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: 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.

Local ablation for hepatocellular carcinoma, a non-surgical option that directly targets and destroys tumor cells, has advanced significantly since the 1990s. Therapies with different energy sources, such as radiofrequency ablation, microwave ablation, and cryoablation, employ different mechanisms to induce tumor necrosis. The precision, safety, and effectiveness of these therapies have increased with advances in guiding technologies and device improvements.Consequently, local ablation has become the first-line treatment for early-stage hepatocellular carcinoma. The lack of organized evidence and expert opinions regarding patient selection, preprocedure preparation, procedural methods, swift post-treatment evaluation, and follow-up has resulted in clinicians following varied practices. Therefore, an expert consensus-based practical recommendation for local ablation was developed by a group of experts in radiology and hepatology from the Research Committee of the Korean Liver Cancer Association in collaboration with the Korean Society of Image-Guided Tumor Ablation to provide useful information and guidance for performing local ablation and for the pre- and post-treatment management of patients.

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: A widely distributed cell cycle inhibitor, p27, regulates cyclin-dependent kinase-cyclin complexes. Although the prognostic value of p27 has been established for various types of carcinomas, its role in luminal breast cancer remains poorly understood. This study aimed to explore the functional enrichment of p27 and identify potential drug targets in patients with luminal-type breast cancer. Methods: Clinicopathological data were collected from 868 patients with luminal-type breast cancer. Additionally, publicly available data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset (1,500 patients) and the Gene Expression Omnibus database (855 patients) were included in the analysis. Immunohistochemical staining for p27, differential gene expression analysis, disease ontology analysis, survival prediction modeling using machine learning (ML), and in vitro drug screening were also performed. Results: Low p27 expression correlated with younger age, advanced tumor stage, estrogen receptor/progesterone receptor negativity, decreased cluster of differentiation 8+ T cell count, and poorer survival outcomes in luminal-type breast cancer. The METABRIC data revealed that reduced cyclin-dependent kinase inhibitor 1B (CDKN1B) expression (encoding p27) was associated with cell proliferation-related pathways and epigenetic polycomb repressive complex 2. Using ML, p27 emerged as the second most significant survival factor after N stage, thereby enhancing survival model performance. Additionally, luminal-type breast cancer cell lines with low CDKN1B expression demonstrated increased sensitivity to specific anticancer drugs such as voxtalisib and serdemetan, implying a potential therapeutic synergy between CDKN1B-targeted approaches and these drugs. Conclusion The integration of ML and bioinformatic analyses of p27 has the potential to enhance risk stratification and facilitate personalized treatment strategies for patients with breast cancer.

Purpose: A widely distributed cell cycle inhibitor, p27, regulates cyclin-dependent kinase-cyclin complexes. Although the prognostic value of p27 has been established for various types of carcinomas, its role in luminal breast cancer remains poorly understood. This study aimed to explore the functional enrichment of p27 and identify potential drug targets in patients with luminal-type breast cancer. Methods: Clinicopathological data were collected from 868 patients with luminal-type breast cancer. Additionally, publicly available data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset (1,500 patients) and the Gene Expression Omnibus database (855 patients) were included in the analysis. Immunohistochemical staining for p27, differential gene expression analysis, disease ontology analysis, survival prediction modeling using machine learning (ML), and in vitro drug screening were also performed. Results: Low p27 expression correlated with younger age, advanced tumor stage, estrogen receptor/progesterone receptor negativity, decreased cluster of differentiation 8+ T cell count, and poorer survival outcomes in luminal-type breast cancer. The METABRIC data revealed that reduced cyclin-dependent kinase inhibitor 1B (CDKN1B) expression (encoding p27) was associated with cell proliferation-related pathways and epigenetic polycomb repressive complex 2. Using ML, p27 emerged as the second most significant survival factor after N stage, thereby enhancing survival model performance. Additionally, luminal-type breast cancer cell lines with low CDKN1B expression demonstrated increased sensitivity to specific anticancer drugs such as voxtalisib and serdemetan, implying a potential therapeutic synergy between CDKN1B-targeted approaches and these drugs. Conclusion The integration of ML and bioinformatic analyses of p27 has the potential to enhance risk stratification and facilitate personalized treatment strategies for patients with breast cancer.

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: A widely distributed cell cycle inhibitor, p27, regulates cyclin-dependent kinase-cyclin complexes. Although the prognostic value of p27 has been established for various types of carcinomas, its role in luminal breast cancer remains poorly understood. This study aimed to explore the functional enrichment of p27 and identify potential drug targets in patients with luminal-type breast cancer. Methods: Clinicopathological data were collected from 868 patients with luminal-type breast cancer. Additionally, publicly available data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset (1,500 patients) and the Gene Expression Omnibus database (855 patients) were included in the analysis. Immunohistochemical staining for p27, differential gene expression analysis, disease ontology analysis, survival prediction modeling using machine learning (ML), and in vitro drug screening were also performed. Results: Low p27 expression correlated with younger age, advanced tumor stage, estrogen receptor/progesterone receptor negativity, decreased cluster of differentiation 8+ T cell count, and poorer survival outcomes in luminal-type breast cancer. The METABRIC data revealed that reduced cyclin-dependent kinase inhibitor 1B (CDKN1B) expression (encoding p27) was associated with cell proliferation-related pathways and epigenetic polycomb repressive complex 2. Using ML, p27 emerged as the second most significant survival factor after N stage, thereby enhancing survival model performance. Additionally, luminal-type breast cancer cell lines with low CDKN1B expression demonstrated increased sensitivity to specific anticancer drugs such as voxtalisib and serdemetan, implying a potential therapeutic synergy between CDKN1B-targeted approaches and these drugs. Conclusion The integration of ML and bioinformatic analyses of p27 has the potential to enhance risk stratification and facilitate personalized treatment strategies for patients with breast cancer.

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.