Recent Advances of Emerging Spleen-Targeting Nanovaccines for Immunotherapy.

Vaccines provide a powerful tool to modulate the immune system for human disease prevention and treatment. Classical vaccines mainly initiate immune responses in the lymph nodes (LNs) after subcutaneous injection. However, some vaccines suffer from inefficient delivery of antigens to LNs, undesired inflammation, and slow immune induction when encountering the rapid proliferation of tumors. Alternatively, the spleen, as the largest secondary lymphoid organ with a high density of antigen-presenting cells (APCs) and lymphocytes, acts as an emerging target organ for vaccinations in the body. Upon intravenous administration, the rationally designed spleen-targeting nanovaccines can be internalized by the APCs in the spleen to induce selective antigen presentation to T and B cells in their specific sub-regions, thereby rapidly boosting durable cellular and humoral immunity. Herein, the recent advances of spleen-targeting nanovaccines for immunotherapy based on the anatomical architectures and functional zones of the spleen, as well as their limitations and perspectives for clinical applications are systematically summarized. The aim is to emphasize the design of innovative nanovaccines for enhanced immunotherapy of intractable diseases in the future.

Screening and identifying a novel M-MDSCs-related gene signature for predicting prognostic risk and immunotherapeutic responses in patients with lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) shows intratumoral heterogeneity, a highly complex phenomenon that known to be a challenge during cancer therapy. Considering the key role of monocytic myeloid-derived suppressor cells (M-MDSCs) in the tumor microenvironment (TME), we aimed to build a prognostic risk model using M-MDSCs-related genes. Methods: M-MDSCs-related genes were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Utilized univariate survival analysis and random forest algorithm to screen candidate genes. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was selected to build the risk model. Patients were scored and classified into high- and low-risk groups based on the median risk scores. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis along with R packages "estimate" and "ssGSEA" were performed to reveal the mechanism of risk difference. Prognostic biomarkers and tumor mutation burden (TMB) were combined to predict the prognosis. Nomogram was carried out to predict the survival probability of patients in 1, 3, and 5 years. Results: 8 genes (VPREB3, TPBG, LRFN4, CD83, GIMAP6, PRMT8, WASF1, and F12) were identified as prognostic biomarkers. The GEO validation dataset demonstrated the risk model had good generalization effect. Significantly enrichment level of cell cycle-related pathway and lower content of CD8+ T cells infiltration in the high-risk group when compared to low-risk group. Morever, the patients were from the intersection of high-TMB and low-risk groups showed the best prognosis. The nomogram demonstrated good consistency with practical outcomes in predicting the survival rate over 1, 3, and 5 years. Conclusion: The risk model demonstrate good prognostic predictive ability. The patients from the intersection of low-risk and high-TMB groups are not only more sensitive response to but also more likely to benefit from immune-checkpoint-inhibitors (ICIs) treatment.