A novel prognostic model based on four circulating miRNA in diffuse large B-cell lymphoma: implications for the roles of MDSC and Th17 cells in lymphoma progression

Abstract

MicroRNA (miRNA) have been emerged as prognostic biomarkers in diffuse large B-cell lymphoma (DLBCL). To understand the potential underlying mechanisms and translate these findings into clinical prediction on lymphoma progression, large patient cohorts should be evaluated. Here, using miRNA PCR array, we analyzed the miRNA expression profiles in serum samples of 20 DLBCL patients at diagnosis, remission and relapse. Four candidate miRNA were identified and subsequently evaluated for their ability to predict relapse and survival. A prognostic model based on four circulating miRNA (miR21, miR130b, miR155 and miR28) was established and tested in a training cohort of 279 patients and in a validation cohort of 225 patients (NCT01852435). The prognostic value of the 4-circulating miRNA model was assessed by univariate and multivariate analyses. The novel 4-circulating miRNA prognostic model significantly predicted clinical outcome of DLBCL, independent of International Prognostic Index in the training cohort [hazard ratio (HR) = 2.83, 95% CI 2.14-3.51, P < 0.001] and in the validation cohort (HR = 2.71, 95% CI 1.91-3.50, P < 0.001). Moreover, DNA- and RNA-sequencing was performed on tumor samples to detect genetic mutations and signaling pathway dysregulation. DNA-sequencing data showed no significant difference of tumor mutation burden between the low-risk and the high-risk groups of the 4-circulating miRNA model. RNA-sequencing revealed a correlation between the 4-circulating miRNA model and aberrant Ras protein signaling transduction. The impact of the miRNA signature on oncogenic signaling and tumor microenvironment was analyzed in vitro and in vivo. In B-lymphoma cells, modulation of the miRNA regulated IGF1 and JUN expression, thereby altering MDSC and Th17 cells. In DLBCL patients, the high-risk group presented Ras signaling activation, increased MDSC and Th17 cells, and immunosuppressive status compared with the low-risk group. In conclusion, the easy-to-use 4-circulating miRNA prognostic model effectively predicted relapse and survival in DLBCL. Moreover, the tumor microenvironment contributes to the role of the 4-circulating miRNA model in DLBCL progression.

Keywords: Ras protein signal transduction; diffuse large B-cell lymphoma; microRNA; prognosis; tumor microenvironment.

Conflict of interest statement

The authors declare no conflicts of interest.

© 2020 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.

Figures

Fig. 1

Establishment of a 4‐circulating miRNA prognostic model in DLBCL. (A) Serum miRNA expression fold change calculated between diagnosis vs remission, and relapse vs remission in DLBCL (n = 20) by real‐time PCR. The relative expression level of each sample was calculated based on the lowest expression value. (B) Differential expression of each miRNA at diagnosis vs remission, and relapse vs remission. (C) The fold change of miRNAs significantly altered at diagnosis or at relapse. (D) Validation of the above miRNAs in relapse patients (n = 20) and non‐relapse DLBCL (n = 80).

Fig. 2

Association of the 4‐circulating miRNA prognostic model with disease relapse. (A,B) The true‐positive rate, true non‐positive rate, false‐positive rate and false non‐positive rate of each miRNA and 4‐circulating miRNA model in the training cohort (A) and validation cohort (B). (C,D) Forest plot showing the distribution of clinical features in terms of relapse in the training cohort (C) and validation cohort (D).

Fig. 3

Association of the 4‐circulating miRNA prognostic model with survival time. (A) ROC curve of the training cohort and the validation cohort. (B) Survival curve of progression‐free survival (PFS) and overall survival (OS) in the training cohort according to the low‐risk and the high‐risk group of the 4‐circulating miRNA model. (C) Survival curve of PFS and OS in the validation cohort according to the low‐risk and the high‐risk of the 4‐circulating miRNA model.

Fig. 4

Association of the 4‐circulating miRNA prognostic model with oncogenic signaling pathways. (A) Signaling pathways regulated by four miRNA according tomirpathv.3 and signaling pathways enriched through gene ontology. (B) Lymphoma‐associated pathways identified in gene ontology. (C) Gene–gene interaction network of Ras protein signal transduction. Greater nodes indicated genes that were more likely to be functionally related. (D) Network analysis of Ras protein signal transduction associated with IGF1 and JUN. (E) Correlation coefficient between genes involved in Ras protein signal transduction and each miRNA, 4‐circulating miRNA model as determined by Pearson correlation coefficient analysis. (F) Increased IGF1 and JUN positivity were more frequently observed in tumor samples of patients in the high‐risk group (n = 26) than in the low‐risk group (n = 26). Statistical significance was assessed by chi‐square test. (G) Bioinformatics analysis predicted potential binding sites of miR130b on the promotor region of IGF1. (H) The effect of miR130b on transcriptional activity of the IGF1 promoter revealed by luciferase reporter assay in HEK‐293T cells transfected with control mimics or miR130b mimics. Mean ± SD from triplicates is shown by vertical bars (n = 3). Statistical significance was assessed byt‐test. (I) GSEA analysis of signaling pathways significantly altered in the high and low Ras groups.

Fig. 5

Association of the 4‐circulating miRNA prognostic model with tumor immunity. (A) Immune activity scores of immune cell subsets according to low‐risk and high‐risk group of the 4‐circulating miRNA model. (B) Signaling pathways related to both MDSC and Th17 cells according to gene ontology. (C) Correlation of miRNA with IGF1 expression and MDSC percentage in co‐culture of OCI‐LY10 cells with PBMC. Mean ± SD from triplicates is shown by vertical bars (n = 3). Statistical significance was assessed byt‐test. (D) Correlation of miRNA with JUN expression and Th17 cells percentage in co‐culture of OCI‐LY10 cells with PBMC. Mean ± SD from triplicates is shown by vertical bars (n = 3). Statistical significance was assessed byt‐test. (E) Volcano plot of gene expression profile involved in MDSC and Th17 cells, including cytokines and growth factors, intracellular signaling factors, phenotyping expression in MDSC (upper panel) and Th17 cells (lower panel). (F) Correlation of miRNA with TGFB1, IL‐6 and IL‐17D expression.