Competing endogenous RNA networks related to prognosis in chronic lymphocytic leukemia: comprehensive analyses and construction of a novel risk score model

Abstract

Background: Chronic lymphocytic leukemia (CLL) is a heterogeneous B-cell malignancy that lacks specific biomarkers and drug targets. Competing endogenous RNAs (ceRNAs) play vital roles in oncogenesis and tumor progression by sponging microRNAs (miRNAs). Nevertheless, the regulatory mechanisms of survival-related ceRNA networks in CLL remain to be uncovered.

Methods: We included 865 de novo CLL patients to investigate RNA expression profiles and Illumina sequencing was performed on four CLL patients, two CLL cell lines and six healthy donors in our center. According to univariate Cox regression, LASSO regression as well as multivariate Cox regression analyses, we established a novel risk score model in CLL patients. Immune signatures were compared between the low- and high-risk groups with CIBERSORT and ESTIMATE program. Afterwards, we analyzed the relationship between differentially expressed miRNAs (DEmiRNAs) and IGHV mutational status, p53 mutation status and del17p. Based on the survival analyses and differentially expressed RNAs with targeting relationships, the lncRNA/circRNA-miRNA-mRNA ceRNA networks were constructed. In addition, the circRNA circ_0002078/miR-185-3p/TCF7L1 axis was verified and their interrelations were delineated by dual-luciferase reporter gene assay.

Results: Totally, 57 differentially expressed mRNAs (DEmRNAs) and 335 DEmiRNAs were identified between CLL patient specimens and normal B cells. A novel risk score model consisting of HTN3, IL3RA and NCK1 was established and validated. The concordance indexes of the model were 0.825, 0.719 and 0.773 in the training, test and total sets, respectively. The high-risk group was related to del(13q14) as well as shorter overall survival (OS). Moreover, we identified DEmiRNAs that related to cytogenetic abnormality of CLL patients, which revealed that miR-324-3p was associated with IGHV mutation, p53 mutation and del17p. The survival-related lncRNA/circRNA-miRNA-mRNA ceRNA networks were constructed to further facilitate the development of potential predictive biomarkers. Besides, the expression of circ_0002078 and TCF7L1 were significantly elevated and miR-185-3p was obviously decreased in CLL patients. Circ_0002078 regulated TCF7L1 expression by competing with TCF7L1 for miR-185-3p.

Conclusions: The comprehensive analyses of RNA expression profiles provide pioneering insights into the molecular mechanisms of CLL. The novel risk score model and survival-related ceRNA networks promote the development of prognostic biomarkers and potential therapeutic vulnerabilities for CLL.

Keywords: Chronic lymphocytic leukemia; Competing endogenous RNA; Non-coding RNAs; Prognostic biomarkers; Risk score model.

Fig. 1

The heatmap and functional enrichment analyses in chronic lymphocytic leukemia (CLL) patients. A The heatmap of differentially expressed miRNAs in CLL patients, CLL cell lines and control. B The heatmap of differentially expressed mRNAs in CLL patients, CLL cell lines and control. C GO analysis results showed that changes in molecular function (MF), cell component (CC) and biological processes (BP) of DEGs between CLL patients and control were mainly enriched in heterocyclic compound binding, organelle part and regulation of gene expression. D KEGG pathway analysis revealed that the DEGs between CLL patients and control were mainly enriched in Notch signaling pathway and JAK-STAT signaling pathway

Fig. 2

Construction and validation of the novel risk score model. A-B The relative regression coefficients of 8 genes identified by the LASSO regression analysis. C Multivariate Cox analysis of 3 DEGs. D Nomogram of 3 DEGs. E-I The distribution of the risk score, survival status, expression of 3 survival-related DEGs in high-risk and low-risk groups, Kaplan–Meier survival curve, ROC curve analyses of the risk score model in the training set. J-N The distribution of the risk score, survival status, expression of 3 survival-related DEGs in high-risk and low-risk groups, Kaplan–Meier survival curve, ROC curve analyses of the risk score model in the test set. O-S The distribution of the risk score, survival status, expression of 3 survival-related DEGs in high-risk and low-risk groups, Kaplan–Meier survival curve, ROC curve analyses of the risk score model in the total set

Fig. 3

Immune signatures of low- and high-risk groups of CLL patients. A The infiltration of 22 types of immune cells in the low-risk group. B The infiltration of 22 types of immune cells in the high-risk group. C Correlations between immune cells in low-risk group. D Correlations between immune cells in high-risk group. E Differential expression of immune cells in low- and high-risk groups. F The stromal score, immune score and ESTIMATE score in high-risk group were lower than those in low-risk group. G Differential expression of immune checkpoint in low- and high-risk groups. H The heatmap of immune function analysis in low- and high-risk groups. (^*p < 0.05, ^**p < 0.01, ^***p < 0.001)

Fig. 4

The relationship between differentially expressed miRNAs (DEmiRNAs) and clinical characteristics. A-M DEmiRNAs associated with mutational status of IGHV. N-Q DEmiRNAs associated with p53 mutational status. R-T DEmiRNAs associated with del17p. (^*p < 0.05, ^**p < 0.01, ^***p < 0.001)

Fig. 5

The lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network between CLL patients and healthy donors

Fig. 6

The circRNA-miRNA-mRNA ceRNA network between CLL patients and healthy donors

Fig. 7

The verification of circ_0002078/miR-185-3p/TCF7L1 axis and the function of circ_0002078/miR-185-3p. A The expression of TCF7L1 in patient specimens were significantly increased. B The expression of miR-185-3p in patient specimens were significantly decreased. C The expression of circ_0002078 in patient specimens were significantly increased. D There was a strong linear correlation between TCF7L1 and circ_0002078. E Higher expression of TCF7L1 was associated with shorter TTFT. F Lower expression of miR-185-3p was associated with shorter OS. G Higher expression of circ_0002078 was associated with shorter OS. H The luciferase activity analysis showed that miR-185-3p can bind to the circ_0002078. I The luciferase activity analysis shows that miR-185-3p can bind to 3′UTR of TCF7L1. J Overexpression of miR-185-3p inhibited cell proliferation. K Efficiency verification of circ_0002078 knockdown in EHEB cells by qRT-PCR. L Knockdown of circ_0002078 inhibited cell proliferation. M, N Knockdown of circ_0002078 promoted cell apoptosis. O-Q Knockdown of circ_0002078 led to G2/M cell cycle arrest. All the results are expressed as mean ± SEM. (^*p < 0.05, ^**p < 0.01, ^***p < 0.001)

Fig. 8

The mechanism of circRNA circ_0002078/miR-185-3p/TCF7L1 axis in CLL cells. The interaction mechanism between them is that circ_0002078 can absorb miR-185-3p like a sponge, thereby inhibiting the inhibitory effect of miR-185-3p on TCF7L1. This competitive combination leads to the increasing expression of TCF7L1, which contributes to the promotion of cell proliferation, metastasis and other life processes that can conducive to the development of cancer