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Updated: Jul 5, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Predictive Panel for Immunotherapy in Low-Grade Glioma
Qingqing Lv1, Zhaoyu Zhang1, Haijuan Fu1
1Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China; The Key Laboratory of Carcinogenesis of the Chinese Ministry of Health, The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China.
Background:
The main treatment of low-grade glioma (LGG) is still surgical resection followed by radiotherapy and/or chemotherapy, which has certain limitations, including side effects and drug resistance. Immunotherapy is a promising treatment for LGG, but it is generally hindered by the tumor microenvironment with the limited expression of tumor antigens.
Methods:
We integrated RNA sequencing data sets and clinical information and conducted consistent cluster analysis to explore the most suitable patients for immune checkpoint therapy. Gene set enrichment analysis, UMAP analysis, mutation correlation analysis, TIMER analysis, and TIDE analysis were used to identify the immune characteristics of 3 immune subtypes and the feasibility of 5 antigens as immune checkpoint markers.
Results:
We analyzed the isolation and mutation of homologous recombination repair genes (HRR) of the 3 immune subtypes, and the HRR genes of the 3 subtypes were obviously segregated. Among them, the IS2 subtype has a large number of HRR gene mutations, which increases the immunogenicity of tumors-this is consistent with the results of tumor mutation load analysis of 3 immune subtypes. Then we evaluated the immune cell infiltration of immune subtypes and found that IS2 and IS3 subtypes were rich in immune cells. It is worth noting that there are many Treg cells and NK cells in the IS1 subtype. In addition, when analyzing the immune checkpoint gene expression of the 3 subtypes, we found that they were upregulated most in IS2 subtypes compared with other subtypes. Then when we further confirmed the role of immune-related genes in LGG; through TIDE analysis and TISIDB analysis, we obtained 5 markers that can predict the efficacy of ICB in patients with LGG. In addition, we confirmed that they were associated with poor prognosis through survival analysis.
Conclusions:
We obtained 3 reliable immune subtypes, and patients with the IS2 subtype are suitable for immunotherapy, in which NAMPT, SLC11A1, TNC, VIM, and SPP1 are predictive panel markers for ICB in the LGG group. Our findings provide a rationale for immunotherapy selection and prediction of patient prognosis in LGG patients.
