Identification of cystic fibrosis transmembrane conductance regulator as a prognostic marker for juvenile myelomonocytic leukemia via the whole-genome bisulfite sequencing of monozygotic twins and data mining

Abstract

Background: Linked deoxyribonucleic acid (DNA) hypermethylation investigations of promoter methylation levels of candidate genes may help to increase the progressiveness and mortality rates of juvenile myelomonocytic leukemia (JMML), which is a unique myelodysplastic/myeloproliferative neoplasm caused by excessive monocyte and granulocyte proliferation in infancy/early childhood. However, the roles of hypermethylation in this malignant disease are uncertain.

Methods: Bone marrow samples from a JMML patient and peripheral blood samples from a healthy monozygotic twin and an unrelated healthy donor were collected with the informed consent of the participant's parents. Whole-genome bisulfite sequencing (WGBS) was then performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to analyze specific differentially methylated region (DMG) related genes. The target genes were screened with Cytoscape and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), which are gene/protein interaction databases. A data mining platform was used to examine the expression level data of the healthy control and JMML patient tissues in Gene Expression Omnibus data sets, and a survival analysis was performed for all the JMML patients.

Results: The STRING analysis revealed that the red node [i.e., the cystic fibrosis transmembrane conductance regulator (CFTR)] was the gene of interest. The gene-expression microarray data set analysis suggested that the CFTR expression levels did not differ significantly between the JMML patients and healthy controls (P=0.81). A statistically significant difference was observed in the CFTR promoter methylation level but not in the CFTR gene body methylation level. The overall survival analysis demonstrated that a high level of CFTR expression was associated with a worse survival rate in patients with JMML (P=0.039).

Conclusions: CFTR promoter hypermethylation may be a novel biomarker for the diagnosis, monitoring of disease progression, and prognosis of JMML.

Keywords: Bioinformatics analysis; cystic fibrosis transmembrane conductance regulator (CFTR); monozygotic twins.

Figure 1

Schematic representations of the cytosine methylation levels in each subject, as determined by WGBS. The x-axis shows the methylation level. (A) Cytosine methylation levels in the healthy control. (B) Cytosine methylation levels in the healthy control MZ twin. (C) Cytosine methylation levels in the patient. WGBS, whole-genome bisulfite sequencing; MZ, monozygotic; C, cytosine; G, guanine; CHG, where H is non-G nucleotides.

Figure 2

Methylation trends in gene regions. (A) Methylation trends in the healthy control. (B) Methylation trends in the healthy control MZ twin. (C) Methylation trends in the patient. MZ, monozygotic; C, cytosine; G, guanine; CHG, where H is non-G nucleotides.

Figure 3

GO enrichment analysis of DMR-associated genes. (A,B) Expression of DMR-related genes and promoters between the twins. (C,D) Expression of DMR-related genes and promoters between the JMML patient and the healthy controls. GO, Gene Ontology; DMR, differentially methylated regions; JMML, juvenile myelomonocytic leukemia.

Figure 4

Bubble diagram of KEGG enrichment analysis results. (A,B) Pathway enrichment analysis of DMR-associated genes and relevant promoters between the twins. (C,D) Pathway enrichment analysis of DMR-associated genes and relevant promoters between the JMML patient and the healthy controls. KEGG, Kyoto Encyclopedia of Genes and Genomes; DMR, differentially methylated regions; JMML, juvenile myelomonocytic leukemia.

Figure 5

Venn diagram showing the overlap among the 3 subjects. (A) Pathway enrichment analysis of the differentially expressed genes between the twins (light color). (B) Pathway enrichment analysis of the differentially expressed genes between JMML patients and healthy controls. Pathway enrichment analysis of the overlapping differentially expressed genes. JMML, juvenile myelomonocytic leukemia.

Figure 6

Network visualization based on pathway enrichment analysis with Cytoscape and STRING. STRING, Search Tool for the Retrieval of Interacting Genes/Proteins.

Figure 7

Validation of RNA-seq data by RT-qPCR. Relative expression level of CFTR determined by RT-qPCR. HC, healthy control; JMML, juvenile myelomonocytic leukemia; CFTR, cystic fibrosis transmembrane conductance regulator; RT-qPCR, real-time quantitative polymerase chain reaction; ns, no significance.

Figure 8

Prognostic value of CFTR for long-term survival in patients with JMML. (A) Differential expression of CFTR between normal (n=16) and JMML (n=82) samples. (B) Kaplan-Meier analysis of overall survival in JMML patients. (C,D) Differential expression of CFTR between the normal (n=16) and the high (n=35) and low (n=47) expression subgroups of samples. **, P<0.01. ***, P<0.001. JMML, juvenile myelomonocytic leukemia; CFTR, cystic fibrosis transmembrane conductance regulator. ns, no significance.