ObjectiveGliomas are the most aggressive intracranial tumors accounting for the vast majority of brain tumors with very poor prognosis and overall survival (OS). Cancer-derived immunoglobulin G (cancer-IgG) has been found to be widely expressed in several malignancies such as breast cancer, colorectal cancer, and lung cancer. Cancer-IgG could promote tumorigenesis and progression. However, its role in glioma has not been revealed yet.MethodsWe mined open databases including the Chinese Glioma Genome Atlas (CGGA), The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO) to study the role of IGHG1, which encodes cancer-IgG in glioma. Examination of the differential expression of IGHG1 was carried out in the GEO and TCGA databases. Furthermore, its expression in different molecular subtypes was analyzed. Stratified analysis was performed with clinical features. Subsequently, immune infiltration analysis was conducted using single-sample gene set enrichment analysis (ssGSEA). GSEA was performed to reveal the mechanisms of IGHG1. Lastly, immunohistochemistry was processed to validate our findings.ResultsIn this study, we found that the expression of IGHG1 was higher in glioma and molecular subtypes with poor prognosis. The overall survival of patients with a high expression of IGHG1 was worse in the stratified analysis. Immune infiltration analysis indicated that the expression level of IGHG1 was positively correlated with the stromal score, ESTIMATE score, and immune score and negatively correlated with tumor purity. Results from the GSEA and DAVID demonstrated that IGHG1 may function in phagosome, antigen processing and presentation, extracellular matrix structural constituent, antigen binding, and collagen-containing extracellular matrix. Finally, immunohistochemistry assay validated our findings that patients with a high expression of cancer-IgG had poor OS and disease-free survival (DFS).ConclusionCancer-IgG is a promising biomarker of diagnosis and treatment for patients with glioma.

Objectives. Although patients with grade 2 glioma have a relatively better prognosis and longer survival than those with high-grade glioma, there are still a number of patients with disappointing outcomes. In order to accurately predict the prognosis of patients, relevant risk factors were included in the analysis to establish a clinical prediction model so as to provide a basis for clinically individualized treatment. Methods. A retrospective study was conducted in patients diagnosed with grade 2 glioma. Data including clinical features, pathological type, molecular classification, neuroimaging examination, treatment, and survival were collected. The data sets were randomly assigned, with 80% of the data used for model building and 20% for validation. Cox proportional hazard regression analysis was used to construct the model using important risk factors and present it in the form of a nomogram. The nomogram was evaluated a using C-index and calibration chart. Results. A total of 160 patients were enrolled in this analysis, including 128 in the training group and 32 in the validation group. In the training group, eight important risk factors including preoperative KPS, the first presenting symptom, the extent of resection, the gross tumor size, 1p19q, IDH, radiotherapy, and chemotherapy were identified to construct the model. The C-index of the training group and the validation group was 0.832 and 0.801, respectively, indicating that the model had good prediction ability. The calibration charts of the two groups were drawn respectively, which showed that the calibration line and the standard line had a good consistency, which suggested that the model-predicted risk had a good consistency with the actual risk. Conclusions. Based on the data of our center, a nomogram prediction model with eight variables has been established as an off-the-rack tool and verified its accuracy, which can guide clinical work and provide consultation for patients.

BackgroundAberrant DNA methylation of tumor suppressor genes is a common event in the development and progression of gastric cancer(GC). Our previous study showed NDRG1, which could suppress cell invasion and migration, was frequently down-regulated by DNA methylation of its promoter in GC.Purpose and MethodsTo analyze the relationship between the expression and DNA methylation of NDRG1 and DNA methyltransferase (DNMT) family. We performed a comprehensive comparison analysis using 407 patients including sequencing analysis data of GC from TCGA.ResultsNDRG1 was negatively correlative to DNMT1 (p =0.03), DNMT3A(p =0.01), DNMT3B(p =0.88), respectively. Whereas, the DNA methylation of NDRG1 was positively correlative to DNMT family(DNMT1 p<0.01, DNMT3A p<0.001, DNMT3B p=0.57, respectively). NDRG1 expression was significantly inverse correlated with invasion depth (p =0.023), and DNMT1 was significantly positive correlated with the degree of tumor cell differentiation (p =0.049). DNMT3B was significantly correlated with tumor cell differentiation (p =0.030). However, there was no association between the expression of DNMT3A and clinicopathological features. The univariate analysis showed that NDRG1and DNMTs had no association with prognosis of GC patients. But, multivariate analysis showed DNMT1 was significantly correlated with prognosis of GC patients.ConclusionThese data suggest that down-regulation of NDRG1 in gastric cancer is due to DNA methylation of NDRG1 gene promoter via DNMT family. The demethylating agent maybe a potential target drug for GC patients.