High expression of trimethylated histone H3 at lysine 27 predicts better prognosis in non-small cell lung cancer

Epigenetic parameters such as DNA methylation and histone modifications play pivotal roles in carcinogenesis. Global histone modification patterns have been implicated as possible predictors of cancer recurrence and prognoses in a great variety of tumor entities. Our study was designed to evaluate the association among trimethylated histone H3 at lysine 27 (H3K27me3), clinicopathological variables and outcome in early-stage non-small cell lung cancer (NSCLC). The expression of H3K27me3 and its methyltransferase, enhancer of zeste homolog 2 (EZH2) together with proliferating cell nuclear antigen (PCNA) were evaluated by immunohistochemistry in normal lung tissue (n=5) and resected NSCLC patients (n=42). In addition, the specificity of antibody for H3K27me3 was tested by western blot analysis. The optimal cut-off point of H3K27me3 expression for prognosis was determined by the X-tile program. The prognostic significance was determined by means of Kaplan-Meier survival estimates and log-rank tests. As a result, enhanced trimethylation of H3K27me3 was correlated with longer overall survival (OS) and better prognosis (P<0.05). Moreover, both univariate and multivariate analyses indicated that H3K27me3 level was a significant and independent predictor of better survival (hazard ratio, 0.187; 95% confidence interval, 0.066-0.531, P=0.002). Furthermore, H3K27me3 expression was positively correlated with DNA methylation level at CCGG sites while reversely related to EZH2 expression (P<0.05). In conclusion, H3K27me3 level defines unrecognized subgroups of NSCLC patients with distinct epigenetic phenotype and clinical outcome, and can probably be used as a novel predictor for better prognosis in NSCLC patients

Histological characterisation and prognostic evaluation of 62 gastric neuroendocrine carcinomas

Aim of the study: To determine the significance of expression of synaptophysin, chromogranin A, and Ki-67 and their association with clinicopathological parameters, and to find out the possible prognostic factors in gastric neuroendocrine carcinoma (G-NEC). Material and methods: We investigated the immunohistochemical features and prognosis of 62 G-NECs, and evaluated the association among expressions of synaptophysin, chromogranin A, and Ki-67, clinicopathological variables, and outcome. Results : Chromogranin A expression was found more commonly in small-cell NECs (9/9, 100%) than in large-cell NECs (27/53, 51%) (p = 0.008). No statistical significance was found in Ki-67 (p = 0.494) or synaptophysin (p > 0.1) expression between NEC cell types. Correlation analyses revealed that Ki-67 expression was significantly associated with mid-third disease of stomach (p = 0.005) and vascular involvement (p = 0.006), and hada trend of significant correlation with tumour relapse (p = 0.078). High expression of chromogranin A was significantly associated with histology of small-cell NECs (p = 0.008) and lesser tumour greatest dimension (p = 0.038). The prognostic significance was determined by means of Kaplan-Meier survival estimates and log-rank tests, and as a result, early TNM staging and postoperative chemotherapy were found to be correlated with longer overall survival (p < 0.05). Univariate analysis revealed associations between poor prognosis in NECs and several factors, including high TNM staging (p = 0.048), vascular involvement (p = 0.023), relapse (p = 0.004), and microscopic/macroscopic residual tumour (R1/2, p < 0.001). In a multivariate analysis, relapse was identified as the sole independent prognostic factor. Conclusions : No significant correlation between survival and expression of synaptophysin, chromogranin A, or Ki-67 has been determined in G-NECs. Our study indicated that early diagnosis, no-residual-tumour resection, and postoperative chemotherapy were possible prognostic factors

DataSheet_2_Molecular subtypes and a prognostic model for hepatocellular carcinoma based on immune- and immunogenic cell death-related lncRNAs.xlsx

BackgroundAccumulating evidence shows that immunogenic cell death (ICD) enhances immunotherapy effectiveness. In this study, we aimed to develop a prognostic model combining ICD, immunity, and long non-coding RNA biomarkers for predicting hepatocellular carcinoma (HCC) outcomes.MethodsImmune- and immunogenic cell death-related lncRNAs (IICDLs) were identified from The Cancer Genome Atlas and Ensembl databases. IICDLs were extracted based on the results of differential expression and univariate Cox analyses and used to generate molecular subtypes using ConsensusClusterPlus. We created a prognostic signature based on IICDLs and a nomogram based on risk scores. Clinical characteristics, immune landscapes, immune checkpoint blocking (ICB) responses, stemness, and chemotherapy responses were also analyzed for different molecular subtypes and risk groups.ResultA total of 81 IICDLs were identified, 20 of which were significantly associated with overall survival (OS) in patients with HCC. Cluster analysis divided patients with HCC into two distinct molecular subtypes (C1 and C2), with patients in C1 having a shorter survival time than those in C2. Four IICDLs (TMEM220-AS1, LINC02362, LINC01554, and LINC02499) were selected to develop a prognostic model that was an independent prognostic factor of HCC outcomes. C1 and the high-risk group had worse OS (hazard ratio > 1.5, p + T cells, B cells, macrophages, neutrophils, and myeloid dendritic cells), low immune checkpoint gene expression, poor response to ICB therapy, and high stemness. Different molecular subtypes and risk groups showed significantly different responses to several chemotherapy drugs, such as doxorubicin (p ConclusionOur study identified molecular subtypes and a prognostic signature based on IICDLs that could help predict the clinical prognosis and treatment response in patients with HCC.</p

DataSheet_1_Molecular subtypes and a prognostic model for hepatocellular carcinoma based on immune- and immunogenic cell death-related lncRNAs.xlsx

BackgroundAccumulating evidence shows that immunogenic cell death (ICD) enhances immunotherapy effectiveness. In this study, we aimed to develop a prognostic model combining ICD, immunity, and long non-coding RNA biomarkers for predicting hepatocellular carcinoma (HCC) outcomes.MethodsImmune- and immunogenic cell death-related lncRNAs (IICDLs) were identified from The Cancer Genome Atlas and Ensembl databases. IICDLs were extracted based on the results of differential expression and univariate Cox analyses and used to generate molecular subtypes using ConsensusClusterPlus. We created a prognostic signature based on IICDLs and a nomogram based on risk scores. Clinical characteristics, immune landscapes, immune checkpoint blocking (ICB) responses, stemness, and chemotherapy responses were also analyzed for different molecular subtypes and risk groups.ResultA total of 81 IICDLs were identified, 20 of which were significantly associated with overall survival (OS) in patients with HCC. Cluster analysis divided patients with HCC into two distinct molecular subtypes (C1 and C2), with patients in C1 having a shorter survival time than those in C2. Four IICDLs (TMEM220-AS1, LINC02362, LINC01554, and LINC02499) were selected to develop a prognostic model that was an independent prognostic factor of HCC outcomes. C1 and the high-risk group had worse OS (hazard ratio > 1.5, p + T cells, B cells, macrophages, neutrophils, and myeloid dendritic cells), low immune checkpoint gene expression, poor response to ICB therapy, and high stemness. Different molecular subtypes and risk groups showed significantly different responses to several chemotherapy drugs, such as doxorubicin (p ConclusionOur study identified molecular subtypes and a prognostic signature based on IICDLs that could help predict the clinical prognosis and treatment response in patients with HCC.</p

DataSheet_3_Molecular subtypes and a prognostic model for hepatocellular carcinoma based on immune- and immunogenic cell death-related lncRNAs.xlsx

BackgroundAccumulating evidence shows that immunogenic cell death (ICD) enhances immunotherapy effectiveness. In this study, we aimed to develop a prognostic model combining ICD, immunity, and long non-coding RNA biomarkers for predicting hepatocellular carcinoma (HCC) outcomes.MethodsImmune- and immunogenic cell death-related lncRNAs (IICDLs) were identified from The Cancer Genome Atlas and Ensembl databases. IICDLs were extracted based on the results of differential expression and univariate Cox analyses and used to generate molecular subtypes using ConsensusClusterPlus. We created a prognostic signature based on IICDLs and a nomogram based on risk scores. Clinical characteristics, immune landscapes, immune checkpoint blocking (ICB) responses, stemness, and chemotherapy responses were also analyzed for different molecular subtypes and risk groups.ResultA total of 81 IICDLs were identified, 20 of which were significantly associated with overall survival (OS) in patients with HCC. Cluster analysis divided patients with HCC into two distinct molecular subtypes (C1 and C2), with patients in C1 having a shorter survival time than those in C2. Four IICDLs (TMEM220-AS1, LINC02362, LINC01554, and LINC02499) were selected to develop a prognostic model that was an independent prognostic factor of HCC outcomes. C1 and the high-risk group had worse OS (hazard ratio > 1.5, p + T cells, B cells, macrophages, neutrophils, and myeloid dendritic cells), low immune checkpoint gene expression, poor response to ICB therapy, and high stemness. Different molecular subtypes and risk groups showed significantly different responses to several chemotherapy drugs, such as doxorubicin (p ConclusionOur study identified molecular subtypes and a prognostic signature based on IICDLs that could help predict the clinical prognosis and treatment response in patients with HCC.</p