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.

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.

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.

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.

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.

Background: We explored the utility of a small multi-gene DNA panel for assessing molecular profiles of thyroid nodules and influencing clinical decisions by comparing outcomes between tested and untested nodules. Methods: Between April 2022 and May 2023, we prospectively performed fine-needle aspiration (FNA) with gene testing via DNA panel of 11 genes (BRAF, RAS [NRAS, HRAS, KRAS], EZH1, DICER1, EIF1AX, PTEN, TP53, PIK3CA, TERT promoter) in 278 consecutive nodules (panel group). Propensity score-matching (1:1) was performed with 475 nodules that consecutively underwent FNA without gene testing between January 2021 and December 2021 (control group). Results: In the panel group, positive call rate for mutations was 41.7% (BRAF 16.2%, RAS 12.6%, others 11.5%, double mutation 1.4%) for all nodules, and 40.0% (BRAF 4.3%, RAS 19.1%, others 15.7%, double mutation 0.9%) for indeterminate nodules. Benign call rate was 69.8% for all nodules, and 75.7% for indeterminate nodules. In four nodules, additional TP53 (in addition to BRAF or EZH1) or PIK3CA (in addition to BRAF or TERT) mutations were co-detected. Sensitivity, specificity, positive predictive value, and negative predictive value were 80.0%, 53.3%, 88.1%, 38.1% for all nodules, and 78.6%, 45.5%, 64.7%, 62.5% for indeterminate nodules, respectively. Panel group exhibited lower surgical resection rates than the control group for all nodules (27.0% vs. 52.5%, P<0.001), and indeterminate nodules (23.5% vs. 68.2%, P<0.001). Malignancy risk was significantly different between the panel and control groups (81.5% vs. 63.9%, P=0.008) for all nodules. Conclusion Our panel aids in managing thyroid nodules by providing information on malignancy risk based on mutations, potentially reducing unnecessary surgery in benign nodules or patients with less aggressive malignancies.

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.

Objective: Methamphetamine (MA), cocaine, and heroin cause severe public health problems as well as impairments in neural plasticity and cognitive function in the hippocampus. This study aimed to identify the genes differentially expressed in the hippocampi of cynomolgus monkeys in response to these drugs. Methods: After the monkeys were chronically exposed to MA, cocaine, and heroin, we performed large-scale gene expression profiling of the hippocampus using RNA-Seq technology and functional annotation of genes differentially expressed. Some genes selected from RNA-Seq analysis data were validated with reverse transcription-quantitative polymerase chain reaction (RT-qPCR). And the expression changes of ADAM10 protein were assessed using immunohistochemistry. Results: The changes in genes related to axonal guidance (PTPRP and KAL1), the cell cycle (TLK2), and the regulation of potassium ions (DPP10) in the drug-treated groups compared to the control group were confirmed using RT-qPCR. Comparative analysis of all groups showed that among genes related to synaptic long-term potentiation, CREBBP and GRIN3A were downregulated in both the MA- and heroin-treated groups compared to the control group. In particular, the mRNA and protein expression levels of ADAM10 were decreased in the MA-treated group but increased in the cocaine-treated group compared to the control group. Conclusion These results provide insights into the genes that are upregulated and downregulated in the hippocampus by the chronic administration of MA, cocaine, or heroin and basic information for developing novel drugs for the treatment of hippocampal impairments caused by drug abuse.

Purpose: This study aimed to identify the factors influencing the interaction between mothers and preschool-age children and construct a structural equation model based on the factors identified. Methods: The data were collected from the mothers of children aged three to six years enrolled in kindergartens in Seoul, Gyeonggi, Chungbuk, and Jeonbuk. A total of 328 questionnaires were used for the analysis. Analysis was performed using SPSS 26.0 and AMOS 26.0 programs. Results: The child’s self-regulation (β = .42, p < .001) had the most significant influence on the interaction between mothers and preschool children, followed by the mother’s parenting stress (β = - .23, p = .008), social support (β = .17, p < .001), and the child’s active temperament (β = .15, p < .001). The child’s gender, emotional temperament, social temperament, and mother’s education level had indirect influences. Conclusion These results suggest that interventions considering the child’s temperament, mother’s parenting stress, and social support are required to promote the interaction between preschool-age children and mothers.

PURPOSE: This study was conducted to determine the influence of ego-identity, parenting efficacy, and stress coping style on depression in mothers of toddlers. METHODS: This was a cross-sectional, descriptive study. Data were collected from 164 mothers of toddlers, and analyzed using descriptive statistics, the t-test, analysis of variance, Pearson correlation coefficients, and multiple regression analysis using SPSS for Windows version 24.0. RESULTS: The factors influencing depression in mothers of toddlers included ego-identity, economic status, and perceived health status. These variables explained 60.0% of depression in mothers of toddlers. CONCLUSION: Lower ego-identity was associated with more intense depression. Therefore, healthcare providers should develop intervention programs that can improve ego-identity to decrease depression among mothers of toddlers.
Child, Preschool ; Depression ; Health Personnel ; Humans ; Mothers ; Parenting ; Parents