A Focal Adhesion-Related Gene Signature Predicts Prognosis in Glioma and Correlates With Radiation Response and Immune Microenvironment

Haonan Li; Guohui Wang; Wenyan Wang; Jie Pan; Huandi Zhou; Xuetao Han; Linlin Su; Zhenghui Ma; Liubing Hou; Xiaoying Xue

doi:10.3389/fonc.2021.698278

A Focal Adhesion-Related Gene Signature Predicts Prognosis in Glioma and Correlates With Radiation Response and Immune Microenvironment

Front Oncol. 2021 Sep 22:11:698278. doi: 10.3389/fonc.2021.698278. eCollection 2021.

Authors

Haonan Li^{1

2}, Guohui Wang^{1

3}, Wenyan Wang¹, Jie Pan⁴, Huandi Zhou^{1

2}, Xuetao Han¹, Linlin Su¹, Zhenghui Ma¹, Liubing Hou^{1

2}, Xiaoying Xue¹

Affiliations

¹ Department of Radiotherapy, Second Hospital of Hebei Medical University, Shijiazhuang, China.
² Department of Central Laboratory, Second Hospital of Hebei Medical University, Shijiazhuang, China.
³ Department of Radiation Oncology, PekingUniversity China-Japan Friendship School of Clinical Medicine, Beijing, China.
⁴ The Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States.

Abstract

Background: Glioma is the most frequent brain malignancy presenting very poor prognosis and high recurrence rate. Focal adhesion complexes play pivotal roles in cell migration and act as hubs of several signaling pathways.

Methods: We used bioinformatic databases (CGGA, TCGA, and GEO) and identified a focal adhesion-related differential gene expression (FADG) signature by uniCox and LASSO regression analysis. We calculated the risk score of every patient using the regression coefficient value and expression of each gene. Survival analysis, receiver operating characteristic curve (ROC), principal component analysis (PCA), and stratified analysis were used to validate the FADG signature. Then, we conducted GSEA to identify the signaling pathways related to the FADG signature. Correlation analysis of risk scores between the immune checkpoint was performed. In addition, the correlation of risk scores and genes related with DNA repair was performed. CIBERSORT and ssGSEA were used to explore the tumor microenvironment (TME). A nomogram that involved our FADG signature was also constructed.

Results: In total, 1,726 (528 patients diagnosed with WHO II, 591 WHO III, and 603 WHO IV) cases and 23 normal samples were included in our study. We identified 29 prognosis-related genes in the LASSO analysis and constructed an eight FADG signature. The results from the survival analysis, stratified analysis, ROC curve, and univariate and multivariate regression analysis revealed that the prognosis of the high-risk group was significantly worse than the low-risk group. Correlation analysis between risk score and genes that related with DNA repair showed that the risk score was positively related with BRCA1, BRCA2, RAD51, TGFB1, and TP53. Besides, we found that the signature could predict the prognosis of patients who received radiation therapy. SsGSEA indicated that the high-risk score was positively correlated with the ESTIMATE, immune, and stromal scores but negatively correlated with tumor purity. Notably, patients in the high-risk group had a high infiltration of immunocytes. The correlation analysis revealed that the risk score was positively correlated with B7-H3, CTLA4, LAG3, PD-L1, and TIM3 but inversely correlated with PD-1.

Conclusion: The FADG signature we constructed could provide a sensitive prognostic model for patients with glioma and contribute to improve immunotherapy management guidelines.

Keywords: focal adhesion; glioma; immune checkpoints; prognosis; radiation response; tumor microenvironment.