PDIA5 is Correlated With Immune Infiltration and Predicts Poor Prognosis in Gliomas.

Gliomas are the most common and lethal primary malignant tumor of the brain. Routine treatment including surgical resection, chemotherapy, and radiotherapy produced limited therapeutic effect, while immunotherapy targeting the glioma microenvironment has offered a novel therapeutic option. PDIA5 protein is the member of PDI family, which is highly expressed in glioma and participates in glioma progression. Based on large-scale bioinformatics analysis, we discovered that PDIA5 expression level is upregulated in aggressive gliomas, with high PDIA5 expression predicting poor clinical outcomes. We also observed positive correlation between PDIA5 and immune infiltrating cells, immune related pathways, inflammatory activities, and other immune checkpoint members. Patients with high PDIA5 high-expression benefited from immunotherapies. Additionally, immunohistochemistry revealed that PDIA5 and macrophage biomarker CD68 were upregulated in high-grade gliomas, and patients with low PDIA5 level experienced favorable outcomes among 33 glioma patients. Single cell RNA sequencing exhibited that PDIA5 was in high level presenting in neoplastic cells and macrophages. Cell transfection and co-culture of glioma cells and macrophages revealed that PDIA5 in tumor cells mediated macrophages exhausting. Altogether, our findings indicate that PDIA5 overexpression is associated with immune infiltration in gliomas, and may be a promising therapeutic target for glioma immunotherapy.

Circadian clock genes promote glioma progression by affecting tumour immune infiltration and tumour cell proliferation.

OBJECTIVES: Circadian rhythm controls complicated physiological activities in organisms. Circadian clock genes have been related to tumour progression, but its role in glioma is unknown. Therefore, we explored the relationship between dysregulated circadian clock genes and glioma progression. MATERIALS AND METHODS: Samples were divided into different groups based on circadian clock gene expression in training dataset (n = 672) and we verified the results in other four validating datasets (n = 1570). The GO and GSEA enrichment analysis were conducted to explore potential mechanism of how circadian clock genes affected glioma progression. The single-cell RNA-Seq analysis was conducted to verified previous results. The immune landscape was evaluated by the ssGSEA and CIBERSORT algorithm. Cell proliferation and viability were confirmed by the CCK8 assay, colony-forming assay and flow cytometry. RESULTS: The cluster and risk model based on circadian clock gene expression can predict survival outcome. Samples were scoring by the least absolute shrinkage and selection operator regression analysis, and high scoring tumour was associated with worse survival outcome. Samples in high-risk group manifested higher activation of immune pathway and cell cycle. Tumour immune landscape suggested high-risk tumour infiltrated more immunocytes and more sensitivity to immunotherapy. Interfering TIMELESS expression affected circadian clock gene expression, inhibited tumour cell proliferation and arrested cell cycle at the G0/G1 phase. CONCLUSIONS: Dysregulated circadian clock gene expression can affect glioma progression by affecting tumour immune landscape and cell cycle. The risk model can predict glioma survival outcome, and this model can also be applied to pan-cancer.

A novel integrated system using patient-derived glioma cerebral organoids and xenografts for disease modeling and drug screening.

A physiologically relevant glioma tumor model is important to the study of disease progression and screening drug candidates. However, current preclinical glioma models lack the brain microenvironment, and the established tumor cell lines do not represent glioma biology and cannot be used to evaluate the therapeutic effect. Here, we reported a real-time integrated system by generating 3D ex vivo cerebral organoids and in vivo xenograft tumors based on glioma patient-derived tissues and cells. Our system faithfully recapitulated the histological features, response to chemotherapy drugs, and clinical progression of their corresponding parental tumors. Additionally, our model successfully identified a case from a grade II astrocytoma patient with typical grade IV GBM features in both organoids and xenograft models, which mimicked the disease progression of this patient. Further genomic and transcriptomic characterization was associated with individual clinical features. We have demonstrated the "GBM-&Normal-like" signature to predict prognosis. In conclusion, we developed an integrated system of parallel models from patient-derived glioma cerebral organoids and xenografts for understanding the glioma biology and prediction of response to chemotherapy drugs, which might lead to a new strategy for personalized treatment for this deadly disease.

CD96, a new immune checkpoint, correlates with immune profile and clinical outcome of glioma.

CD96 is a promising candidate for immunotherapy. However, its role and importance in glioma remains unknown. We thus aimed to genetically and clinically characterize CD96 expression in gliomas. For this, we extracted RNA-seq data of 699 glioma samples from the TCGA dataset and validated these findings using the CGGA dataset comprising 325 glioma samples. Clinical and isocitrate dehydrogenase (IDH) mutation status were also analyzed. Various packages in R language were mainly used for statistical analysis. CD96 expression was significantly up-regulated in high-grade, IDH-wildtype, and mesenchymal-molecular subtype gliomas based on TCGA data, which was validated using the CGGA dataset. Subsequent gene ontology analysis of both datasets suggested that genes relevant to CD96 are mainly involved in immune functions in glioma as such genes were positively correlated with CD96 expression. To further explore the relationship between CD96 and immune responses, we selected seven immune-related metagenes and found that CD96 expression was positively correlated with HCK, LCK, and MHC II in the CGGA and TCGA cohorts but negatively associated with IgG. Further, Pearson correlation analysis showed that CD96 is associated with TIGIT, CD226, CRTAM, TIM-3, PD-L1, CTLA-4, and STAT3, indicating the additive antitumoral effects of these checkpoint proteins. CD96 was also suggested to play an important role in immune responses and positively collaborate with other checkpoint members. These findings show that CD96 is promising candidate for immunotherapy, and that such agents could complement current immunotherapy strategies for glioma.