Background: Age is associated with the prognosis of glioma patients, but there is no uniform standard of age-group classification to evaluate the prognosis of glioma patients. In this study, we aimed to establish an age group classification for risk stratification in glioma patients. Methods: 1502 patients diagnosed with gliomas at Nanfang Hospital between 2000 and 2018 were enrolled. The WHO grade of glioma was used as a dependent variable to evaluate the effect of age on risk stratification. The evaluation model was established by logistic regression, and the Akaike information criterion (AIC) value of the model was used to determine the optimal cutoff points for age-classification. The differences in gender, WHO grade, pathological subtype, tumor cell differentiation, tumor size, tumor location, and molecular markers between different age groups were analyzed. The molecular markers included GFAP, EMA, MGMT, P53, NeuN, Oligo2, EGFR, VEGF, IDH1, Ki-67, PR, CD3, H3K27M, TS, and 1p/19q status. Results: The proportion of men with glioma was higher than that of women with glioma (58.3% vs 41.7%). Analysis of age showed that appropriate classifications of age group were 0–14 years old (pediatric group), 15–47 years old (youth group), 48–63 years old (middle-aged group), and ≥ 64 years old (elderly group).The proportions of glioblastoma and large tumor size (4–6 cm) increased with age (p = 0.000, p = 0.018, respectively). Analysis of the pathological molecular markers across the four age groups showed that the proportion of patients with larger than 10% area of Ki-67 expression or positive PR expression increased with age (p = 0.000, p = 0.017, respectively). Conclusions: Appropriate classifications of the age group for risk stratification are 0–14 years old (pediatric group), 15–47 years old (young group), 48–63 years old (middle age group) and ≥ 64 years old (elderly group). This age group classification is effective in evaluating the risk of glioblastoma in glioma patients.

Background: Gastric cancer (GC) is a digestive system tumor with high morbidity and mortality. It is urgently required to identify genes to elucidate the underlying molecular mechanisms. The aim of this study is to identify the key genes which may affect the prognosis of GC patients and be a therapeutic strategy for GC patients by bioinformatic analysis.Methods: The significant prognostic differentially expressed genes (DEGs) were screened out from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. The protein–protein interaction (PPI) network was established by STRING and screening key genes by MCODE and CytoNCA plug-ins in Cytoscape. Functional enrichment analysis, construction of a prognostic risk model, and nomograms verify key genes as potential therapeutic targets.Results: In total, 997 genes and 805 genes were related to the prognosis of GC in the GSE84437 and TCGA datasets, respectively. We define the 128 genes shared by the two datasets as prognostic DEGs (P-DEGs). Then, the first four genes (MYLK, MYL9, LUM, and CAV1) with great node importance in the PPI network of P-DEGs were identified as key genes. Independent prognostic risk analysis found that patients with high key gene expression had a poor prognosis, excluding their age, gender, and TNM stage. GO and KEGG enrichment analyses showed that key genes may exert influence through the PI3K-Akt pathway, in which extracellular matrix organization and focal adhesion may play important roles in key genes influencing the prognosis of GC patients.Conclusion: We found that MYLK, MYL9, LUM, and CAV1 are potential and reliable prognostic key genes that affect the invasion and migration of gastric cancer.

Pleomorphic xanthoastrocytoma (PXA), anaplastic pleomorphic xanthoastrocytoma (A-PXA), and epithelioid glioblastoma (E-GBM) show overlapping features. However, little is known about their clinical characteristics, molecular features and relationship with progression. Fourteen patients diagnosed at Nanfang Hospital from 2016 to 2019 were enroled, including eleven PXA patients, two A-PXA patients, and one E-GBM patient. All tumour tissue samples of the fourteen patients were examined by immunohistochemical staining (MGMT, VEGF, BRAF-V600E, etc.). The mean age of 13 patients with PXA or A-PXA was 25.4 years; twelve of these patients had tumours at supratentorial regions. VEGF positivity was detected in the tumour samples of 13 patients, MGMT positivity in 10 patients, and BRAF-V600E positivity in 7 patients. The recurrent tumour tissue of the patient with E-GBM arising from A-PXA was screened to detect 11 glioma markers (MGMT, BRAF-V600E, etc.) and chromosome 1p/19q by next-generation sequencing (NGS). For the tumour sample of the E-GBM patient who survived for up to 11 years after the fourth resection, BRAF V600E was wild type in the sample obtained from the first surgery, while it was mutant in the second, third, and fourth surgeries. In contrast, the promoter status of MGMT in the four surgeries was unmethylated. The NGS results showed that the mutation frequencies of BRAF V600E in the second, third and fourth surgeries were 14.06%, 9.13% and 48.29%, respectively. Collectively, the results suggest that patients with A-PXA may relapse multiple times and eventually progress to E-GBM with the BRAF-V600E mutation.