BackgroundImmune checkpoint blockade (ICB) targeting programmed death ligand-1 (PD-L1)/programmed cell death protein-1 (PD-1) pathway has become an attractive strategy for cancer treatment; however, unsatisfactory efficacy has limited its clinical benefits. Therefore, a more comprehensive understanding of the regulation of PD-L1 expression is essential for developing more effective cancer immunotherapy. Recent studies have revealed the important roles of eukaryotic elongation factor 2 kinase (eEF2K) in promoting epithelial-mesenchymal transition (EMT), angiogenesis, tumor cell migration and invasion; nevertheless, the exact role of eEF2K in the regulation of tumor immune microenvironment (TIME) remains largely unknown.MethodsIn this study, we used a cohort of 38 patients with melanoma who received anti-PD-1 treatment to explore the association between eEF2K expression and immunotherapy efficacy against melanoma. Immunoprecipitation-mass spectrometry analysis and in vitro assays were used to examine the role and molecular mechanism of eEF2K in regulating PD-L1 expression. We also determined the effects of eEF2K on tumor growth and cytotoxicity of CD8+ T cells in TIME in a mouse melanoma model. We further investigated the efficacy of the eEF2K inhibition in combination with anti-PD-1 treatment in vivo.ResultsHigh eEF2K expression is correlated with better therapeutic response and longer survival in patients with melanoma treated with PD-1 monoclonal antibody (mAb). Moreover, eEF2K protein expression is positively correlated with PD-L1 protein expression. Mechanistically, eEF2K directly bound to and inactivated glycogen synthase kinase 3 beta (GSK3β) by phosphorylating it at serine 9 (S9), leading to PD-L1 protein stabilization and upregulation, and subsequently tumor immune evasion. Knockdown of eEF2K decreased PD-L1 expression and enhanced CD8+ T cell activity, thus dramatically attenuating murine B16F10 melanoma growth in vivo. Clinically, p-GSK3β/S9 expression is positively correlated with the expressions of eEF2K and PD-L1, and the response to anti-PD-1 immunotherapy. Furthermore, eEF2K inhibitor, NH125 treatment or eEF2K knockdown enhanced the efficacy of PD-1 mAb therapy in a melanoma mouse model.ConclusionsOur results suggest that eEF2K may serve as a biomarker for predicting therapeutic response and prognosis in patients receiving anti-PD-1 therapy, reveal a vital role of eEF2K in regulating TIME by controlling PD-L1 expression and provide a potential combination therapeutic strategy of eEF2K inhibition with ICB therapy.
Background: Whether tumor mutation burden (TMB) correlated with improved survival outcomes or promotion of immunotherapies remained controversy in various malignancies. We aimed to explore the prognostic value of TMB and the relationship between TMB and immune infiltration in human epidermal growth factor receptor 2-positive (HER2+) breast cancer (BC).Methods: We downloaded somatic mutation data and clinical information for 216 HER2+ BC patients from the The Cancer Genome Atlas (TCGA) and cBioPortal databases. Patients were divided into highand low-TMB groups through TMB calculation. Cox regression analysis was used to establish an immuneand mutant-related risk model based on 5-hub genes. The relationship between 5-hub genes mutants and the level of immune infiltration, as well as the relationship between the risk model and the immune microenvironment were analyzed by "TIMER" database.Results: TMB was negatively correlated with overall survival (OS) and disease-free survival (DFS), and high TMB may inhibit immune infiltration in HER2+ BC. Furthermore, risk score classified effectively patients into low-and high-risk groups in training and validation cohorts. The infiltration of CD4+ T cells and NK cells and the levels of immune checkpoint pathway genes were lower in the high-risk group, which indicated a poor prognosis.Conclusions: Higher TMB correlated with poor survival outcomes and might inhibit the immune infiltrates in HER2+ BC. The 5-hub TMB-related signature conferred lower immune cells infiltration which deserved further validation.
The current diagnostic technologies for assessing the axillary lymph node metastasis (ALNM) status accurately in breast cancer (BC) remain unsatisfactory. Here, we developed a diagnostic model for evaluating the ALNM status using a combination of mRNAs and the T stage of the primary tumor as a novel biomarker. We collected relevant information on T1-2 BC from public databases. An ALNM prediction model was developed by logistic regression based on the screened signatures and then internally and externally validated. Calibration curves and the area under the curve (AUC) were employed as performance metrics. The prognostic value and tumor immune infiltration of the model were also determined. An optimal diagnostic model was created using a combination of 11 mRNAs and T stage of the primary tumor and showed high discrimination, with AUCs of 0.828 and 0.746 in the training sets. AUCs of 0.671 and 0.783 were achieved in the internal validation cohorts. The mean external AUC value was 0.686 and ranged between 0.644 and 0.742. Moreover, the new model has good specificity in T1 and hormone receptor-negative/human epidermal growth factor receptor 2- negative (HR-/HER2-) BC and good sensitivity in T2 BC. In addition, the risk of ALNM and 11 mRNAs were correlated with the infiltration of M2 macrophages, as well as the prognosis of BC. This novel prediction model is a useful tool to identify the risk of ALNM in T1-2 BC patients, particularly given that it can be used to adjust surgical options in the future.
Context The extracts of Aspongopus chinensis Dallas (Pentatomidae), an insect used in traditional Chinese medicine, have a complex chemical composition and possess multiple pharmacological activities. Objective This study comprehensively characterizes the chemical constituents of A. chinensis by an integrated targeted and untargeted strategy using UPLC-QTOF-MS combined with molecular networking. Materials and methods The ultra-performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) combined with molecular networking-based dereplication was proposed to facilitate the identification of the chemical constituents of aqueous and ethanol extracts of A. chinensis . The overall strategy was designed to avoid the inefficiency and costliness of traditional techniques. The targeted compounds discovered in the A. chinensis extracts were identified by searching a self-built database, including fragment ions, precursor ion mass, and other structural information. The untargeted compounds were identified by analyzing the relationship between different categories, fragmentation pathways, mass spectrometry data, and the structure of the same cluster of nodes within the molecular network. The untargeted strategy was verified using commercial standard samples under the same mass spectrometry conditions. Results The proposed integrated targeted and untargeted strategy was successfully applied to the comprehensive profiling of the chemical constituents of aqueous and ethanol extracts of A. chinensis. A total of 124 compounds such as fatty acids, nucleosides, amino acids, and peptides, including 74 compounds that were reported for the first time, were identified in this study. Conclusions The integrated strategy using LC tandem HRMS combined with molecular networking could be popularised for the comprehensive profiling of chemical constituents of other traditional insect medicines.
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