AUTHOR=Lan Yu-Long , Nie Tian , Zou Shuang TITLE=Identification of the prognostic and immunological roles of aquaporin 4: A potential target for survival and immunotherapy in glioma patients JOURNAL=Frontiers in Cellular Neuroscience VOLUME=16 YEAR=2022 URL=https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2022.1061428 DOI=10.3389/fncel.2022.1061428 ISSN=1662-5102 ABSTRACT=

Recent studies have revealed the critical role of AQP4 in the occurrence and development of gliomas. However, the role of AQP4 in immune regulation has not yet been reported. Many recent reports have identified the lymphatic system’s occurrence within the central nervous system (CNS) and the vital role of immune regulation in treating brain tumors. Therefore, the present study aimed to explore the role of AQP4 in the immune regulation of glioma. We used bioinformatics analysis to investigate the immunoregulatory function of AQP4, including its correlation with immunity, anti-tumor immune processes, immunotherapy, immune infiltration, tumor mutational burden (TMB), stemness, mutation, and pan-cancer. The results revealed that AQP4 was significantly associated with the expression of multiple immune checkpoints, immune cells, as well as multiple immune cell effector genes, and antigen presentation and processing abilities. Although no significant correlation was found between the AQP4 gene and IDH mutation and MGMT, AQP4 demonstrated substantial expression differences in different immunophenotypes and molecular types. Using the TTD database, we discovered that EGFR, ABAT, and PDGFRA are strongly associated with AQP4 expression in the glioblastoma (GBM) classification, and these factors could be the potential AQP4-related immunotherapy targets. Afterward, we screened the differential genes in the high and low AQP4 gene expression group, the high and low immune score group, and the high and low matrix score group and took the intersection as the candidate factor. Finally, univariate Cox analysis was used to find eight prognostic variables with significant differences across the candidate genes. After lasso dimensionality reduction, three genes built the model (RARRES1, SOCS3, and TTYH1). The scoring model generated by the three genes was eventually obtained after the multi-factor screening of the three genes. Finally, combined with clinical information and cox regression analysis, it was further confirmed that the model score could be used as an independent prognostic factor.