Exosomes and cancer immunotherapy: A review of recent cancer research

As phospholipid extracellular vesicles (EVs) secreted by various cells, exosomes contain non-coding RNA (ncRNA), mRNA, DNA fragments, lipids, and proteins, which are essential for intercellular communication. Several types of cells can secrete exosomes that contribute to cancer initiation and progression. Cancer cells and the immune microenvironment interact and restrict each other. Tumor-derived exosomes (TDEs) have become essential players in this balance because they carry information from the original cancer cells and express complexes of MHC class I/II epitopes and costimulatory molecules. In the present study, we aimed to identify potential targets for exosome therapy by examining the specific expression and mechanism of exosomes derived from cancer cells. We introduced TDEs and explored their role in different tumor immune microenvironment (TIME), with a particular emphasis on gastrointestinal cancers, before briefly describing the therapeutic strategies of exosomes in cancer immune-related therapy

Silicon regulation of the interface microstructure and shear strength of rheological cast-rolling aluminum/steel composite plates

This study reports the preparation of medium-thickness aluminum/steel composite plates with different diffusion layers using rheological cast-rolling technology. The thermodynamic software and first-principles were employed to determine the effects of different silicon contents on the solidification process and mechanical properties of the 6061 aluminum/steel composite plates. The research revealed that the Fe2Al5 phase on the steel side possessed a tongue-like preferential growth at a silicon content of less than 1.2 wt%. A granular Al–Fe–Si phase was formed on the aluminum side of the diffusion layer when the silicon content exceeded the saturation solubility (1.8 wt%), which completely suppressed the tongue-like preferential growth of the Fe2Al5 phase. First-principles calculations revealed that the matrixes showed superior toughness in comparison to binary intermetallic compounds (IMCs), while the toughness of binary IMCs surpassed that of ternary IMCs. The mechanical properties showed that the shear strength of the sample prepared by rheological cast-rolling was higher than that of composite casting and close to that of composite rolling, reaching a maximum value of 73.4 MPa. The shear strength initially increased and then decreased with an increase in isothermal diffusion time, and the fracture location was transferred from the loose FeAl3 to the Fe2Al5 phase. An increase in silicon content resulted in a slight increase in the microhardness of the diffusion layer, a delay of the peak value of the shear strength, and a reduction in the gradient of the shear strength. A substantial decrease in peak shear strength was observed when the silicon content reached 1.8 wt%

A Large-Scale Combinatorial Many-Objective Evolutionary Algorithm for Intensity-Modulated Radiotherapy Planning

Tian Y, Feng Y, Wang C, et al. A Large-Scale Combinatorial Many-Objective Evolutionary Algorithm for Intensity-Modulated Radiotherapy Planning. IEEE Transactions on Evolutionary Computation. 2022;26(6):1511-1525.Intensity-modulated radiotherapy (IMRT) is one of the most popular techniques for cancer treatment. However, existing IMRT planning methods can only generate one solution at a time and, consequently, medical physicists should perform the planning process many times to obtain diverse solutions to meet the requirement of a clinical case. Meanwhile, multiobjective evolutionary algorithms (MOEAs) have not been fully exploited in IMRT planning since they are ineffective in optimizing the large number of discrete variables of IMRT. To bridge the gap, this article formulates IMRT planning into a large-scale combinatorial many-objective optimization problem and proposes a coevolutionary algorithm to solve it. In contrast to the existing MOEAs handling high-dimensional search spaces via variable grouping or dimensionality reduction, the proposed algorithm evolves one population with fine encoding for local exploitation and evolves another population with rough encoding for global exploration. Moreover, the convergence speed is further accelerated by two customized local search strategies. The experimental results verify that the proposed algorithm outperforms state-of-the-art MOEAs and IMRT planning methods on a variety of clinical cases

A MRI-based radiomics model for predicting the response to anlotinb combined with temozolomide in recurrent malignant glioma patients

Abstract Objective Anlotinib is a multitarget anti-angiogenic drug that combined with temozolomide (TMZ) can effectively prolongs the overall survival (OS) of recurrent malignant glioma(rMG),but some patients do not respond to anlotinib combined with TMZ. These patients were associated with a worse prognosis and lack effective identification methods. Therefore, it is necessary to differentiate patients who may have good response to anlotinb in combination with TMZ from those who are not, in order to provide personalized targeted therapies. Methods Fifty three rMG patients (42 in training cohort and 11 in testing cohort) receiving anlotinib combined with TMZ were enrolled. A total of 3668 radiomics features were extracted from the recurrent MRI images. Radiomics features are reduced and filtered by hypothesis testing and Least Absolute Shrinkage And Selection (LASSO) regression. Eight machine learning models construct the radiomics model, and then screen out the optimal model. The performance of the model was assessed by its discrimination, calibration, and clinical usefulness with validation. Results Fifty three patients with rMG were enrolled in our study. Thirty four patients displayed effective treatment response, showed a higher survival benefits than non-response group, the median progression-free survival(PFS) was 8.53 months versus 5.33 months (p = 0.06) and the median OS was 19.9 months and 7.33 months (p = 0.029), respectively. Three radiomics features were incorporated into the model construction as final variables after LASSO regression analysis. In testing cohort, Logistic Regression (LR) model has the best performance with an Area Under the Curve (AUC) of 0.93 compared with other models, which can effectively predict the response of rMG patients to anlotinib in combination with TMZ. The calibration curve confirmed the agreement between the observed actual and prediction probability. Within the reasonable threshold probability range (0.38–0.88), the radiomics model shows good clinical utility. Conclusions The above-described radiomics model performed well, which can serve as a clinical tool for individualized prediction of the response to anlotinb combined with TMZ in rMG patients

DNA polymerase iota promotes EMT and metastasis of esophageal squamous cell carcinoma by interacting with USP7 to stabilize HIF-1α

Abstract Esophageal squamous cell carcinoma (ESCC) is one of the most lethal cancer types, with a low 5-year survival rate of ~20%. Our prior research has suggested that DNA Polymerase iota (Pol ι), a member of Y-family DNA polymerase, plays a crucial role in the invasion and metastasis of ESCC. However, the underlying mechanism is not well understood. In this study, we utilized ChIP-PCR and luciferase reporter assays to investigate the binding of HIF-1α to the promoter of the Pol ι gene. Transwell, wound healing, and mouse models were employed to assess the impact of Pol ι and HIF-1α on the motility of ESCC cells. Co-immunoprecipitation and Western blot were carried out to explore the interaction between Pol ι and HIF-1α, while qRT-PCR and Western blot were conducted to confirm the regulation of Pol ι and HIF-1α on their downstream targets. Our results demonstrate that HIF-1α activates the transcription of the Pol ι gene in ESCC cells under hypoxic conditions. Furthermore, the knockdown of Pol ι impeded HIF-1α-induced invasion and metastasis. Additionally, we found that Pol ι regulates the expression of genes involved in epithelial-mesenchymal transition (EMT) and initiates EMT through the stabilization of HIF-1α. Mechanistically, Pol ι maintains the protein stability of HIF-1α by recruiting USP7 to mediate the deubiquitination of HIF-1α, with the residues 446–578 of Pol being crucial for the interaction between Pol ι and USP7. Collectively, our findings unveil a novel feedforward molecular axis of HIF-1α- Pol ι -USP7 in ESCC that contributes to ESCC metastasis. Hence, our results present an attractive target for intervention in ESCC