The CXCL Family Contributes to Immunosuppressive Microenvironment in Gliomas and Assists in Gliomas Chemotherapy

• Published in: Frontiers in immunology Vol. 12; p. 731751
• Main Authors: Wang, Zeyu, Liu, Yuze, Mo, Yuyao, Zhang, Hao, Dai, Ziyu, Zhang, Xun, Ye, Weijie, Cao, Hui, Liu, Zhixiong, Cheng, Quan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 13.09.2021
• Subjects: Antineoplastic Agents - therapeutic use; Brain Neoplasms - drug therapy; Brain Neoplasms - genetics; Brain Neoplasms - immunology; Brain Neoplasms - metabolism; Chemokines, CXC - genetics; Chemokines, CXC - metabolism; chemotherapy; Clinical Decision-Making; CXCL; Databases, Chemical; Databases, Genetic; Gene Expression Profiling; Glioma - drug therapy; Glioma - genetics; Glioma - immunology; Glioma - metabolism; gliomas; Humans; immune checkpoint genes; Immunology; immunosuppressive; Nomograms; Predictive Value of Tests; Prognosis; Risk Assessment; Risk Factors; Thiazoles - therapeutic use; Transcriptome; Tumor Microenvironment - immunology; CXCL; immunosuppressive; immune checkpoint genes; gliomas; chemotherapy
• ISSN: 1664-3224; 1664-3224
• DOI: 10.3389/fimmu.2021.731751

Gliomas are a type of malignant central nervous system tumor with poor prognosis. Molecular biomarkers of gliomas can predict glioma patient’s clinical outcome, but their limitations are also emerging. C-X-C motif chemokine ligand family plays a critical role in shaping tumor immune landscape and modulating tumor progression, but its role in gliomas is elusive. In this work, samples of TCGA were treated as the training cohort, and as for validation cohort, two CGGA datasets, four datasets from GEO database, and our own clinical samples were enrolled. Consensus clustering analysis was first introduced to classify samples based on CXCL expression profile, and the support vector machine was applied to construct the cluster model in validation cohort based on training cohort. Next, the elastic net analysis was applied to calculate the risk score of each sample based on CXCL expression. High-risk samples associated with more malignant clinical features, worse survival outcome, and more complicated immune landscape than low-risk samples. Besides, higher immune checkpoint gene expression was also noticed in high-risk samples, suggesting CXCL may participate in tumor evasion from immune surveillance. Notably, high-risk samples also manifested higher chemotherapy resistance than low-risk samples. Therefore, we predicted potential compounds that target high-risk samples. Two novel drugs, LCL-161 and ADZ5582, were firstly identified as gliomas’ potential compounds, and five compounds from PubChem database were filtered out. Taken together, we constructed a prognostic model based on CXCL expression, and predicted that CXCL may affect tumor progression by modulating tumor immune landscape and tumor immune escape. Novel potential compounds were also proposed, which may improve malignant glioma prognosis.