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. 2022 Mar 11:2022:3370727.
doi: 10.1155/2022/3370727. eCollection 2022.

VSports - Uncovering the Immune Cell Infiltration Landscape in Low-Grade Glioma for Aiding Immunotherapy

Youyuan Yang  1 , Yue Tian  2 , Qing Li  3 , Rui Jiang  4 , Junfeng Zhang  4
Affiliations

Uncovering the Immune Cell Infiltration Landscape in Low-Grade Glioma for Aiding Immunotherapy

Youyuan Yang et al. J Oncol. .

Abstract

Objective: Low-grade glioma (LGG) mainly threatens the elderly population, with undesirable prognoses. This study uncovered the immune cell infiltration (ICI) landscape in LGG. VSports手机版.

Methods: RNA-seq profiles of LGG were retrieved from TCGA and CGGA databases. CIBERSORTx and ESTIMATE algorithms were employed to characterize the ICI landscape in LGG tissues V体育安卓版. Through unsupervised clustering analysis, ICI subtypes were clustered. ICI scores were computed via principal component analysis (PCA). The differences in survival, tumor-infiltrating immune cells, stromal scores, immune scores, immune checkpoint genes, immune activity genes, and tumor mutation burden (TMB) were assessed between high and low ICI score groups. .

Results: Three ICI subtypes were constructed in LGG, with distinct survival outcomes, PD-L1 expression, and infiltration levels of immune cells. Furthermore, ICI scores were developed V体育ios版. Both in TCGA and CGGA datasets, low ICI scores were indicative of undesirable outcomes. High ICI scores were significantly correlated to increased infiltration levels of memory B cells, CD8 T cells, CD4 naïve T cells, T follicular helper cells, macrophages M0, and eosinophils, while low ICI scores were characterized by increased infiltration levels of naïve B cells, plasma cells, CD4 memory resting T cells, Tregs, resting NK cells, macrophages M2, and activated dendritic cells. High ICI scores exhibited correlations with lower immune activity genes and immune checkpoint genes. Furthermore, TMB was distinctly reduced in the high ICI score group. .

Conclusion: The ICI scores may serve as a promising prognostic index and predictive indicator for immunotherapies, extending our understanding of immune microenvironment in LGG. VSports最新版本.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Characterization of ICI subtypes with distinct survival outcomes for LGG in TCGA-LGG dataset. ((a)–(c)) Unsupervised clustering analysis for classifying three ICI subtypes by the “ConsensusClusterPlus” package. (a) Consensus cumulative distribution function graph. (b) Delta area plot. (c) Heatmap for consensus matrix when k = 3. (d) PCA plots for the classification patterns of the ICI subtypes. (e) Heatmap of tumor-infiltrating immune cells in different clinical phenotypes and ICI subtypes. (f) Kaplan-Meier curves for OS of LGG patients in the three ICI subtypes.
Figure 2
Figure 2
The landscape of tumor microenvironment components in the three ICI subtypes of LGG. (a) Correlations between tumor-infiltrating immune cells, immune scores, and stromal scores in LGG tissues. The more towards red, the greater the positive correlation coefficient; the more towards blue, the greater the negative correlation coefficient. (b) Box plots for the infiltration levels of tumor-infiltrating immune cells in each ICI subtype. (c) Violin plots for the expression of PD-L1 in each ICI subtype. Kruskal-Wallis test, ns: not significant; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.
Figure 3
Figure 3
Construction of ICI gene clusters for LGG. ((a)–(c)) Unsupervised clustering analysis for identifying ICI gene clusters based on DEGs among ICI subtypes. (d) Heatmap for clinical features and expression patterns of ICI gene signatures in each ICI gene cluster. (e) Violin plots for the ICI scores in each ICI gene cluster. Kruskal-Wallis test, p < 2.2e − 16.
Figure 4
Figure 4
Functional enrichment analysis of ICI gene signatures A and B; GO and KEGG pathway enrichment results of ((a) and (b)) ICI gene signatures A and ((c) and (d)) ICI gene signatures B.
Figure 5
Figure 5
Development of ICI score system for LGG. (a) Alluvial diagram for the distributions of ICI scores and survival status in the four ICI gene clusters. ((b) and (c)) Kaplan-Meier curves of OS for patients with high and low ICI scores in the (b) TCGA-LGG and (c) CGGA-LGG datasets. Log-rank test, p < 0.001. ((d) and (e)) GSEA for the enrichment results in (d) high and (e) low ICI score groups.
Figure 6
Figure 6
Assessment of the roles of ICI score in predicting response to immunotherapy. (a) The correlations between ICI scores and tumor-infiltrating immune cells. (b) The correlations of ICI scores with immune checkpoint genes and immune activity genes. (c) The TMB levels in high and low ICI score groups. (d) Scatter plots for the Spearman correlation between TMB and ICI scores. Wilcoxon test, ns: not significant; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.
Figure 7
Figure 7
Prediction of potential small molecular drugs based on ICI scores by the CMap database. (a) Heatmap for upregulated genes (red) and downregulated genes (blue) between high and low ICI score groups. (b) Mechanisms of action shared by small molecular compounds.
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