Microarray profiling of bone marrow long non-coding RNA expression in Chinese pediatric acute myeloid leukemia patients
- Authors:
- Published online on: November 12, 2015 https://doi.org/10.3892/or.2015.4415
- Pages: 757-770
Abstract
Introduction
Acute myeloid leukemia (AML) is the most common form of acute leukemia that occurs more frequently in adults, but relatively rare in children; and AML incidence increases with age (1). AML is characterized by the rapid growth of abnormal white blood cells that accumulates in the bone marrow, which inhibits production and differentiation of normal blood cells (1,2). Genetically, AML is defined as a clonal disorder caused by malignant transformation of bone marrow-derived cells, self-renewing stem cells, or progenitors that demonstrate a decreased rate of self-destruction and aberrant differentiation (3). Pediatric AML comprises of up to 20% of all childhood leukemia (4). Although overall AML prognosis has been improved in the last decade, further development and identification of prognostic markers or novel targets for AML treatment could improve the survival rate of AML patients, especially pediatric AML patients.
Long non-coding RNA (lncRNA) regulates gene transcription and protein expressions genetically and epigenetically, and altered expressions result in cancer development. Human genomic data has shown that ~75% of the human genome is transcribed into RNA, and only a few above 1% codes protein expressions; indicating that a large portion of the genome is dedicated as regulators (5,6). Many lncRNAs ranging from 0.2 to 100 kilobases (kb) in length are transcribed from the genome. Among these newly discovered RNA elements, lncRNAs have been identified to have functional roles in a diverse range of cellular functions such as development, differentiation, cell fate, as well as disease pathogenesis (7–11). Expression analyses of cancer versus normal cells have revealed aberrant non-coding RNA (ncRNA) expression in various human cancers. For example, an altered PCGEM1 expression was associated with increased proliferation and colony formation in prostate cancer cells (12). MALAT1, also known as NEAT2, was originally identified as an abundantly expressed ncRNA during metastasis of early-stage non-small cell lung cancer; and its overexpression is a prognostic marker for poor patient survival rate (13,14). MALAT1 was also found to be highly expressed in hepatocellular carcinoma (15–17). The oncofetal H19 gene was the first imprinted ncRNA to be identified, and loss of imprinting (LOI) at chromosome 11p15.5H19/IGF2 locus leads to an imbalanced expression of H19 and IGF2. H19 dysregulation has been implicated in a variety of human cancers such as colorectal (18), HCC (19), breast (20), and bladder cancers (21,22).
Various lncRNAs were reported to be implicated in malignant hematopoiesis associated with blood cell neoplasms such as leukemia (23,24). H19 ncRNA was highly expressed in Bcr-Abl-transformed cell lines and primary cells derived from patients in a Bcr-Abl kinase-dependent manner (23). lncRNA MONC and MIR100HG were highly expressed in acute mega-karyoblastic leukemia blasts (24). Thus, lncRNAs may be useful as diagnostic and prognostic markers in leukemia. In the present study, we profiled differential expression of lncRNAs in pediatric AML to better understand AML pathogenesis and identify biomarkers and novel therapeutic targets.
Materials and methods
Patients and samples
Bone marrow specimens were obtained from 22 pediatric AML patients at the Department of Hematology and Oncology, Children's Hospital of Soochow University, between 2011 and 2014. This study was approved by the Ethics Committee of the Children's Hospital of Soochow University (#SUEC2011-021 and #SUEC2014-037), and written informed consent was obtained from all parents or guardians. AML diagnosis was made in accordance with the revised French-American-British (FAB) classification (25). The main clinical and laboratory features of this cohort of patients are summarized in Table I. Additionally, bone marrow samples from 20 donors without leukemia were used as controls. Bone marrow mononuclear cells (BMNCs) were isolated using Ficoll solution within 2 h after bone marrow samples were harvested, and subjected for isolation of total cellular RNA.
Profiling of lncRNA expression using Arraystar Human LncRNA Array V3.0
Arraystar Human LncRNA Array V3.0 was used to profile expression of lncRNAs, which was performed by KangChen Bio-tech (Shanghai, China) according to a previous study (26). Briefly, RNA samples from BMNCs were further purified to remove rRNA, and transcribed into fluorescent cRNA as probes to hybridize to the Human LncRNA Array V3.0 (8660 K; Arraystar). The array contains 30,586 lncRNAs and 26,109 coding transcripts, which were collected from the most authoritative databases (such as RefSeq, UCSC, Knowngenes, and Ensembl) and related literature. Array data were then analyzed by MultiExperiment Viewer software for upregulation or downregulation of lncRNA expression in AML samples compared to control samples with a cut-off point of 2-fold for upregulation and a cut-off point of 0.5-fold for downregulation.
Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses
Gene Ontology (GO) functionally analyzes differentially expressed genes with GO categories (http://david.abcc.ncifcrf.gov/summary.jsp). Pathway analysis of differentially expressed genes was performed based on the latest Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg). Differentially expressed genes and gene product enrichment with particular attention to GO biological processes and molecular functions were grouped into gene pathways using a p-value ≤0.05, as shown below.
Construction of the lncRNA-mRNA co-expression network
The construction of the lncRNA-mRNA co-expression network included three steps: i), lncRNA screening: lncRNAs that were upregulated or downregulated with a fold-change >3.0 and a p-value <0.05 were first selected to enhance data reliability. Sequences of lncRNAs that have not been recorded in ENCODE were removed. ii), lncRNA-miRNA interactions were predicted by miRcode (http://www.mircode.org/). iii), mRNAs targeted by miRNAs with experimental support were obtained from TarBase (http://www.microrna.gr/tarbase).
Network construction procedures included the following: i), preprocessing of data: if one coding gene has different transcripts, the median value was taken to represent the value of this gene expression without special treatment of lncRNA expression values; ii), data were screened and subset of data were removed according to the lists of differential lncRNA and mRNA expressions obtained from GO and KEGG pathway analyses; iii), Pearson's correlation coefficient was calculated, and the R value was used to calculate the correlation coefficient between lncRNA and coding genes; and iv), Pearson's correlation coefficient was used for screening, wherein, RNAs with a Pearson's correlation coefficient ≥0.98 were considered significant. The lncRNA-mRNA co-expression network was then constructed by Cytoscape software (The Cytoscape Consortium, San Diego, CA, USA).
RNA isolation and qRT-PCR
Total cellular RNA was isolated from bone marrow samples using a TRIzol reagent (Invitrogen, Carlsbad, CA, USA) and stored at −80°C until use. RNA concentration was determined using a spectrophotometer (NanoDrop 2000) and purity was assessed by agarose gel electrophoresis. RNA samples were then reversely transcribed into cDNA using 4 µg of RNA samples in a 10-µl volume and SuperScript II reverse transcriptase (Invitrogen) according to manufacturer's protocols. For qPCR, we first designed PCR primers according to the database of Real-time primers (Center for Medical Genetics, http://medgen.ugent.be/CMGG/) or using the online program, Primer 3 (www.fokker.wi.mit.edu/primer3/input.htm). Primer selection parameters were set to primer size: 20–26 nts; primer melting temperature: 60–64°C; GC clamp: 1; and product size range: generally 120–240 bp, which went down to 100 bp if no appropriate primers could be identified. Primers were synthesized by Invitrogen. The qPCR amplification was set in a 20-µl reaction volume containing 1 µl of cDNA, 0.2 mM of each primer, and 10 µl of SyBR Green Mix (Roche, Indianapolis, IN, USA); and was performed in a LightCycler 480 (Roche) using universal thermal cycling parameters (an initial 95°C for 10 min and 45 cycles of 15 sec at 95°C, 15 sec at 60°C, and 60 sec at 72°C. After that, the melting curve for 10 sec at 95°C and 60 sec at 65°C). For gene expression levels, we used the comparative Ct method. First, gene expression levels for each sample were normalized to the expression level of the housekeeping gene encoding glyceraldehyde 3-phosphate dehydrogenase (GAPDH) within a given sample (−ΔCt); and the relative expression of each gene was calculated with 106 × log2 (−ΔCt). The difference between pediatric AML samples compared to control samples was used to determine 106 × log2 (−ΔCt).
Statistical analysis
SPSS v11.5 (SPSS Inc., Chicago, IL, USA) was used for all statistical analyses. Differentially expressed lncRNAs in AML samples were statistically compared with normal controls, and a cut-off point of 2-fold for upregulation and 0.5-fold for downregulation of lncRNA expressions were used in AML samples. For gene expression, we performed a Student's t-test; and a p-value ≤0.05 was considered statistically significant.
Results
Differentially expressed lncRNAs and mRNAs in pediatric AML
The Arraystar Human LncRNA 8×60k V3.01 micro-array was used to profile differentially expressed lncRNAs and mRNAs in pediatric AML versus normal controls. A total of 2,335 differently expressed mRNAs were found in pediatric AML (Fig. 1A and B). Among them, 51 mRNAs were significantly upregulated and 85 mRNAs were significantly downregulated for >10-fold in pediatric AML compared to normal controls. Clustering analysis was used to visualize the relationships between the mRNA expression patterns present in the samples (fold-changes ≥10; Fig. 1C and D).
Moreover, a total of 2,413 differentially expressed lncRNA were identified in pediatric AML (Fig. 2A and B). Hierarchical clustering analysis of these differentially expressed lncRNAs with >10-fold difference is shown in Fig. 2C and D and Tables II and III.
 | Table IIlncRNA upregulated >10-fold in the pediatric acute myeloid leukemia compared with normal control. |
| Table IIIlncRNA downregulated >10-fold in the pediatric acute myeloid leukemia compared with normal control. |
Altered lncRNA-related gene pathways in pediatric AML
Ontological pathway enrichment analysis was performed on differentially expressed lncRNA and mRNA, and a biological process enrichment analysis was performed using the DAVID tool to gain insights into their functions (GEO accession no. GSE64975). The most enriched GO targeted by upregulated and downregulated transcripts were involved in cell cycle regulation, immune response, and immune system process (Fig. 3A and B). The most significant molecular function (MF) enrichment scores are shown in Fig. 3C and D. KEGG annotations of the most enriched pathways are shown in Fig. 3E, and KEGG pathway annotations of the most enriched pathways were cell cycle regulation with a p-value <7.22764E−10. Cell cycle pathway proteins included ANAPC11, ANAPC13, ANAPC7, BUB1, BUB1B, CCNA2, CCNB1 and CCNB2.
lncRNA/mRNA co-expression network in pediatric AML
Next, we constructed a coding and non-coding gene expression network based on the correlation analysis between differentially expressed lncRNAs and mRNAs. Pearson's correlation coefficient analysis was performed using a coefficient no less than 0.98 to construct the network (Fig. 4). The expression network indicated that one mRNA or lncRNA may correlate with one to tens of lncRNAs. The co-expression network may suggest the inter-regulation of lncRNAs and mRNAs during AML development.
Confirmation of dysregulated lncRNAs in pediatric AML versus normal control samples
To confirm the microarray data, we selected 97 dysregulated lncRNAs from the micro-array analysis of 22 pediatric AML samples and 20 control samples using qRT-PCR. Our data revealed that the lncRNA expression profile in pediatric AML was significantly different from normal controls (Fig. 5A). A total of 20 genes were successfully clustered using lncRNA samples (Fig. 5B). A total of 24 lncRNAs were confirmed to be dysregulated in pediatric AML (Table IV), and detailed expressions of each upregulated and downregulated lncRNA in pediatric AML are shown in Figs. 6 and 7. The most upregulated lncRNA in pediatric AML is ENST00000435695 and the most downregulated lncRNA in pediatric AML is ENST00000415964.
| Table IVlncRNAs most dysregulated in the pediatric acute myeloid leukemia compared with normal control. |
Discussion
Our present study profiled differentially expressed lncRNAs and mRNAs in pediatric AMLs. We demonstrated for the first time the expression profiles of human lncRNAs in pediatric AML by microarray; and identified a collection of aberrantly expressed lncRNAs in pediatric AML compared to normal controls. It is likely that these dysregulated lncRNAs play a key or partial role in the development and/or progression of pediatric AML. Previous genome-wide profiling studies revealed that many transcribed non-coding ultra conserved regions exhibit distinct profiles in various human cancers. For example, a genome-wide RNA sequencing (RNAseq) analysis evaluated the differential expressions of lncRNAs in a cohort of 102 prostate cancer versus benign samples (27); and identified 121 unannotated transcripts that could accurately discriminate benign, localized and metastatic samples. Other lncRNA expression profile studies were conducted in >100 paired esophageal squamous cell carcinoma and normal samples (28), 5 pairs of liver cancer and normal tissues (29), and 6 pairs of renal clear cell carcinoma and corresponding normal tissues (26); which revealed large numbers of lncRNAs that were significantly dysregulated in cancer tissues. Recent studies have started to reveal the importance of lncRNAs in leukemia tumorigenesis. For example, lncRNA H19 was highly expressed in Bcr-Abl-transformed cell lines and primary cells derived from patients in a Bcr-Abl kinase-dependent manner (23). lncRNA MONC and MIR100HG were highly expressed in acute megakaryoblastic leukemia blasts (24). However, it remains to be determined how these lncRNAs participate and contribute to leukemia development or progression. In this study, we found that some of these differentially expressed lncRNAs regulated cell cycle progression and immunosystem functions. Future study is required to investigate whether manipulating lncRNA expressions could control AML progression and be used as a therapeutic target to control AML.
Our present study profiled and identified a group of dysregulated lncRNAs and mRNAs in bone marrow samples obtained from pediatric AML patients versus normal controls. We also verified 97 lncRNAs in >20 AML and normal control samples; and found 24 dysregulated lncRNAs in pediatric AML. These lncRNAs could regulate cell cycle progression and immunoresponses that could be associated with AML development or progression. Further study is required to determine whether these lncRNAs may serve as new therapeutic targets and diagnostic markers for pediatric AML.
Acknowledgments
The present study was supported in part by grants from the National '12th Five-Year' Major Science and Technology fund (#2011ZX09302-007-01), the National Natural Science Foundation of China (nos. 81100371, 81370627, 81300423 and 81272143), the Key Medical Subjects of Jiangsu Province (#XK201120), the Key Laboratory of Suzhou (#SZS201108 and #SZS201307), the Suzhou Science and Technology Development Planning 2013 Projects (#SyS201352), the Suzhou City youth Science and Technology Projects (#KJXW2012021), and the Technological Special Project for 'Significant New Drugs Creation' (#2012ZX09103301-040). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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