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A Whole Exon Screening-Based Score Model Predicts Prognosis and Immune Checkpoint Inhibitor Therapy Effects in Low-Grade Glioma

Authors
  • Luo, Cheng1, 2, 3
  • Wang, Songmao1, 3, 4
  • Shan, Wenjie1, 3, 5
  • Liao, Weijie1, 3
  • Zhang, Shikuan1, 3, 4
  • Wang, Yanzhi1, 3, 4
  • Xin, Qilei1, 3, 6
  • Yang, Tingpeng1, 3, 6
  • Hu, Shaoliang7
  • Xie, Weidong1, 3, 5
  • Xu, Naihan1, 3, 5
  • Zhang, Yaou1, 3, 5
  • 1 China State Key Laboratory of Chemical Oncogenomics, Tsinghua Shenzhen International Graduate School, Shenzhen , (China)
  • 2 Department of Biomedical Engineering, Tsinghua University, Beijing , (China)
  • 3 Key Lab in Healthy Science and Technology of Shenzhen, Tsinghua Shenzhen International Graduate School, Shenzhen , (China)
  • 4 School of Life Sciences, Tsinghua University, Beijing , (China)
  • 5 Open Faculty for Innovation, Education, Science, Technology and Art, Tsinghua Shenzhen International Graduate School, Shenzhen , (China)
  • 6 Department of Chemical Engineering, Tsinghua University, Beijing , (China)
  • 7 Research and Development Department, Shenzhen Combined Biotech Co., Ltd, Shenzhen , (China)
Type
Published Article
Journal
Frontiers in Immunology
Publisher
Frontiers Media SA
Publication Date
Jun 13, 2022
Volume
13
Identifiers
DOI: 10.3389/fimmu.2022.909189
Source
Frontiers
Keywords
Disciplines
  • Immunology
  • Original Research
License
Green

Abstract

Objective This study aims to identify prognostic factors for low-grade glioma (LGG) via different machine learning methods in the whole genome and to predict patient prognoses based on these factors. We verified the results through in vitro experiments to further screen new potential therapeutic targets. Method A total of 940 glioma patients from The Cancer Genome Atlas (TCGA) and The Chinese Glioma Genome Atlas (CGGA) were included in this study. Two different feature extraction algorithms – LASSO and Random Forest (RF) – were used to jointly screen genes significantly related to the prognosis of patients. The risk signature was constructed based on these screening genes, and the K-M curve and ROC curve evaluated it. Furthermore, we discussed the differences between the high- and low-risk groups distinguished by the signature in detail, including differential gene expression (DEG), single-nucleotide polymorphism (SNP), copy number variation (CNV), immune infiltration, and immune checkpoint. Finally, we identified the function of a novel molecule, METTL7B, which was highly correlated with PD-L1 expression on tumor cell, as verified by in vitro experiments. Results We constructed an accurate prediction model based on seven genes (AUC at 1, 3, 5 years= 0.91, 0.85, 0.74). Further analysis showed that extracellular matrix remodeling and cytokine and chemokine release were activated in the high-risk group. The proportion of multiple immune cell infiltration was upregulated, especially macrophages, accompanied by the high expression of most immune checkpoints. According to the in vitro experiment, we preliminarily speculate that METTL7B affects the stability of PD-L1 mRNA by participating in the modification of m6A. Conclusion The seven gene signatures we constructed can predict the prognosis of patients and identify the potential benefits of immune checkpoint inhibitors (ICI) therapy for LGG. More importantly, METTL7B, one of the risk genes, is a crucial molecule that regulates PD-L1 and could be used as a new potential therapeutic target.

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