Prognosis and Immunotherapy Significances of a Cancer-Associated Fibroblasts-Related Gene Signature in Gliomas - Chen, Zhimin; Zhuo, Shenghua; He, Guiying; Tang, Jingzhi; Hao, Weijie; Gao, Wei-Qiang; Yang, Kun; Xu, Huiming (Frontiers in Cell and Developmental Biology, 2021)
Malignant glioma, as a cold tumor, has strong immunosuppression and immune escape characteristics. The tumor microenvironment (TME) provides the “soil” for the survival of malignant tumors. As the architects of matrix remodeling in TME, cancer-associated fibroblasts (CAFs) can regulate the recruitment and functional differentiation of immune cells by synthesizing and secreting a large number of collagens, cytokines, chemokines, and other soluble factors, which interact with tumor cells to create immunosuppressive TME facilitating immune escape of tumor cells. It is possible to improve TME, enhance the efficacy of immunotherapy by targeting CAFs. Thus, regulation of CAFs and CAFs-related genes are potentially effective immunotherapies for gliomas. Here, we found that the proportion of CAFs in tumor is associated with the clinical and immune characteristics of gliomas based on the Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) database analysis. Moreover, a risk model based on the expression of CAFs-related six-gene for the assessment of glioma patients was constructed using the least absolute shrinkage and selection operator (LASSO) and the results showed that a high-risk group had a higher expression of the CAFs-related six-genes and lower overall survival (OS) rates compared with those in the low-risk group. In addition, the patients in the high-risk group were associated with older age, high tumor grade, isocitrate dehydrogenase (IDH) wildtype, 1p/19q non-codeletion, O-6-methylguanine-DNA methyltransferase (MGMT) promoter unmethylation poor prognosis. Meanwhile, the high-risk subtype had a high proportion CAFs in the TME of glioma, as well as a high expression of immune checkpoint genes. Furthermore, based on the analysis of the Submap algorithm, the high-risk patients responded to anti-PD-1 therapy. In summary, the risk prediction model based on the expression of six-gene can be used as an independent prognostic indicator and a predictor for the response to immunotherapy.