Kaposi's sarcoma-associated herpesvirus (KSHV) and Epstein-Barr virus (EBV) are herpesviruses associated with human malignancies. As exosomes can shuttle many herpesvirus-associated biomolecules from host cells to recipient cells, the exosomal pathway is utilized by herpesviruses to achieve extensive infections and even oncogenesis. In this review, we discuss the oncogenic biomolecules present in exosomes derived from KSHV-and EBV-infected cells. Moreover, oncogenesis via exosomal biomolecules mainly occurs through three processes, including regulation of downstream signals, promotion of immune dysfunction and transformation of cells.Also, the exosomes may provide diagnostic markers and therapeutic targets specific for KSHV-and EBV-associated malignancies.
Immunotherapy as an alternative treatment strategy for B-cell lymphoma is undesirable because of tumor heterogeneity and immune surveillance. Spermidine (SPM), as a regulator of the tumor microenvironment (TME), can facilitate the release of damage-associated molecular patterns (DAMPs) from cancer cells, promote immune recognition, and thus alleviate immune surveillance in the TME. Hence, in this work, self-assembled spermidine-based metal-immunopeptide nanocomplexes (APP-Fe NCs; APP is anti-programmed death ligand-1 peptide) with pH-responsive release kinetics were prepared via the flash nanocomplexation (FNC) technique based on the noncovalent interaction between APP-SPM-dextran (DEX) and sodium tripolyphosphate (TPP) and coordination between Fe 3+ and TPP. An in vitro study suggested that APP-Fe NCs effectively induce strong oxidative stress and mitochondrial dysfunction and subsequently lead to ferroptosis in cells by interfering with homeostasis in lymphoma cells. Further investigation on lymphoma mice models demonstrated that APP-Fe NCs effectively inhibited the growth and liver metastasis of lymphomas. Mechanistically, by triggering ferroptosis in tumor tissues, these spermidine-containing APP-Fe NCs efficiently facilitated the release of DAMPs and ultimately reshaped TME to enhance immunotherapy efficacy in lymphoma. Combined with its good histocompatibility and facile preparation technique, this pH-responsive APP-Fe NCs with regulation on TME may hold potential for cascade amplification on the combinative immunotherapy of lymphoma in the clinic.
Background: Hepatocellular carcinoma (HCC) is a lethal tumor. Its prognosis prediction remains a challenge. Meanwhile, cellular senescence, one of the hallmarks of cancer, and its related prognostic genes signature can provide critical information for clinical decision-making. Method: Using bulk RNA sequencing and microarray data of HCC samples, we established a senescence score model via multi-machine learning algorithms to predict the prognosis of HCC. Single-cell and pseudo-time trajectory analyses were used to explore the hub genes of the senescence score model in HCC sample differentiation. Result: A machine learning model based on cellular senescence gene expression profiles was identified in predicting HCC prognosis. The feasibility and accuracy of the senescence score model were confirmed in external validation and comparison with other models. Moreover, we analyzed the immune response, immune checkpoints, and sensitivity to immunotherapy drugs of HCC patients in different prognostic risk groups. Pseudo-time analyses identified four hub genes in HCC progression, including CDCA8, CENPA, SPC25, and TTK, and indicated related cellular senescence. Conclusions: This study identified a prognostic model of HCC by cellular senescence-related gene expression and insight into novel potential targeted therapies.
Using multi-kernel learning to deal with the non-linear relationship of data has become a new research topic in the field of multi-view subspace clustering. However, the existing methods have the following three defects: 1) the simple consensus kernel weighting strategy cannot give full play to the advantages of multiple kernels; 2) they are sensitive to non-Gaussian noise and their learning affinity matrices cannot meet the block diagonal properties required by clustering, resulting in low clustering performance; 3) the complementary feature information between the data of each view cannot be fully mined. In this paper, a novel robust multi-view subspace clustering method is proposed based on weighted multi-kernel learning and co-regularization (WMKMSC). Based on the self-expression learning framework, block diagonal regularizer (BDR), multi-kernel learning strategy and co-regularization are integrated into the proposed model. Especially, as a robust learning method, the mixture correntropy is used to construct a robust multi-kernel weighting strategy, which is helpful to learn the best consensus kernel. Our method is more effective and robust than several of the most advanced methods on five commonly used datasets.
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