Immunohistochemistry (IHC) for programmed cell death ligand 1 (PD-L1) displays staining diversity. We compared IHC staining of PD-L1 in gastric cancer (GC) by using three commercially available antibody clones, and analyzed the correlation with the prognosis. IHC using PD-L1 antibodies (clones SP142, 28–8 and E1L3N) in 315 formalin-fixed paraffin-embedded samples was qualitatively compared at the 1, 5 and 10% cut-off by two pathologists on total, tumor and immune/stromal cells. We used computer – assisted scoring to quantitatively analyze and compare the “H-score” of PD-L1 expression in 66 samples on total cells. The antibody clone SP142 was selected to investigate the infiltration of PD-L1+CD8+ T cells using automated quantitative immunofluorescence analyses (n = 50) and the prognostic significance. The prognoses were assessed by log-rank test. PD-L1 clones SP142 and 28–8 displayed great concordance by qualitative (κ = 0.816, 0.810 for total cells and tumor cells at the 5% cut-off) and quantitative analyses (R2 = 0.7991, 0.8187 for positive percentage and “H-score”). PD-L1 clone SP142 showed the highest positivity in immune/stromal cells staining (18.41%) compared to 28–8 (7.62%), while clone E1L3N showed poor staining in both tumor and immune/stromal cells. Clone SP142, but not 28–8 and E1L3N, predicted a worse prognosis at the 5% cut-off (p = 0.0243). Both the clone SP142 and 28–8 had high inter-pathologist correlation for tumor staining (R2 = 0.9805 and R2 = 0.9853), but a moderate correlation for stromal/immune cell staining (R2 = 0.5653 and R2 = 0.5745). Furthermore, a higher density of PD-L1+CD8+ T cells was correlated with a shorter survival time (R2 = 0.0909, p = 0.0352). PD-L1 antibody clone SP142 was superior in cell staining, particularly in immune/stromal cell and prognosis. These findings are important for selection of PD-L1 antibody clones in the future diagnostic test.

Background: The correlation and difference in T-cell phenotypes between peripheral blood lymphocytes (PBLs) and the tumor immune microenvironment (TIME) in patients with gastric cancer (GC) is not clear. We aimed to characterize the phenotypes of CD8⁺ T cells in tumor infiltrating lymphocytes (TILs) and PBLs in patients with different outcomes and to establish a useful survival prediction model. Methods: Multiplex immunofluorescence staining and flow cytometry were used to detect the expression of inhibitory molecules (IMs) and active markers (AMs) in CD8⁺TILs and PBLs, respectively. The role of these parameters in the 3-year prognosis was assessed by receiver operating characteristic analysis. Then, we divided patients into two TIME clusters (TIME-A/B) and two PBL clusters (PBL-A/B) by unsupervised hierarchical clustering based on the results of multivariate analysis, and used the Kaplan–Meier method to analyze the difference in prognosis between each group. Finally, we constructed and compared three survival prediction models based on Cox regression analysis, and further validated the efficiency and accuracy in the internal and external cohorts. Results: The percentage of PD-1⁺CD8⁺TILs, TIM-3⁺CD8⁺TILs, PD-L1⁺CD8⁺TILs, and PD-L1⁺CD8⁺PBLs and the density of PD-L1⁺CD8⁺TILs were independent risk factors, while the percentage of TIM-3⁺CD8⁺PBLs was an independent protective factor. The patients in the TIME-B group showed a worse 3-year overall survival (OS) (HR: 3.256, 95% CI 1.318–8.043, P = 0.006), with a higher density of PD-L1⁺CD8⁺TILs (P < 0.001) and percentage of PD-1⁺CD8⁺TILs (P = 0.017) and PD-L1⁺CD8⁺TILs (P < 0.001) compared to the TIME-A group. The patients in the PBL-B group showed higher positivity for PD-L1⁺CD8⁺PBLs (P = 0.042), LAG-3⁺CD8⁺PBLs (P < 0.001), TIM-3⁺CD8⁺PBLs (P = 0.003), PD-L1⁺CD4⁺PBLs (P = 0.001), and LAG-3⁺CD4⁺PBLs (P < 0.001) and poorer 3-year OS (HR: 0.124, 95% CI 0.017–0.929, P = 0.015) than those in the PBL-A group. In our three survival prediction models, Model 3, which was based on the percentage of TIM-3⁺CD8⁺PBLs, PD-L1⁺CD8⁺TILs and PD-1⁺CD8⁺TILs, showed the best sensitivity (0.950, 0.914), specificity (0.852, 0.857) and accuracy (κ = 0.787, P < 0.001; κ = 0.771, P < 0.001) in the internal and external cohorts, respectively. Conclusion: We established a comprehensive and robust survival prediction model based on the T-cell phenotype in the TIME and PBLs for GC prognosis.

The correlation between the triglyceride-glucose index (TyG) and the prognosis of ischemic stroke has been well established. This study aims to assess the influence of the TyG index on the clinical outcomes of critically ill individuals suffering from intracerebral hemorrhage (ICH). Patients diagnosed with ICH were retrospectively retrieved from the Medical Information Mart for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Various statistical methods, including restricted cubic spline (RCS) regression, multivariable logistic regression, subgroup analysis, and sensitivity analysis, were employed to examine the relationship between the TyG index and the primary outcomes of ICH. A total of 791 patients from MIMIC-IV and 1,113 ones from eICU-CRD were analyzed. In MIMIC-IV, the in-hospital and ICU mortality rates were 14% and 10%, respectively, while in eICU-CRD, they were 16% and 8%. Results of the RCS regression revealed a consistent linear relationship between the TyG index and the risk of in-hospital and ICU mortality across the entire study population of both databases. Logistic regression analysis revealed a significant positive association between the TyG index and the likelihood of in-hospital and ICU death among ICH patients in both databases. Subgroup and sensitivity analysis further revealed an interaction between patients' age and the TyG index in relation to in-hospital and ICU mortality among ICH patients. Notably, for patients over 60 years old, the association between the TyG index and the risk of in-hospital and ICU mortality was more pronounced compared to the overall study population in both MIMIC-IV and eICU-CRD databases, suggesting a synergistic effect between old age (over 60 years) and the TyG index on the in-hospital and ICU mortality of patients with ICH. This study established a positive correlation between the TyG index and the risk of in-hospital and ICU mortality in patients over 60 years who diagnosed with ICH, suggesting that the TyG index holds promise as an indicator for risk stratification in this patient population.

Introduction: The role of SMU1 in DNA replication and RNA splicing is well-established, yet its specific function and dysregulated mechanisms in gastric cancer (GC) remain inadequately elucidated. This study seeks to investigate the potential oncogenic and progression-promoting effects of SMU1 in GC, with the ultimate goal of informing novel approaches for treatment and diagnosis. Methods: The study investigated the expression levels of SMU1 in GC and adjacent normal tissues by analyzing data from the TCGA (27 tissue pairs) and GEO (47 tissue pairs) databases. Immunohistochemistry was used to examine 277 tumor tissue and adjacent non-tumor tissue spots from GC tissue chips, along with relevant follow-up information. The study further assessed the proliferation, invasion, and migration capabilities of cells by manipulating SMU1 expression levels and conducting various assays, including CCK-8, EdU incorporation, colony formation, transwells, flow cytometry, and subcutaneous tumorigenesis assays. Results: Our study revealed a significant upregulation of SMU1 mRNA and protein levels in GC tissues compared to adjacent tissues. Univariate and multivariate Cox analysis demonstrated that elevated levels of SMU1 were independent prognostic factors for GC prognosis ( P = 0.036). Additionally, median survival analysis indicated a significant association between high SMU1 expression and poor prognosis in GC patients ( P = 0.0002). In experiments conducted both in vivo and in vitro, it was determined that elevated levels of SMU1 can enhance the proliferation, invasion, and migration of GC cells, whereas suppression of SMU1 can impede the progression of GC by modulating the G1/S checkpoint of the cell cycle. Conclusions: Our research introduces the novel idea that SMU1 could serve as a prognostic marker for GC progression, influencing cell proliferation through cell cycle activation. These results offer valuable insights into the understanding, diagnosis, and management of gastric carcinoma.

POEMS syndrome is a rare clonal plasma cell disease associated with multisystem involvement. We reported a case of 48‐year‐old woman with large volume of exudative ascites with an increased level of λ‐light chain and hepatosplenomegaly. The patient was treated with thalidomide and dexamethasone and showed a good clinical response.

Background The correlation and difference of T cells phenotype between peripheral blood lymphocytes (PBLs) and tumor immune microenvironment (TIME) in patients with gastric cancer (GC) is not clear. We aimed to characterize the phenotypes of CD8⁺ T cells in tumor infiltrating lymphocytes (TILs) and PBLs in patients with different outcomes and to establish a useful survival prediction model. Methods Multiplex immunofluorescence staining and flow cytometry were used to detect the expression of inhibitory molecule (IM) and active marker (AM) in CD8⁺TILs and PBLs, respectively. The role of these parameters in 3-year prognosis was assessed by receiver operating characteristic. Then, we divided patients into two TIME clusters (TIME-A/B) and two PBL clusters (PBL-A/B) by unsupervised hierarchical clustering based on the results of multivariate analysis, and used Kaplan-Meier method to analyze the difference in prognosis between each group. Finally, we constructed and compared three survival prediction models based on Cox regression analysis, and further validated the efficiency and accuracy in the internal and external cohort. Results The percentage of PD-1⁺CD8⁺TILs, TIM-3⁺CD8⁺TILs, PD-L1⁺CD8⁺TILs, PD-L1⁺CD8⁺PBLs and the density of PD-L1⁺CD8⁺TILs were independent risk factors, while the percentage of TIM-3⁺CD8⁺PBLs was an independent protective factor. The patients in TIME-B showed a worse 3-year overall survival (OS) (HR: 3.256, 95%CI: 1.318–8.043, P = 0.006), with a higher density of PD-L1⁺CD8⁺TILs (P < 0.001) and percentage of PD-1⁺CD8⁺TILs (P = 0.017) and PD-L1⁺CD8⁺TILs (P < 0.001) compared to TIME-A group. The patients in PBL-B showed a higher positivity of PD-L1⁺CD8⁺PBLs (P = 0.042), LAG-3⁺CD8⁺PBLs (P < 0.001), TIM-3⁺CD8⁺PBLs (P = 0.003), PD-L1⁺CD4⁺PBLs (P = 0.001), LAG-3⁺CD4⁺PBLs (P < 0.001) and poorer 3-year OS (HR: 0.124, 95%CI: 0.017–0.929, P = 0.015) compared to PBL-A group. In our three survival prediction models, Model 3, which was based on the percentage of TIM-3⁺CD8⁺PBLs, PD-L1⁺CD8⁺TILs and PD-1⁺CD8⁺TILs, showed the best sensitivity (0.950, 0.914), specificity (0.852, 0.857) and accuracy (κ = 0.787, P < 0.001; κ = 0.771, P < 0.001) in the internal and external cohort, respectively. Conclusion We established a comprehensive and robust survival prediction model based on the T cell phenotype in TIME and PBLs for the prognosis in GC.