Background: Multiple endocrine neoplasia type 1 (MEN1) is an autosomal dominant disorder characterized by tumors in multiple endocrine organs, caused by variants in the MEN1 gene. This study analyzed the clinical and genetic features of MEN1 in a Korean cohort, identifying prevalent manifestations and genetic variants, including novel variants. Methods: This multicenter retrospective study reviewed the medical records of 117 MEN1 patients treated at three tertiary centers in Korea between January 2012 and September 2022. Patient demographics, tumor manifestations, outcomes, and MEN1 genetic testing results were collected. Variants were classified using American College of Medical Genetics and Genomics (ACMG) and French Oncogenetics Network of Neuroendocrine Tumors propositions (TENGEN) guidelines. Results: A total of 117 patients were enrolled, including 55 familial cases, with a mean age at diagnosis of 37.4±15.3 years. Primary hyperparathyroidism was identified as the most common presentation (84.6%). The prevalence of gastroenteropancreatic neuroendocrine tumor and pituitary neuroendocrine tumor (PitNET) was 77.8% (n=91) and 56.4% (n=66), respectively. Genetic testing revealed 61 distinct MEN1 variants in 101 patients, with 18 being novel. Four variants were reclassified according to the TENGEN guidelines. Patients with truncating variants (n=72) exhibited a higher prevalence of PitNETs compared to those with non-truncating variants (n=25) (59.7% vs. 36.0%, P=0.040). Conclusion The association between truncating variants and an increased prevalence of PitNETs in MEN1 underscores the importance of genetic characterization in guiding the clinical management of this disease. Our study sheds light on the clinical and genetic characteristics of MEN1 among the Korean population.

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: Multiple endocrine neoplasia type 1 (MEN1) is an autosomal dominant disorder characterized by tumors in multiple endocrine organs, caused by variants in the MEN1 gene. This study analyzed the clinical and genetic features of MEN1 in a Korean cohort, identifying prevalent manifestations and genetic variants, including novel variants. Methods: This multicenter retrospective study reviewed the medical records of 117 MEN1 patients treated at three tertiary centers in Korea between January 2012 and September 2022. Patient demographics, tumor manifestations, outcomes, and MEN1 genetic testing results were collected. Variants were classified using American College of Medical Genetics and Genomics (ACMG) and French Oncogenetics Network of Neuroendocrine Tumors propositions (TENGEN) guidelines. Results: A total of 117 patients were enrolled, including 55 familial cases, with a mean age at diagnosis of 37.4±15.3 years. Primary hyperparathyroidism was identified as the most common presentation (84.6%). The prevalence of gastroenteropancreatic neuroendocrine tumor and pituitary neuroendocrine tumor (PitNET) was 77.8% (n=91) and 56.4% (n=66), respectively. Genetic testing revealed 61 distinct MEN1 variants in 101 patients, with 18 being novel. Four variants were reclassified according to the TENGEN guidelines. Patients with truncating variants (n=72) exhibited a higher prevalence of PitNETs compared to those with non-truncating variants (n=25) (59.7% vs. 36.0%, P=0.040). Conclusion The association between truncating variants and an increased prevalence of PitNETs in MEN1 underscores the importance of genetic characterization in guiding the clinical management of this disease. Our study sheds light on the clinical and genetic characteristics of MEN1 among the Korean population.

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: Multiple endocrine neoplasia type 1 (MEN1) is an autosomal dominant disorder characterized by tumors in multiple endocrine organs, caused by variants in the MEN1 gene. This study analyzed the clinical and genetic features of MEN1 in a Korean cohort, identifying prevalent manifestations and genetic variants, including novel variants. Methods: This multicenter retrospective study reviewed the medical records of 117 MEN1 patients treated at three tertiary centers in Korea between January 2012 and September 2022. Patient demographics, tumor manifestations, outcomes, and MEN1 genetic testing results were collected. Variants were classified using American College of Medical Genetics and Genomics (ACMG) and French Oncogenetics Network of Neuroendocrine Tumors propositions (TENGEN) guidelines. Results: A total of 117 patients were enrolled, including 55 familial cases, with a mean age at diagnosis of 37.4±15.3 years. Primary hyperparathyroidism was identified as the most common presentation (84.6%). The prevalence of gastroenteropancreatic neuroendocrine tumor and pituitary neuroendocrine tumor (PitNET) was 77.8% (n=91) and 56.4% (n=66), respectively. Genetic testing revealed 61 distinct MEN1 variants in 101 patients, with 18 being novel. Four variants were reclassified according to the TENGEN guidelines. Patients with truncating variants (n=72) exhibited a higher prevalence of PitNETs compared to those with non-truncating variants (n=25) (59.7% vs. 36.0%, P=0.040). Conclusion The association between truncating variants and an increased prevalence of PitNETs in MEN1 underscores the importance of genetic characterization in guiding the clinical management of this disease. Our study sheds light on the clinical and genetic characteristics of MEN1 among the Korean population.

Background: Multiple endocrine neoplasia type 1 (MEN1) is an autosomal dominant disorder characterized by tumors in multiple endocrine organs, caused by variants in the MEN1 gene. This study analyzed the clinical and genetic features of MEN1 in a Korean cohort, identifying prevalent manifestations and genetic variants, including novel variants. Methods: This multicenter retrospective study reviewed the medical records of 117 MEN1 patients treated at three tertiary centers in Korea between January 2012 and September 2022. Patient demographics, tumor manifestations, outcomes, and MEN1 genetic testing results were collected. Variants were classified using American College of Medical Genetics and Genomics (ACMG) and French Oncogenetics Network of Neuroendocrine Tumors propositions (TENGEN) guidelines. Results: A total of 117 patients were enrolled, including 55 familial cases, with a mean age at diagnosis of 37.4±15.3 years. Primary hyperparathyroidism was identified as the most common presentation (84.6%). The prevalence of gastroenteropancreatic neuroendocrine tumor and pituitary neuroendocrine tumor (PitNET) was 77.8% (n=91) and 56.4% (n=66), respectively. Genetic testing revealed 61 distinct MEN1 variants in 101 patients, with 18 being novel. Four variants were reclassified according to the TENGEN guidelines. Patients with truncating variants (n=72) exhibited a higher prevalence of PitNETs compared to those with non-truncating variants (n=25) (59.7% vs. 36.0%, P=0.040). Conclusion The association between truncating variants and an increased prevalence of PitNETs in MEN1 underscores the importance of genetic characterization in guiding the clinical management of this disease. Our study sheds light on the clinical and genetic characteristics of MEN1 among the Korean population.

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.

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.