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: and Purpose Patients with cluster headache (CH) exhibit impaired health-related quality of life (HRQoL). However, there have been few studies related to the HRQoL of patients with CH from Asian backgrounds. This study aimed to determine the impact of CH on HRQoL and to identify the factors affecting HRQoL in patients with CH during cluster periods. Methods: This prospective study enrolled patients with CH from 17 headache clinics in South Korea between September 2016 and February 2021. The study aimed to determine HRQoL in patients with CH using the EuroQol 5 Dimensions (EQ-5D) index and the time trade-off (TTO) method. Age- and sex-matched headache-free participants were recruited as a control group. Results: The study included 423 patients with CH who experienced a cluster period at the time. EQ-5D scores were lower in patients with CH (0.88±0.43, mean±standard deviation) than in the controls (0.99±0.33, p<0.001). The TTO method indicated that 58 (13.6%) patients with CH exhibited moderate-to-severe HRQoL deterioration. The HRQoL states in patients with CH were associated with current smoking patterns, headache severity, frequency, and duration, and scores on the Generalized Anxiety Disorder 7-item scale (GAD-7), Patient Health Questionnaire 9-item scale (PHQ-9), 6-item Headache Impact Test, and 12-item Allodynia Symptom Checklist. Multivariable logistic regression analyses demonstrated that the HRQoL states in patients with CH were negatively correlated with the daily frequency of headaches, cluster period duration, and GAD-7 and PHQ-9 scores. Conclusions Patients with CH experienced a worse quality of life during cluster periods compared with the headache-free controls, but the degree of HRQoL deterioration varied among them. The daily frequency of headaches, cluster period duration, anxiety, and depression were factors associated with HRQoL deterioration severity in patients with CH.

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.

Background/Aims: Fexuprazan is a novel potassium-competitive acid blocker that could be of benefit to patients with gastric mucosal injury. The aim of this study was to assess the 2-week efficacy and safety of fexuprazan in patients with acute or chronic gastritis. Methods: In this study, 327 patients with acute or chronic gastritis who had one or more gastric erosions on endoscopy and subjective symptoms were randomized into three groups receiving fexuprazan 20 mg once a day (q.d.), fexuprazan 10 mg twice a day (b.i.d.), or placebo for 2 weeks. The posttreatment assessments were the primary endpoint (erosion improvement rate), secondary endpoints (cure rates of erosion and edema and improvement rates of redness, hemorrhage, and subjective symptoms), and drug-related adverse events. Results: Among the patients, 57.8% (59/102), 65.7% (67/102), and 40.6% (39/96) showed erosion improvement 2 weeks after receiving fexuprazan 20 mg q.d., fexuprazan 10 mg b.i.d., and placebo, respectively. Both fexuprazan 20 mg q.d. and 10 mg b.i.d. showed superior efficacy to the placebo (p=0.017 and p<0.001, respectively). Likewise, both fexuprazan 20 mg q.d. and 10 mg b.i.d. also showed higher erosion healing rates than the placebo (p=0.033 and p=0.010, respectively). No difference was noted in the edema healing rate and the improvement rates for redness, hemorrhage, and subjective symptoms between the fexuprazan and placebo groups.No significant difference was noted in the incidence of adverse drug reactions. Conclusions Fexuprazan 20 mg q.d. and 10 mg b.i.d. for 2 weeks showed therapeutic efficacy superior to that of placebo in patients with acute or chronic gastritis (ClinicalTrials.gov identifier NCT04341454).

Objective: This study evaluated the clinical usefulness of mean platelet volume (MPV) for predicting functional outcomes in subarachnoid hemorrhage (SAH) patients. Methods: This is a retrospective analysis of patients who were diagnosed with SAH in the emergency room. Based on their modified Rankin Scale (mRS) score, patients were divided into two groups: 0-2 (good outcome) and 3-6 (poor outcome). Univariable and multivariable analyses were performed to investigate whether MPV, along with other multiple factors, was associated with poor prognosis. Receiver operating characteristic (ROC) curve analysis was performed to determine the value of MPV as a predicting factor of neurological prognosis. Compared to other factors, Hunt Hess grade (HHG) and modified Fisher grade (mFG) considerably influenced the outcomes in both groups (Model 1; model including all factors). Hence, a new model (Model 2) was constructed, comprising multiple factors excluding these two factors. Results: A total of 143 patients were included in this study. Although MPV was different between the two groups, it was not a significant factor in Model 1 in the multivariable analysis. In Model 2, MPV (odds ration [OR], 1.71; 95% confidence interval [CI], 1.05-2.8), age (OR, 1.06; 95% CI, 1.03-1.1), and surgical treatment (OR, 0.37; 95% CI, 0.15-0.87) were significant factors related to poor outcomes. Area under the curve (AUC) of Model 1 was 0.93, 0.85 in HHG; 0.78 in Model 2, 0.65 in mFG, and 0.62 in MPV. Conclusion Although MPV differed significantly between the good and poor outcome groups, it is insufficient to predict poor outcomes in SAH patients as an independent biomarker.

Purpose: This study aimed to analyze whether patients with lung cancer have a higher susceptibility of coronavirus disease 2019 (COVID-19), severe presentation, and higher mortality than those without lung cancer. Materials and Methods: A nationwide cohort of confirmed COVID-19 (n=8,070) between January 1, 2020, and May 30, 2020, and a 1:15 age-, sex-, and residence-matched cohort (n=121,050) were constructed. A nested case-control study was performed to compare the proportion of patients with lung cancer between the COVID-19 cohort and the matched cohort. Results: The proportion of patients with lung cancer was significantly higher in the COVID-19 cohort (0.5% [37/8,070]) than in the matched cohort (0.3% [325/121,050]) (p=0.002). The adjusted odds ratio [OR] of having lung cancer was significantly higher in the COVID-19 cohort than in the matched cohort (adjusted OR, 1.51; 95% confidence interval [CI], 1.05 to 2.10). Among patients in the COVID-19 cohort, compared to patients without lung cancer, those with lung cancer were more likely to have severe COVID-19 (54.1% vs. 13.2%, p < 0.001), including mortality (18.9% vs. 2.8%, p < 0.001). The adjusted OR for the occurrence of severe COVID-19 in patients with lung cancer relative to those without lung cancer was 2.24 (95% CI, 1.08 to 4.74). Conclusion The risk of COVID-19 occurrence and severe presentation, including mortality, may be higher in patients with lung cancer than in those without lung cancer.