Prognostic value of preoperative soluble interleukin 2 receptor α as a novel immune biomarker in epithelial ovarian cancer

Abstract

Purpose: Epithelial ovarian cancer (EOC) is regarded as the deadliest gynecological cancer, and the demand for novel noninvasive prognostic biomarkers remains significant. This study aimed to investigate the prognostic value of preoperative blood biomarkers in EOC patients.

Methods: In total, 73 patients who had undergone ovarian mass resection were enrolled. Serum concentration of biomarkers, including soluble interleukin 2 receptor α (sIL-2R), was measured 1-2 weeks before surgery. Independent prognostic factors for progression-free survival (PFS) were investigated with multivariate Cox regression analysis. A prognostic model was subsequently developed and evaluated by discrimination, calibration and clinical net benefit. Furthermore, transcriptome data of 376 EOC cases from The Cancer Genome Atlas (TCGA) were analyzed with ESTIMATE, CIBERSORT and Maftools algorithm to evaluate the correlation of IL2RA expression with tumor immune microenvironment and immunotherapeutic response.

Results: High sIL-2R concentration was found to be the only significant prognostic blood biomarker for PFS by multivariate Cox regression analysis in our center. A prognostic nomogram was developed with satisfactory discrimination, calibration and clinical net benefit. In addition, higher IL2RA expression was significantly associated with higher immune scores, activated CD4⁺ T cells, M2 macrophages and resting dendritic cells in TCGA data. Furthermore, IL2RA expression was closely related to TMB scores.

Conclusions: sIL-2R is a potential prognostic immune biomarker for EOC patients, and a comprehensive prognostic model comprising sIL-2R with satisfactory discrimination and clinical appliance was developed. Therefore, we recommend routine sIL-2R testing in EOC patients.

Keywords: Epithelial ovarian cancer; Immune biomarker; Prognostic; sIL-2R.

Fig. 1

Analysis workflow of this study. TCGA, The Cancer Genome Atlas; TIL, tumor-infiltrating lymphocyte; GSEA, gene set enrichment analysis; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DCA, decision curve analysis

Fig. 2

Differentiated concentration of sIL-2R in samples and its correlation with tumor progression and prognosis. The concentration of sIL-2R was significantly higher in patients with EOC compared with patients with a benign ovarian mass (A) and in patients with advanced-stage EOC (B). Kaplan–Meier survival curves for progression-free survival in epithelial ovarian cancer patients with respect to sIL-2R (C), residual tumor size (D) and FIGO stage (E). The P value was calculated from log-rank test. sIL-2R, soluble interleukin 2 receptor α chain; FIGO, International Federation of Gynecology and Obstetrics; EOC, epithelial ovarian cancer

Fig. 3

Prognostic nomogram (A), calibration curve (B and C) and DCA curve (D and E) of PFS for EOC. A A vertical line between each variable and point scale can be drawn to determine the points for each variable. The predicted survival rate was calculated according to the total points by drawing a vertical line from the total points scale to the 1- and 2-year survival scales. B and C Nomogram-predicted survival is plotted on the x-axis; actual survival is plotted on the y-axis. A plot along the 45-degree line indicates a perfect model in which the predicted probabilities are identical to the actual outcomes. D and E The y-axis measures the net benefit, which is calculated by summing the benefit (true positives) and subtracting the harms (false positives). The dashed line indicates the prognostic model, and the two other lines indicate the “intervention for all” (light gray line) and “intervention for none” (black line). A model is considered of clinical value if it has a higher net benefit than other models and simple strategies at any given threshold. EOC, epithelial ovarian cancer; sIL-2R, soluble interleukin 2 receptor α chain; FIGO, International Federation of Gynecology and Obstetrics; DCA, decision curve analysis

Fig. 4

Estimation of the tumor immune microenvironment and cancer immunotherapy response using IL2RA expression levels in the TCGA dataset. A IL2RA expression correlated with immune scores and stromal scores. The immune and stromal scores were stratified by the median cutoff value. B Top 10 GSEA pathways associated with high IL2RA expression based on GO (BP, CC and MF) and KEGG pathway. The X-axis shows the normalized enrichment score, and all the pathways shown represent those with significant P values (P < 0.0001) and FDRs (FDR < 0.0001). C GSEA enrichment plot for activation of immune response, adaptive immune response, lymphocyte activation involved in immune response and regulation of innate immune response. D Violin plot showing that IL2RA was significantly highly expressed in CD4⁺ T cells, M2 macrophages, resting dendritic cells and resting mast cells and weakly expressed in M0 macrophages and activated mast cells (P < 0.05). The Wilcoxon rank-sum test was used as the significance test. E A scatter plot showing that IL2RA expression had a significant positive correlation with active CD4⁺ T cells, M2 macrophages and resting dendritic cells (P < 0.05), and the Pearson correlation test was used as the correlation test. F Waterfall plot displays mutation information of the genes with high mutation frequencies in the IL2RA high- and IL2RA low-expression groups. G TMB difference in the IL2RA high- and IL2RA low-expression patients. BP, biological process; CC, cellular component; MF, molecular function; NES, normalized enrichment score; FDR, false discovery rate. TMB, tumor mutation burden