Glioma tissues consist of not only glioma cells but also glioma-associated nontumor cells, such as stromal cells and immune cells. These nontumor cells dilute the purity of glioma cells and play important roles in glioma biology. Currently, the implications of variation in glioma purity are not sufficiently clarified. Here, tumor purity was inferred for 2,249 gliomas and 29 normal brain tissues from 5 cohorts. Based on the transcriptomic profiling method, we classified CGGA and TCGA-RNAseq cohorts as the RNAseq set for discovery. Cases from TCGA-microarray, REMBRANDT, and GSE16011 cohorts were grouped as a microarray set for validation. Tissues from the CGGA cohort were reviewed for histopathologic validation. We found that glioma purity was highly associated with major clinical and molecular features. Low purity cases were more likely to be diagnosed as malignant entities and independently correlated with reduced survival time. Integrating glioma purity into prognostic nomogram significantly improved the predictive validity. Moreover, most recognized prognostic indicators were no longer significantly effective under different purity conditions. These results highlighted the clinical importance of glioma purity. Further analyses found distinct genomic patterns associated with glioma purity. Low purity cases were distinguished by enhanced immune phenotypes. Macrophages, microglia, and neutrophils were mutually associated and enriched in low purity gliomas, whereas only macrophages and neutrophils served as robust indicators for poor prognosis. Glioma purity and relevant nontumor cells within microenvironment confer important clinical, genomic, and biological implications, which should be fully valued for precise classification and clinical prediction. .

We profiled the immune status in glioma and established a local immune signature for GBM, which could independently identify patients with a high risk of reduced survival, indicating the relationship between prognosis and local immune response.

Although O(6)-methylguanine DNA methyltransferase (MGMT) promoter methylation status is an important marker for glioblastoma multiforme (GBM), there is considerable variability in the clinical outcome of patients with similar methylation profiles. The present study aimed to refine the prognostic and predictive value of MGMT promoter status in GBM by identifying a micro (mi)RNA risk signature. Data from The Cancer Genome Atlas was used for this study, with MGMT promoter-methylated samples randomly divided into training and internal validation sets. Data from The Chinese Glioma Genome Atlas was used for independent validation. A five miRNA-based risk signature was established for MGMT promoter-methylated GBM to distinguish cases as high- or low-risk with distinct prognoses, which was confirmed using internal and external validation sets. Importantly, the prognostic value of the signature was significant in different cohorts stratified by clinicopathologic factors and alkylating chemotherapy, and a multivariate Cox analysis found it to be an independent prognostic marker along with age and chemotherapy. Based on these three factors, we developed a quantitative model with greater accuracy for predicting the 1-year survival of patients with MGMT promoter-methylated GBM. These results indicate that the five-miRNA signature is an independent risk predictor for GBM with MGMT promoter methylation and can be used to identify patients at high risk of unfavorable outcome and resistant to alkylating chemotherapy, underscoring its potential for personalized GBM management.

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Isocitrate dehydrogenase (IDH)1 mutation is one of the most important genetic aberrations in glioma. Even several genetic events have refined its prognostic value, the genome-wide expression alteration has not been systematically profiled. In this work, RNA-seq expression data from 310 patients in the Chinese Glioma Genome Atlas database were included as training set, while another 297 patients with microarray data were used as internal validation set. An independent cohort of GSE16011 (n = 205) constituted an external validation set. Approximately one fifth of the genes were differentially expressed in LGG according to IDH1 mutation status, yielding distinct gene expression profiles. A six-gene risk signature was established for IDH1-mutant LGG to distinguish low- from high-risk cases, which had distinct prognoses. The six-gene signature was an independent prognostic factor for IDH1-mutant LGG and had superior predictive value as compared to traditional clinicopathologic factors. Moreover, we depicted the differential expression pattern in GBM attributing to various IDH1 status, which was similar to that of LGG. It suggested that the effect of IDH1 mutation is conserved across histological classifications. The six-gene signature had equal prognostic value for IDH1-mutant GBM. By combining glioma grade, IDH1 status, and the six-gene signature, all glioma patients could be classified into six subgroups. These six subgroups could be further summarized into three sets with distinct prognosis. Taken together, a gene expression profile associated with IDH1 status was identified in LGG and GBM; a risk signature based on six genes was developed with equal prognostic value for IDH1-mutant LGG and GBM. When combined with clinicopathologic factors, the six-gene signature is a tool that enables precise risk stratification and can improve clinical management.

Immunity is an important physiological function acquired throughout evolution as a defense system against the invasion of pathogenic microorganisms. The immune system also eliminates senescent cells and maintains homeostasis, monitoring cell mutations and preventing tumor development via the action of the immune cells and molecules. Immunotherapy often relies on the interaction of immune cells with the tumor microenvironment (TME). Based on the distribution of the number of lymphocytes (CD3 and CD8) in the center and edge of the tumor and the expression level of B7-H1/PD-L1, tumors are divided into hot tumors, cold tumors, and intermediate tumors (including immune-suppressed and isolated). This review focuses on the advances in precision combination immunotherapy, which has been widely explored in recent years, and its application in different tumor types.

OBJECTIVE Glioblastoma (GBM) is the most common and lethal type of malignant glioma. The Cancer Genome Atlas divides the gene expression-based classification of GBM into classical, mesenchymal, neural, and proneural subtypes, which is important for understanding GBM etiology and for designing effective personalized therapy. Signal transducer and activator of transcription 3 (STAT3), a critical transcriptional activator in tumorigenesis, is persistently phosphorylated and associated with an unfavorable prognosis in GBM. Although a set of specific targets has been identified, there have been no systematic analyses of STAT3 signaling based on GBM subtype. METHODS This study compared STAT3-associated messenger RNA, protein, and microRNA expression profiles across different subtypes of GBM. RESULTS The analyses revealed a prominent role for STAT3 in the mesenchymal but not in other GBM subtypes, which can be reliably used to classify patients with mesenchymal GBM into 2 groups according to phosphorylated STAT3 expression level. Differentially expressed genes suggest an association between Notch and STAT3 signaling in the mesenchymal subtype. Their association was validated in the U87 cell, a malignant glioma cell line annotated as mesenchymal subtype. Specific associated proteins and microRNAs further profile the STAT3 signaling among GBM subtypes. CONCLUSIONS These findings suggest a prominent role for STAT3 signaling in mesenchymal GBM and highlight the importance of identifying signaling pathways that contribute to specific cancer subtypes.