EVA1C Is a Potential Prognostic Biomarker and Correlated With Immune Infiltration Levels in WHO Grade II/III Glioma

BACKGROUND

Immunotherapy is an effective therapeutic approach for multiple human cancer types. However, the correlations between EVA1C and patients' prognosis as well as immune infiltration remain obscure. Herein, we employed transcriptomic and clinical data extracted from two independent databases to systematically investigate the role of EVA1C in the oncological context.

METHODS

The differential expression of EVA1C was analyzed via TCGA and Oncomine databases. We evaluated the influence of EVA1C on clinical prognosis using Kaplan-Meier plotter. We then used the expression profiler to calculate stromal score, immune score, and ESTIMATE score based on the ESTIMATE algorithm. The abundance of infiltrating immune cells was calculated via TIMER. The correlations between EVA1C expression and immune infiltration levels were analyzed in two independent cohorts.

RESULTS

In patients with World Health Organization (WHO) grade II/III glioma, high EVA1C expression was associated with malignant clinicopathological features and poor overall survival in both cohorts. EVA1C expression was positively associated with immune infiltration levels of B cell, CD4+ T cell, neutrophil, macrophage, and dendritic cells (DCs). Besides, EVA1C expression strongly correlated with diverse immune marker sets. And the predictive power of EVA1C was better than that of other indicators in predicting high immune infiltration levels in glioma.

CONCLUSIONS

For the first time, we identified the overexpression of EVA1C in glioma, which was tightly correlated with the high infiltration levels of multiple immune cells as well as poor prognosis. Meanwhile, EVA1C might be a potential biomarker for predicting high immune infiltration in WHO grade II/III gliomas.

Transcription factor CASZ1 increases an oncogenic transcriptional process in tumorigenesis and progression of glioma cells

As a transcription factor, the role of CASZ1 in different entities is inconsistent. Glioma is one of the leading causes of cancer death worldwide. Its prognostic relevance and biological functions in glioma remain obscure. We focused on the role, mechanism, and prognostic value of CASZ1 in glioma cells. Herein, CASZ1 was identified as a novel potential oncogene in glioma tissues from GEO and TCGA datasets. CASZ1 was highly expressed in glioma tissues, predicting poor prognosis in glioma patients. Knockdown of CASZ1 inhibited proliferation and invasion in vitro, whereas upregulation of CASZ1 presented opposite results. Overexpression of CASZ1 increased transcriptional process of target gene p75NTR. CASZ1 was the potential transcriptional regulators for p75NTR. In addition, the p75NTR expression is essential for CASZ1 to exert its function as an oncogene. Our findings indicate that highly expressed CASZ1 in glioma cells acts as a pro-oncogene factor in gliomas via regulating transcriptional process of target gene p75NTR, which was identified as an unfavorable prognostic marker in patients with gliomas. CASZ1 is expected to become a novel target for the treatment of gliomas.

Screening of autophagy genes as prognostic indicators for glioma patients

Although autophagy is reported to be involved in tumorigenesis and cancer progression, its correlation with the prognosis of glioma patients remains unclear. Thus, the aim of this study was to identify prognostic autophagy-related genes, analyze their correlation with clinicopathological features of glioma, and further construct a prognostic model for glioma patients. After 139 autophagy-related genes were obtained from the GeneCards database, their expression data in glioma patients were extracted from the Chinese Glioma Genome Atlas database. Univariate and multivariate COX regression analyses were performed to identify prognostic autophagy-related genes. Ten hub autophagy-related genes associated with prognosis were identified. The autophagy risk score (ARS) was only positively correlated with histopathology (P = 0.000) and World Health Organization grade (P = 0.000). Kaplan-Meier analysis showed that the overall survival of patients with a high ARS was significantly worse than that of patients with a low ARS (hazard ratio = 1.59, 95% confidence interval = 1.25-2.03, P = 0.0001). In addition, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed several common biological processes and signaling pathways related to the 10 hub genes in glioblastoma. A prediction model was developed for glioma patients, which demonstrated high prediction efficiency on calibration. Moreover, the area under the receiver operating characteristic curve values for 1-, 3- and 5-year survival probabilities were 0.790, 0.861, and 0.853, respectively. In conclusion, we identified 10 autophagy-related genes that can serve as novel prognostic biomarkers for glioma patients. Our prediction model accurately predicted patient outcomes, and thus, may be a valuable tool in clinical practice.