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The human leukocyte antigen (HLA) system, which is part of the major histocompatibility complex (MHC) plays a vital role in immune responses by differentiating between itself and foreign cells and antigens. The significant diversity of alleles affects disease susceptibility and immune responses within different populations. Specifically, the HLA-A and HLA-DRB1 alleles are associated with various immune-related diseases, and understanding the frequency and haplotype associations of these alleles is vital for genetic and immunological research. To investigate the distribution of these characteristics in Koreans, we isolated peripheral blood mononuclear cells (PBMCs) from blood samples donated by volunteers at the Seoul Central Blood Bank and performed HLA typing on 790 samples. Our study found that the HLA-A and HLA-DRB1 alleles are widely distributed within the Korean population, with HLA-A*24:02 (21.7%) and HLA-DRB1*09:01 (9.9%) being the most frequent. Significant haplotype associations between specific HLA-A and HLA-DRB1 alleles were identified using the Chi-square test, suggesting that certain genetic combinations may influence disease onset. This insight could contribute to the development of predictive and preventative strategies for various diseases. The unique genetic characteristics of the Korean population highlight the importance of studying the HLA allele and the haplotype distributions in this group as key indicators for understanding disease susceptibility.

IntroductionAryl hydrocarbon receptor (AhR) is a transcription factor that performs various functions upon ligand activation. Several studies have explored the role of AhR expression in tumor progression and immune surveillance. Nevertheless, investigations on the distribution of AhR expression, specifically in cancer or immune cells in the tumor microenvironment (TME), remain limited. Examining the AhR expression and distribution in the TME is crucial for gaining insights into the mechanism of action of AhR-targeting anticancer agents and their potential as biomarkers.MethodsHere, we used multiplexed immunohistochemistry (mIHC) and image cytometry to investigate the AhR expression and distribution in 513 patient samples, of which 292 are patients with one of five solid cancer types. Additionally, we analyzed the nuclear and cytosolic distribution of AhR expression.ResultsOur findings reveal that AhR expression was primarily localized in cancer cells, followed by stromal T cells and macrophages. Furthermore, we observed a positive correlation between the nuclear and cytosolic expression of AhR, indicating that the expression of AhR as a biomarker is independent of its localization. Interestingly, the expression patterns of AhR were categorized into three clusters based on the cancer type, with high AhR expression levels being found in regulatory T cells (Tregs) in non-small cell lung cancer (NSCLC).DiscussionThese findings are anticipated to serve as pivotal evidence for the design of clinical trials and the analysis of the anticancer mechanisms of AhR-targeting therapies.

IntroductionTo understand the immune system within the tumor microenvironment (TME) of non-small cell lung cancer (NSCLC), it is crucial to elucidate the characteristics of molecules associated with T cell activation.MethodsWe conducted an in-depth analysis using single-cell RNA sequencing data obtained from tissue samples of 19 NSCLC patients. T cells were classified based on the Tumor Proportion Score (TPS) within the tumor region, and molecular markers associated with activation and exhaustion were analyzed in T cells from high TPS areas.ResultsNotably, tetraspanins CD81 and CD82, belonging to the tetraspanin protein family, were found to be expressed in activated T cells, particularly in cytotoxic T cells. These tetraspanins showed strong correlations with activation and exhaustion markers. In vitro experiments confirmed increased expression of CD81 and CD82 in IL-2-stimulated T cells. T cells were categorized into CD81highCD82high and CD81lowCD82low groups based on their expression levels, with CD81highCD82high T cells exhibiting elevated activation markers such as CD25 and CD69 compared to CD81lowCD82low T cells. This trend was consistent across CD3+, CD8+, and CD4+ T cell subsets. Moreover, CD81highCD82high T cells, when stimulated with anti-CD3, demonstrated enhanced secretion of cytokines such as IFN-γ, TNF-α, and IL-2, along with an increase in the proportion of memory T cells. Bulk RNA sequencing results after sorting CD81highCD82high and CD81lowCD82low T cells consistently supported the roles of CD81 and CD82. Experiments with overexpressed CD81 and CD82 showed increased cytotoxicity against target cells.DiscussionThese findings highlight the multifaceted roles of CD81 and CD82 in T cell activation, cytokine production, memory subset accumulation, and target cell cytolysis. Therefore, these findings suggest the potential of CD81 and CD82 as promising candidates for co-stimulatory molecules in immune therapeutic strategies for cancer treatment within the intricate TME.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion: Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Purpose: By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems. Materials and Methods: This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data. The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed. Results: We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4+ and CD8+ T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset. Conclusion: In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease. [ABSTRACT FROM AUTHOR]