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Background: Immune checkpoint inhibitors have been shown to promote antitumor immunity and achieve durable tumor remissions. However, certain tumors are refractory to current immunotherapy. These negative results encouraged us to uncover other therapeutic targets and strategies. PTPN2 (protein tyrosine phosphatase, non-receptor type 2) has been newly identified as an immunotherapy target. Loss of PTPN2 sensitizes the tumor to immunotherapy via IFNγ signaling., Methods: Here, we investigated the relationship between PTPN2 mRNA levels and clinical characteristics in gliomas. RNA-seq data of a cohort of 325 patients with glioma were available from the Chinese Glioma Genome Atlas and 671 from The Cancer Genome Atlas. R language, GraphPad Prism 5, and SPSS 22.0 were used to analyze data and draw figures., Results: PTPN2 transcript levels increased significantly with higher grades of glioma and in isocitrate dehydrogenase (IDH) wild-type and mesenchymal subtype gliomas. A comprehensive biological analysis was conducted, which indicated a crucial role of PTPN2 in the immune and inflammation responses in gliomas. Specifically, PTPN2 was positively associated with HCK, LCK, MHC II, and STAT1 but negatively related to IgG and interferon. Moreover, canonical correlation analysis showed a positive correlation of PTPN2 with infiltrating immune cells, such as macrophages, neutrophils, and CD8 + T cells. Clinically, higher levels of PTPN2 were associated with a worse overall survival both in patients with gliomas and glioblastomas., Conclusion: PTPN2 expression level was increased in glioblastomas and associated with gliomas of the IDH wild-type and mesenchymal subtype. There was a close correlation between PTPN2 and the immune response and inflammatory activity in gliomas. Our results show that PTPN2 is a promising immunotherapy target and may provide additional treatment strategies.

Introduction: We have previously indicated that CXCL3 was upregulated in the tissues of prostate cancer, and exogenous administration of CXCL3 played a predominant role in the tumorigenicity of prostate cancer cells. In the present study, we further explored the role and the underlying mechanism of CXCL3 overexpression in the oncogenic potential of prostate cancer in an autocrine/paracrine fashion., Methods: CXCL3-overexpressing prostate cancer cell line PC-3 and immortalized prostate stromal cell line WPMY-1 were established by gene transfection. CCK-8, transwell assays and growth of tumor xenografts were conducted to characterize the effects of CXCL3 on PC-3 cells' proliferation and migration. Western blotting was conducted to test whether CXCL3 could affect the expression of tumorigenesis-associated genes., Results: The results showed that CXCL3 overexpression in PC-3 cells and the PC-3 cells treated with the supernatants of CXCL3-transfected WPMY-1 cells stimulated the proliferation and migration of PC-3 cells in vitro and in a nude mouse xenograft model. Western blotting revealed higher levels of p-ERK, Akt and Bcl-2 and lower levels of Bax in the tumor xenografts transplanted with CXCL3-transfected PC-3 cells. Moreover, the tumor xenografts derived from the PC-3 cells treated with supernatants of CXCL3-transfected WPMY-1 cells showed higher expression of ERK, Akt and Bcl-2 and lower expression of Bax., Conclusions: These findings suggest that CXCL3 autocrine/paracrine pathways are involved in the development of prostate cancer by regulating the expression of the target genes that are related to the progression of malignancies.

Aim: No comprehensive and objective methods yet exist for predicting postoperative seizure., Patients & Methods: mRNA-seq data and corresponding postoperative seizure status of 109 low-grade glioma samples were obtained from Chinese Glioma Genome Atlas database and divided into two sets randomly. Logistic regression and receiver operating characteristic analysis with risk score method were used to develop a ten-gene prediction model., Results: Considering gene number and area under the curve of receiver operating characteristic, a ten-gene model was generated which showed an area under the curve of 0.9965 in training set. Patients with high-risk scores had higher probability of postoperative seizure compared with those with low-risk scores., Conclusion: This is the first prediction model for postoperative seizures in gliomas, integrating multiple genes. Clinical application may help patients with postoperative seizure control.

Anaplastic gliomas are characterized by variable clinical and genetic features, but there are few studies focusing on the substratification of anaplastic gliomas. To identify a more objective and applicable classification of anaplastic gliomas, we analyzed whole genome mRNA expression profiling of four independent datasets. Univariate Cox regression, linear risk score formula and receiver operating characteristic (ROC) curve were applied to derive a gene signature with best prognostic performance. The corresponding clinical and molecular information were further analyzed for interpretation of the different prognosis and the independence of the signature. Gene ontology (GO), Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) were performed for functional annotation of the differences. We found a three-gene signature, by applying which, the anaplastic gliomas could be divided into low risk and high risk groups. The two groups showed a high concordance with grade II and grade IV gliomas, respectively. The high risk group was more aggressive and complex. The three-gene signature showed diagnostic and prognostic value in anaplastic gliomas.

Introduction: Gliomas are the most common primary brain tumors in adults and a significant cause of cancer-related mortality. A 9-gene signature was identified as a novel prognostic model reflecting survival situation obviously in gliomas., Aims: To identify an mRNA expression signature to improve outcome prediction for patients with different glioma grades., Results: We used whole-genome mRNA expression microarray data of 220 glioma samples of all grades from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn) as a discovery set and data from Rembrandt and GSE16011 for validation sets. Data from every single grade were analyzed by the Kaplan-Meier method with a two-sided log-rank test. Univariate Cox regression and linear risk score formula were applied to derive a gene signature with better prognostic performance. We found that patients who had high risk score according to the signature had poor overall survival compared with patients who had low risk score. Highly expressed genes in the high-risk group were analyzed by gene ontology (GO) and gene set variation analysis (GSVA). As a result, the reason for the divisibility of gliomas was likely due to cell life processes and adhesion., Conclusion: This 9-gene-signature prediction model provided a more accurate predictor of prognosis that denoted patients with high risk score have poor outcome. Moreover, these risk models based on defined molecular profiles showed the considerable prospect in personalized cancer management., (© 2013 John Wiley & Sons Ltd.)

Background: The Cancer Genome Atlas (TCGA) has divided patients with glioblastoma multiforme (GBM) into four subtypes based on mRNA expression microarray. The mesenchymal subtype, with a larger proportion, is considered a more lethal one. Clinical outcome prediction is required to better guide more personalized treatment for these patients., Aims: The objective of this study was to identify a mRNA expression signature to improve outcome prediction for patients with mesenchymal GBM., Results: For signature identification and validation, we downloaded mRNA expression microarray data from TCGA as training set and data from Rembrandt and GSE16011 as validation set. Cox regression and risk-score analysis were used to develop the 4 signatures, which were function and prognosis associated as revealed by Gene Ontology (GO) analysis and Gene Set Variation Analysis (GSVA). Patients who had high-risk scores according to the signatures had poor overall survival compared with patients who had low-risk scores., Conclusions: The signatures were identified as risk predictors that patients who had a high-risk score tended to have unfavorable outcome, demonstrating their potential for personalizing cancer management., (© 2013 John Wiley & Sons Ltd.)