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. 2021 Nov 22:12:763791.
doi: 10.3389/fimmu.2021.763791. eCollection 2021.

Identification and Validation of Immune-Related Gene for Predicting Prognosis and Therapeutic Response in Ovarian Cancer

Zhao-Cong Zhang  1 , Jun-Nan Guo  2 , Ning Zhang  1 , Zhi-Qiang Wang  1 , Ge Lou  1 , Bin-Bin Cui  2 , Chang Yang  1
Affiliations

Identification and Validation of Immune-Related Gene for Predicting Prognosis and Therapeutic Response in Ovarian Cancer

Zhao-Cong Zhang et al. Front Immunol. .

Abstract

Ovarian cancer (OC) is a devastating malignancy with a poor prognosis. The complex tumor immune microenvironment results in only a small number of patients benefiting from immunotherapy. To explore the different factors that lead to immune invasion and determine prognosis and response to immune checkpoint inhibitors (ICIs), we established a prognostic risk scoring model (PRSM) with differential expression of immune-related genes (IRGs) to identify key prognostic IRGs. Patients were divided into high-risk and low-risk groups according to their immune and stromal scores. We used a bioinformatics method to identify four key IRGs that had differences in expression between the two groups and affected prognosis. We evaluated the sensitivity of treatment from three aspects, namely chemotherapy, targeted inhibitors (TIs), and immunotherapy, to evaluate the value of prediction models and key prognostic IRGs in the clinical treatment of OC. Univariate and multivariate Cox regression analyses revealed that these four key IRGs were independent prognostic factors of overall survival in OC patients. In the high-risk group comprising four genes, macrophage M0 cells, macrophage M2 cells, and regulatory T cells, observed to be associated with poor overall survival in our study, were higher. The high-risk group had a high immunophenoscore, indicating a better response to ICIs. Taken together, we constructed a PRSM and identified four key prognostic IRGs for predicting survival and response to ICIs. Finally, the expression of these key genes in OC was evaluated using RT-qPCR. Thus, these genes provide a novel predictive biomarker for immunotherapy and immunomodulation.

Keywords: immune checkpoint inhibitors (ICI); immune-related genes (IRGs); ovarian cancer; prognosis; tumor immune microenvironment.

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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
(A) A survival curve based on immune score for patients in the HRG and LRG. (B) A survival curve based on stromal score for patients in the HRG and LRG. (C) Heatmap plots of DEGs in immune score of OC. (D) Heatmap plots of DEGs in stromal score of OC. (E) Venn diagram depicting the number of upregulated DEGs based on two scores. (F) Venn diagram depicting the number of downregulated DEG based on two scores.
Figure 2
Figure 2
(A) In order to achieve a scale-free co-expression network, we chose power index = 4 as the appropriate soft threshold. (B) Identification of a gene consensus module. The branches of the dendrogram correspond to four different gene modules. (C) Correlation between the gene modules and tumor microenvironment related scores, including immune score, stromal score, and ESTIMATE score. Each cell contains corresponding correlation coefficient and p-value. The correlation coefficient decreased in size from red to blue. (D) Scatter plot of module eigengenes in the turquoise module.
Figure 3
Figure 3
(A) Time-dependent receiver operating characteristic (tROC) analysis of the prognostic risk score model. (B) Kaplan-Meier curve of overall survival in model group. (C) Kaplan-Meier curve of overall survival in validation group. (D) Univariate-Cox regression analyze of prognostic factors in model group. (E) Multivariate-Cox regression analyze of prognostic factors in model group.
Figure 4
Figure 4
(A) Summary of the 22 immune cell types abundance estimated by “CIBERSORT” within different risk groups. (B) The differences of 22 immune cell types abundance within different risk groups. Macrophage M0 (p = 0.011), macrophage M2 (p = 0.048), and Treg cells (p < 0.001) were significantly highly expressed in the HSG. Activated memory CD4+ T cells (p= 0.049), T follicular helper (p = 0.045), and activated dendritic cells (aDC) (p < 0.001) were significantly higher in the LSG.
Figure 5
Figure 5
(A) GO-related GSEA between different risk groups. (B) Functional enrichment analysis of KEGG for key IRGs.
Figure 6
Figure 6
(A) Gene nodes ranked among the top 30 in the PPI analyses. PPI, protein-protein interaction. (B) Kaplan-Meier curves of overall survival in four prognostic key IRGs. IRGs, immune related genes. (C) The causal interaction of key gene analysis in DisNor.
Figure 7
Figure 7
RT-qPCR analysis of four key IRGs in the ovarian cancer tissues and normal ovarian tissues. All experiments were performed in triplicate. **p- value t-test < 0.01; ***p- value t-test < 0.001.
Figure 8
Figure 8
Sensitivity analysis of key IRGs expression within different chemotherapeutic drugs.
Figure 9
Figure 9
The expression differences of 7 immunosuppressive checkpoint genes in HRG and LRG.
Figure 10
Figure 10
The sensitivity difference of multiple targeted inhibitors within different risk groups, including AKT inhibitor VIII, GDC0941, JNK Inhibitor VIII, Lapatinib, and GDC-0449.
Figure 11
Figure 11
The association between IPS and risk groups of OC patients.
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