN before Ang II stimulation. Pretreating cells with recombinant APN promoted AMP-activated protein kinase (AMPK) phosphorylation and inhibited ERK activation. By using the inhibitor of AMPK or a lentiviral vector expressing AMPK short hairpin RNA (shRNA) cancelled the positive effect of APN on miR-133a. The ERK inhibitor PD98059 reversed the downregulation of miR-133a induced by Ang II. These results indicated that the AMPK activation and ERK inhibition were responsible for the positive effect of APN on miR-133a. Furthermore, adiponectin receptor 1 (AdipoR1) mRNA expression was inhibited by Ang II stimulation. The positive effects of APN on AMPK activation and miR-133a, and the inhibitory effect on ERK phosphorylation were inhibited in NRVMs transfected with lentiviral AdipoR1shRNA. In addition, APN depressed the elevated expression of connective tissue growth factor (CTGF), a direct target of miR-133a, through the AMPK pathway. Taken together, our data indicated that APN reversed miR-133a levels through AMPK activation, reduced ERK1/2 phosphorylation in cardiomyocytes stimulated with Ang II, revealing a previously undemonstrated and important link between APN and miR-133a.