Search

Abstract Background Epigenetic clocks constructed from DNA methylation patterns have emerged as excellent predictors of aging and aging-related health outcomes. Iron, a crucial element, is meticulously regulated within organisms, a phenomenon referred as iron homeostasis. Previous researches have demonstrated the sophisticated connection between aging and iron homeostasis. However, their causal relationship remains relatively unexplored. Results Through two-sample Mendelian randomization (MR) utilizing the random effect inverse variance weighted (IVW) method, each standard deviation (SD) increase in serum iron was associated with increased GrimAge acceleration (GrimAA, BetaIVW = 0.27, P = 8.54E−03 in 2014 datasets; BetaIVW = 0.31, P = 1.25E−02 in 2021 datasets), HannumAge acceleration (HannumAA, BetaIVW = 0.32, P = 4.50E−03 in 2014 datasets; BetaIVW = 0.32, P = 8.03E−03 in 2021 datasets) and Intrinsic epigenetic age acceleration (IEAA, BetaIVW = 0.34, P = 5.33E−04 in 2014 datasets; BetaIVW = 0.49, P = 9.94E−04 in 2021 datasets). Similar results were also observed in transferrin saturation. While transferrin manifested a negative association with epigenetic age accelerations (EAAs) sensitivity analyses. Besides, lack of solid evidence to support a causal relationship from EAAs to iron-related biomarkers. Conclusions The results of present investigation unveiled the causality of iron overload on acceleration of epigenetic clocks. Researches are warranted to illuminate the underlying mechanisms and formulate strategies for potential interventions.

Brain metastasis is the leading cause of death in most cancer patients; thus, anti‐brain metastasis is a crucial step in cancer treatment. The unique microenvironment and pathophysiological characteristics of brain metastases hamper the development of effective treatment methods, while the advances in nanomedicine demonstrate its immense potential for addressing this challenge. In recent years, due to breakthroughs in nanotechnology, functional and complex nanomedicine is prepared and it has been extensively explored to improve the treatment of brain metastasis. Compared to traditional treatment methods, nanomedicine can combine multiple traditional treatment methods into one single medicinal format and target unique characteristics of brain metastases to enhance the treatment efficacy. This review summarizes the main obstacles in the field of brain metastasis treatment and clinical/preclinical studies and surveys the use of a variety of nanomedicine candidates as antibrain metastasis strategies for different types of brain metastases.

BackgroundDiffuse gliomas possess a kind of malignant brain tumor with high mortality. Glutamine represents the most abundant and versatile amino acid in the body. Glutamine not only plays an important role in cell metabolism but also involves in cell survival and malignancies progression. Recent studies indicate that glutamine could also affect the metabolism of immune cells in the tumor microenvironment (TME).Materials and methodsThe transcriptome data and clinicopathological information of patients with glioma were acquired from TCGA, CGGA, and West China Hospital (WCH). The glutamine metabolism-related genes (GMRGs) were retrieved from the Molecular Signature Database. Consensus clustering analysis was used to discover expression patterns of GMRGs, and glutamine metabolism risk scores (GMRSs) were established to model tumor aggressiveness-related GMRG expression signature. ESTIMATE and CIBERSORTx were applied to depict the TME immune landscape. The tumor immunological phenotype analysis and TIDE were utilized for predicting the therapeutic response of immunotherapy.ResultsA total of 106 GMRGs were retrieved. Two distinct clusters were established by consensus clustering analysis, which showed a close association with the IDH mutational status of gliomas. In both IDH-mutant and IDH-wildtype gliomas, cluster 2 had significantly shorter overall survival compared with cluster 1, and the differentially expressed genes between the two clusters enriched in pathways related to malignant transformation as well as immunity. In silico TME analysis of the two IDH subtypes revealed not only significantly different immune cell infiltrations and immune phenotypes between the GMRG expression clusters but also different predicted responses to immunotherapy. After the screening, a total of 10 GMRGs were selected to build the GMRS. Survival analysis demonstrated the independent prognostic role of GMRS. Prognostic nomograms were established to predict 1-, 2-, and 3-year survival rates in the four cohorts.ConclusionDifferent subtypes of glutamine metabolism could affect the aggressiveness and TME immune features of diffuse glioma, despite their IDH mutational status. The expression signature of GMRGs could not only predict the outcome of patients with glioma but also be combined into an accurate prognostic nomogram.

Cancer immunotherapy is an effective antitumor approach through activating immune systems to eradicate tumors by immunotherapeutics. However, direct administration of “naked” immunotherapeutic agents (such as nucleic acids, cytokines, adjuvants or antigens without delivery vehicles) often results in: (1) an unsatisfactory efficacy due to suboptimal pharmacokinetics; (2) strong toxic and side effects due to low targeting (or off-target) efficiency. To overcome these shortcomings, a series of polysaccharide-based nanoparticles have been developed to carry immunotherapeutics to enhance antitumor immune responses with reduced toxicity and side effects. Polysaccharides are a family of natural polymers that hold unique physicochemical and biological properties, as they could interact with immune system to stimulate an enhanced immune response. Their structures offer versatility in synthesizing multifunctional nanocomposites, which could be chemically modified to achieve high stability and bioavailability for delivering therapeutics into tumor tissues. This review aims to highlight recent advances in polysaccharide-based nanomedicines for cancer immunotherapy and propose new perspectives on the use of polysaccharide-based immunotherapeutics.

Multi-modal therapeutics are emerging for simultaneous diagnosis and treatment of cancer. Polymeric carriers are often employed for loading multiple drugs due to their versatility and controlled release of these drugs in response to a tumor specific microenvironment. A theranostic nanomedicine was designed and prepared by complexing a small gadolinium chelate, conjugating a chemotherapeutic drug PTX through a cathepsin B-responsive linker and covalently bonding a fluorescent probe pheophorbide a (Ppa) with a branched glycopolymer. The branched prodrug-based nanosystem was degradable in the tumor microenvironment with overexpressed cathepsin B, and PTX was simultaneously released to exert its therapeutic effect. The theranostic nanomedicine, branched glycopolymer-PTX-DOTA-Gd, had an extended circulation time, enhanced accumulation in tumors, and excellent biocompatibility with significantly reduced gadolinium ion (Gd3+) retention after 96 h post-injection. Enhanced imaging contrast up to 24 h post-injection and excellent antitumor efficacy with a tumor inhibition rate more than 90% were achieved from glycopolymer-PTX-DOTA-Gd without obvious systematic toxicity. This branched polymeric prodrug-based nanomedicine is very promising for safe and effective diagnosis and treatment of cancer.

The urokinase-type plasminogen activator(PLAU) and its receptor PLAUR participate in a series of cell physiological activities on the extracellular surface. Abnormal expression of PLAU and PLAUR is associated with tumorigenesis. This study aims to evaluate the prognostic value of PLAU/PLAUR transcription expression in glioma and to explore how they affect the generation and progression of glioma. In this study, online databases are applied, such as Oncomine, GEPIA, CGGA, cBioPortal, and LinkedOmics. Overexpression of PLAU/PLAUR was found to be significantly associated with clinical variables including age, tumor type, WHO grade, histology, IDH-1 mutation, and 1p19q status. PLAU and PLAUR had a high correlation in transcriptional expression levels. High expression of PLAU and PLAUR predicted a poor prognosis in primary glioma and recurrent glioma patients, especially in lower grade gliomas. Cox regression analysis indicated that high expression of PLAU and PLAUR were independent prognostic factors for shorter overall survival in glioma patients. In gene co-expression network analysis PLAU and PLAUR and their co-expression genes were found to be involved in inflammatory activities and tumor-related signaling pathways. In conclusion, PLAU and PLAUR could be promising prognostic biomarkers and potential therapeutic targets of glioma patients.

Confusing masses constitute a challenging clinical problem for differentiating between cancer and tuberculosis diagnoses. This review summarizes the major theories designed to identify factors associated with misdiagnosis, such as imaging features, laboratory tests, and clinical characteristics. Then, the clinical experiences regarding the misdiagnosis of cancer and tuberculosis are summarized. Finally, the main diagnostic points and differential diagnostic criteria are explored, and the characteristics of multimodal imaging and radiomics are summarized.

The architecture of space delay tolerant networking can effectively support a deep space communication network with long delay,high error rate and on-off frequently link characteristics through“store-carry-forwards”and other features.Due to the experiment of application of deep space communication is not easy to carry,so building an accurate and reliable semi-physical simulation platform for simulating and making an argumentation of the design of deep space communication tasks was very necessary.A space DTN semi-physical simulation platform was proposed which supported a variety of specific deep space communication tasks simulation.The simulation platform provided new theory and technology for ground simulation and verification,and provided analysis tools and test instruments for carrying out deep space communication application study.

In recent years, the breakthrough of machine learning based on deep reinforcement learning provides a new development direction for intelligent game confrontation. In order to solve the problems of slow convergence speed and great difference in training effect of heterogeneous multi-agent reinforcement learning algorithm in intelligent confrontation, this paper proposes a priori knowledge-driven multi-agent reinforcement learning game antagonism algorithm PK-MADDPG, and constructs a MADDPG model under the framework of double Critic. The model uses the experience first replay technique to optimize the prior knowledge extraction, thus achieving remarkable results in the training of game confrontation. In the national competition of MaCA heterogeneous multi-agent game confrontation, the paper compares the game confrontation results of PK-MADDPG algorithm with classical rule algorithm, and verifies the effectiveness of the algorithm proposed in this paper.