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. 2021 Apr 19:11:654350.
doi: 10.3389/fonc.2021.654350. eCollection 2021.

Integrated Analysis Reveals Prognostic Value and Immune Correlates of CD86 Expression in Lower Grade Glioma

Huaide Qiu  1   2 , Wei Tian  3 , Yikang He  2   4 , Jiahui Li  2 , Chuan He  1 , Yongqiang Li  2 , Ning Liu  3 , Jianan Li  2
Affiliations

Integrated Analysis Reveals Prognostic Value and Immune Correlates of CD86 Expression in Lower Grade Glioma

Huaide Qiu et al. Front Oncol. .

Abstract

Background: CD86 has great potential to be a new target of immunotherapy by regulating cancer immune response. However, it remains unclear whether CD86 is a friend or foe in lower-grade glioma (LGG).

Methods: The prognostic value of CD86 expression in pan-cancer was analyzed using Cox regression and Kaplan-Meier analysis with data from the cancer genome atlas (TCGA). Cancer types where CD86 showed prognostic value in overall survival and disease-specific survival were identified for further analyses. The Chinese Glioma Genome Atlas (CGGA) dataset were utilized for external validation. Quantitative real-time PCR (qRT-PCR), Western blot (WB), and Immunohistochemistry (IHC) were conducted for further validation using surgical samples from Jiangsu Province hospital. The correlations between CD86 expression and tumor immunity were analyzed using the Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm, Tumor IMmune Estimation Resource (TIMER) database, and expressions of immune checkpoint molecules. Gene Set Enrichment Analysis (GSEA) was performed using clusterprofiler r package to reveal potential pathways.

Results: Pan-cancer survival analysis established CD86 expression as an unfavorable prognostic factor in tumor progression and survival for LGG. CD86 expression between Grade-II and Grade-III LGG was validated using qRT-PCR and WB. Additionally, CD86 expression in LGG with unmethylated O(6)-methylguanine-DNA-methyltransferase (MGMT) promoter was significantly higher than those with methylated MGMT (P<0.05), while in LGG with codeletion of 1p/19q it was significantly downregulated as opposed to those with non-codeletion (P<2.2*10-16). IHC staining validated that CD86 expression was correlated with MGMT status and X1p/19q subtypes, which was independent of tumor grade. Multivariate regression validated that CD86 expression acts as an unfavorable prognostic factor independent of clinicopathological factors in overall survival of LGG patients. Analysis of tumor immunity and GSEA revealed pivotal role of CD86 in immune response for LGG.

Conclusions: Integrated analysis shows that CD86 is an unfavorable prognostic biomarker in LGG patients. Targeting CD86 may become a novel approach for immunotherapy of LGG.

Keywords: CD86; immune microenvironment; lower-grade glioma; pan-cancer analysis; prognosis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Schematic flowchart of the study process. The top left panel indicates that CD86 expression level of the brain is 11.86 [expression= Log2(TPM + 1)].
Figure 2
Figure 2
Forest plots of cox regression analysis with CD86 expressions in different cancer types. (A) Overall survival (OS). (B) Disease-specific survival (DSS). Cancer types with statistically significant prognostic value of CD86 in both OS and DSS are highlighted in red.
Figure 3
Figure 3
Kaplan-Meier analysis with CD86 expressions in different cancer types. (A) The Venn diagram of the identified cancer types in cox regression analysis and Kaplan-Meier method. (B–E) Kaplan-Meier survival curve showing the prognostic value of CD86 on OS in LGG (B), SKCM (C), UVM (D), TGCT (E). (F–I) Kaplan-Meier survival curve showing the prognostic value of CD86 on DSS in LGG (F), SKCM (G), UVM (H), CESC (I).
Figure 4
Figure 4
Correlations between CD86 expression and tumor progression. CD86 expression in different stages of SKCM (A) and UVM (C). Multivariate regression analysis of CD86 expression, age, gender, and tumor stage for OS in SKCM (B) and UVM (D). (E) CD86 expression between different grades of LGG. (F) Multivariate regression analysis of CD86 expression, age, gender, and tumor grade for OS in LGG. (G) CD86 mRNA expression evaluated by qRT-PCR in different grades of LGG. (H) CD86 protein expression evaluated by WB in different grades of LGG.
Figure 5
Figure 5
Comparisons of CD86 expression with different histological/molecular subtypes of LGG stratified by tumor grade. (A) CD86 expression in astrocytoma, oligoastrocytoma and oligodendroglioma. (B) CD86 expression in IDH mutant and WT of LGG. (C) CD86 expression in MGMT-methylated LGG versus unmethylated type. (D) CD86 expression in LGG with X1p/19q codeletion versus non-codeletion. (E) IHC staining of CD86 among different molecular subtypes regarding status on MGMT methylation and X1p/19q codeletion. *p<0.05 **p<0.01, ***p<0.001, ****p<0.0001, ns: not significant.
Figure 6
Figure 6
Validation of the prognostic value of CD86 for LGG in CGGA. (A) Kaplan-Meier analysis of CD86 expression and OS in all LGG. (B) Kaplan-Meier analysis of CD86 expression and OS in primary LGG. (C) Kaplan-Meier analysis of CD86 expression and OS in recurrent LGG. (D) Univariate Cox regression of CD86 expression, LGG cancer type (primary or recurrent), grade, gender and age. (E) Multivariate Cox regression using the same variables.
Figure 7
Figure 7
Development and validation of a Nomogram. Univariate Cox regression (A) and Multivariate Cox regression (B) with CD86 expression, demographic and clinicopathological factors; Red dots represent risk factor (HRs>1), while green dots represent protective factor (HRs<1). *P<0.05, **P<0.01, ***P<0.001. (C) Nomogram with independent prognostic factors. ROC curve analysis at 1 year, 3years, and 5 years using TCGA dataset (D) and the CGGA dataset (E). Calibration plot at 1 year, 3years, and 5 years in TCGA (F) and the CGGA (G).
Figure 8
Figure 8
Exploration of CD86-related tumor immunity and GSEA. (A) Correlations between CD86 expression and Stromal Score. (B) Correlations between CD86 expression and Immune Score. Correlations between CD86 expression and different immune cells: B cell (C), CD8 T cell (D), CD4 T cell (E), macrophage (F), neutrophil (G), and dendritic cell (H). (I) Correlations between CD86 expression and different immune checkpoint molecules. (J) GSEA of GO terms. (K) GSEA in KEGG pathway. **p<0.01, ***p<0.001.
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