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Purpose: CRC is a malignant tumor seriously threatening human health. Quercetin and kaempferol are representative components of traditional Chinese medicine (TCM). Previous studies have shown that both quercetin and kaempferol have antitumor pharmacological effects, nevertheless, the underlying mechanism of action remains unclear. To explore the synergy and mechanism of quercetin and kaempferol in colorectal cancer. Methods: In this study, network pharmacology, and bioinformatics are used to obtain the intersection of drug targets and disease genes. Training gene sets were acquired from the TCGA database, acquired prognostic-related genes by univariate Cox, multivariate Cox, and Lasso-Cox regression models, and validated in the GEO dataset. We also made predictions of the immune function of the samples and used molecular docking to map a model for binding two components to prognostic genes. Results: Through Lasso-Cox regression analysis, we obtained three models of drug target genes. This model predicts the combined role of quercetin and kaempferol in the treatment and prognosis of CRC. Prognostic genes are correlated with immune checkpoints and immune infiltration and play an adjuvant role in the immunotherapy of CRC. Conclusion: Core genes are regulated by quercetin and kaempferol to improve the patient's immune system and thus assist in the treatment of CRC.

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Alteration in the polybromo-1 (PBRM1) protein encoding gene PBRM1 is the second most frequent mutation in clear cell renal cell carcinoma (ccRCC). It causes a series of changes in the tumorigenesis, progression, prognosis, and immune response of ccRCC patients. This study explored the PBRM1-associated immunological features and identified the immune-related genes (IRGs) linked to PBRM1 mutation using bioinformatics methods. A total of 37 survival IRGs associated with PBRM1 mutation in ccRCC patients were identified. To further explore the role of these IRGs in ccRCC and their association with immune status, eight IRGs with remarkable potential as individual targets were selected. An immune model that was constructed showed good performance in stratifying patients into different subgroups, showing clinical application potential compared to traditional clinical factors. Patients in the high-risk group were inclined to have more advanced stage and higher grade tumors with node metastasis, distant metastasis, and poorer prognosis. Furthermore, these patients had high percentages of regulatory T cells, follicular helper T cells, and M0 macrophages and exhibited high expression levels of immune checkpoints proteins, such as CTLA-4, PD-1, LAG-3, TIGIT, and CD47. Finally, a nomogram integrating the model and clinical factors for clinical application showed a more robust predictive performance for prognosis. The prediction model associated with PBRM1 mutation status and immunity can serve as a promising tool to stratify patients depending upon their immune status, thus facilitating immunotherapy in the future.

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All biotherapeutics have the potential to induce an immune response. This immunological response is complex and, in addition to antibody formation, involves T cell activation and innate immune responses that could contribute to adverse effects. Integrated immunogenicity data analysis is crucial to understanding the possible clinical consequences of anti-drug antibody (ADA) responses. Because patient- and product-related factors can influence the immunogenicity of a therapeutic protein, a risk-based approach is recommended and followed by most drug developers to provide insight over the potential harm of unwanted ADA responses. This paper examines mitigation strategies currently implemented and novel under investigation approaches used by drug developers. The review describes immunomodulatory regimens used in the clinic to mitigate deleterious ADA responses to replacement therapies for deficiency syndromes, such as hemophilia A and B, and high risk classical infantile Pompe patients (e.g., cyclophosphamide, methotrexate, rituximab); novel in silico and in vitro prediction tools used to select candidates based on their immunogenicity potential (e.g., anti-CD52 antibody primary sequence and IFN beta-1a formulation); in vitro generation of tolerogenic antigen-presenting cells (APCs) to reduce ADA responses to factor VIII and IX in murine models of hemophilia; and selection of novel delivery systems to reduce in vivo ADA responses to highly immunogenic biotherapeutics (e.g., asparaginase). We conclude that mitigation strategies should be considered early in development for biotherapeutics based on our knowledge of existing clinical data for biotherapeutics and the immune response involved in the generation of these ADAs.

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