Genome‑wide profiling of lncRNA expression patterns in patients with acute promyelocytic leukemia with differentiation therapy
- Authors:
- Published online on: June 25, 2018 https://doi.org/10.3892/or.2018.6521
- Pages: 1601-1613
Abstract
Introduction
Acute promyelocytic leukemia (APL) is distinguished by a specific t(15;17) chromosomal translocation, contributing to the expression of the oncoprotein PML-RARα, which counteracts myeloid differentiation and facilitates APL-initiating cell self-renewal (1,2). The dominant-negative effect of the PML-RARα oncoprotein, which antagonizes the myeloid differentiation process, can be reversed by pharmacological doses of all-trans-retinoic acid (ATRA) (3). The combined therapy of ATRA and arsenic trioxide (ATO), which eliminates APL by activating PML-RARα degradation, demonstrates efficacy as an APL therapy and markedly improves the prognosis of patients with APL (4). However, ATO and ATRA induce irreversible resistance, and certain patients with APL ultimately succumb to treatment-resistant diseases (5,6). Therefore, the unifying mechanisms required for myeloid differentiation and response to therapy in APL require further investigation.
Long non-coding RNAs (lncRNAs), which are in excess of 200 nucleotides, are involved in diverse biological processes including epigenetic regulation, chromosome imprinting, transcription, splicing, translation, cell-cycle control, and differentiation (7). They have been functionally coupled to cancer and cellular differentiation. The investigation of lncRNA expression and function may contribute to the current understanding of leukemogenesis, and the identification of novel therapeutic targets and seminal posttranscriptional factors associated with resistance to chemotherapy. Consequently, the global aberrant expression of lncRNAs that occurs during myeloid differentiation can assist in enhancing the efficacy of current APL therapies and identifying novel therapeutic targets for chemotherapies. lncRNAs have been considered as prognostic and diagnostic molecular biomarkers for acute myeloid leukemia (AML) (8,9). Previous investigations have demonstrated that lncRNAs are crucial in myeloid differentiation and APL therapy (10–12). HOTAIRM1, a myeloid-specific lncRNA, has been defined as a key factor in ATRA-induced APL differentiation (11,13). Furthermore, HOTAIRM1 can promote the degradation of PML-RARα via a pathway associated with autophagy during myeloid cell differentiation or ATRA-induced APL differentiation (10). This suggests that aberrant lncRNA expression may be underlying targets for APL therapy and indicators for response to APL therapy. Several lncRNAs have been identified in APL-associated myeloid differentiation (10–13); however, additional key lncRNAs and their functions in regulating myeloid maturation require characterization in the APL-associated myeloid differentiation transcriptome.
NB4 cells represent a suitable cell model to investigate changes in lncRNA expression between APL cells and their terminally differentiated counterparts. System analysis of the transcriptome has been beneficial for the identification of various pathways or cascades at the transcriptome level associated with ATRA/ATO-induced cell differentiation (14). These are common in the absence of the complicated and dynamic intracorporeal synergy between ATRA and ATO in APL therapy, which may affect cell survival and response to therapy. Although it has been suggested that lncRNAs are involved in ATRA/ATO-induced extracorporeal APL differentiation, further understanding of the lncRNA landscape in ATRA/ATO-induced intracorporeal APL differentiation is required.
The aim of the present study was to examine lncRNA profiles and regulatory functions in ATRA-based targeted therapy for APL differentiation. The lncRNA and messenger RNA (mRNA) profiles were compared in three post-induction bone marrow (BM) samples from patients with APL and pre-induction (untreated) matched controls via whole transcriptome microarray. Subsequently, 10 dysregulated lncRNAs were selected and verified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis in another 27 APL BM samples. In addition, the functions of dysregulated lncRNAs were predicted via their co-expressed mRNAs. The findings identified the lncRNA landscape for myeloid differentiation in APL and revealed potential mechanisms occurring due to dysregulated lncRNA expression in ATRA-induced APL differentiation; this may provide underlying targets for APL therapy and lncRNA biomarkers for APL responses to therapy.
Materials and methods
Patient profiles
A total of 30 patients with APL who received ATRA-based targeted therapy at the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University (Wenzhou, China) between January 2014 and December 2016 were recruited for the present study. None of the patients received chemotherapy prior to targeted therapy. Written informed consent was collected from all the patients in conformity with the Declaration of Helsinki, and the present study obtained permission from the Ethics Committee of Wenzhou Medical University. The BM samples from the patients with APL at diagnosis and corresponding BM samples from patients with APL post-induction were collected in BD Vacutainer Heparin tubes (BD Biosciences, San Diego, CA, USA). Patient diagnoses were determined according to the 2016 World Health Organization criteria (15). The patients with APL were treated according to the International APL guidelines (15). Among the 60 samples, three paired samples (comprising three post-induction samples and three pre-induction samples) were used for lncRNA microarray analysis and the other samples were used for RT-qPCR analysis. The primary characteristics of the patients with APL are listed in Table I.
Sample collection and RNA extraction
The mononuclear BM cells (MBMCs) were isolated from ~2 ml heparinized BM samples using density gradient medium centrifugation (800 × g, for 20 min at room temperature). Subsequently, 1×107 cells were resuspended in TRIzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA) and temporarily stored at −80°C until further analysis. Total RNA was extracted from the MBMCs according to the manufacturer's protocol and dissolved in 100 µl nuclease-free water. The RNA yield was measured using a NanoDrop ND-2000 spectrophotometer from Thermo Fisher Scientific, Inc. and the RNA integrity was assessed using an Agilent 2100 bioanalyzer system (Agilent Technologies, Inc., Santa Clara, CA, USA). When the 28S:18S ratio was ascertained and the RNA integrity number (RIN) of each sample was assigned; RNA samples with a 28S:18S ratio ≥0.7 and RIN ≥7.0 were further analyzed.
lncRNA and mRNA microarray expression profiling
RNA from each sample (~200 ng) was applied for lncRNA and mRNA microarray analyses using Cluster 3.0 (http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm). Gene expression was analyzed using an Affymetrix GeneChip® Human Transcriptome Array 2.0 (Affymetrix, Inc., Santa Clara, CA, USA). The microarray contained 67,539 probes for 22,829 human lncRNAs and 44,710 human mRNAs, which were derived from eight authoritative databases, including RefSeq, Ensembl, UCSC, MGC, nocode, lncRNAdb, Broad Institute, TUCP catalogue and Human Body Map lincRNAs. Additionally, the microarray contained probes for small non-coding RNAs, but not microRNAs, and the majority of these were small nuclear RNAs and small nucleolar RNAs. The array experiments and computational analysis were performed according to the manufacturer's protocol (Affymetrix, Inc.). The raw data were extracted and standardized using the GeneChip Command Console software 4.0 and Expression Console software 1.3.1 from Affymetrix, Inc. Additional data processing was performed with GeneSpring software 12.5 (Agilent Technologies, Inc.). Dysregulated lncRNAs or mRNAs defined by an absolute value of fold change (FC) ≥2.0 and P≤0.05 (Student's t-test) were selected for further analysis. The dysregulated mRNAs were categorized into different Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation groups. The lncRNA chip experiments were performed in the laboratory at Shanghai OE Biotech Co., Ltd. (Shanghai, China).
RT-qPCR verification of 10 dysregulated lncRNAs
The lncRNA and mRNA expression were analyzed using FastStart Universal SYBR-Green Master Mix (Rox) from Roche Diagnostics (Indianapolis, IN, USA) and an ABI ViiA™ 7 Real-Time PCR system from Applied Biosystems (Thermo Fisher Scientific, Inc.). Briefly, the total RNA was transcribed into cDNA using a Transcriptor First Strand cDNA Synthesis kit (Roche Diagnostics) as per the manufacturer's protocol. PCR amplification was performed in a 25 µl reaction containing 1 µl of cDNA template (~10 ng), 12.5 µl of FastStart Universal SYBR-Green Master Mix (Rox), 10.5 µl of nuclease-free water, and 0.5 µl of each pair of primers (Shanghai GeneCore BioTechnologies Co., Ltd., Shanghai, China). The RT-qPCR primers for the lncRNAs are listed in Table II. The reaction conditions were as follows: 95°C for 10 min, followed by 40 cycles of 95°C for 15 sec and 60°C for 45 sec. All experiments were repeated three times in parallel. The expression of lncRNAs was further standardized to the GAPDH gene and calculated using the 2−ΔΔCq method (16).
Microarray results analysis and functional prediction of selected dysregulated lncRNAs
The identification of the overall functional distributions for the dysregulated lncRNAs identified in the experiment were performed as follows. Briefly, the co-expressed mRNAs for each dysregulated lncRNA were first calculated, and functional enrichment analysis for the set of co-expressed mRNAs was then performed. The enriched functional terms served as the predicted functional term for a given lncRNA. Furthermore, the co-expressed mRNAs of lncRNAs were identified by calculating the Pearson's correlation (P<0.05). The functional enrichment terms for annotating the co-expressed mRNAs were determined using the hypergeometric cumulative distribution function (17,18). The top 200 reliable prediction associations between the lncRNAs and the predicted functional terms were selected to reflect the functional distribution of the dysregulated lncRNAs. The frequency of each predicted functional term for these associations was determined, following which the GO term and KEGG term with more functional annotations were statistically identified (19).
Identification of cis-regulated mRNAs for the dysregulated lncRNAs
The present study further examined how the dysregulated lncRNAs may exert activities through cis- and/or trans-regulated mRNAs. The regions of cis-regulation were identified as follows: Gene locations for different lncRNAs on the chromosome were determined; for each dysregulated lncRNA, the mRNAs were identified as cis-regulated mRNAs when the co-expressed mRNA loci were within 300 kbp downstream or upstream of the given lncRNA and the Pearson's correlation of lncRNA-mRNA expression was significant at the P<0.05 level. When the mRNAs did not conform to the cis-regulated mRNA rules, they were identified as possible trans-regulated mRNAs.
Identification of transcription factors associated with dysregulated lncRNAs
It has been documented that specific lncRNAs may be involved in certain biological processes, including transcriptional regulation, through key transcription factors (TFs) (17). Therefore, the TF/chromatin regulation complexes that may have critical co-regulatory roles with lncRNAs were identified (18,20). Briefly, the set of co-expressed mRNAs for lncRNA and TF/chromatin regulation complex target genes was determined, the enrichment level of which was analyzed using the hypergeometric distribution. Therefore, the TFs prominently associated with dysregulated lncRNAs were finally determined. The lncRNA-TF network was established using the hypergeometric distribution, and a graph showing the associations between TFs and lncRNAs was drawn using Cytoscape software (version 3.6.1; http://www.cytoscape.org/), an open source software platform. In the network, the core TF with the highest degree of expression was considered the centre of highest importance.
Results
General expression profiles of dysregulated lncRNAs and mRNAs
To investigate the lncRNA landscape involved in ATRA-induced APL differentiation, lncRNA and mRNA microarray analyses were performed on BMMCs from patients with APL. The microarray data were filtered through a volcano plot to determine the dysregulated lncRNAs and mRNAs in BM samples from patients with APL post-induction and corresponding BM samples from patients with APL at diagnosis (Fig. 1A). The lncRNA and mRNA expression data were clustered using Cluster 3.0 (Fig. 1B). Based on the similar expression patterns, the samples were further classified into two groups using dendrogram-based methods for clustering. It was found that 825 lncRNAs were dysregulated between the patients with APL post-induction and the matched controls at diagnosis, with 410 upregulated and 415 downregulated lncRNAs (Fig. 1C). Among the dysregulated lncRNAs, NONHSAT076891 was upregulated the most, with an FC of 304.00, whereas TCONS_00022632-XLOC_010933 was downregulated the most, with an FC of 447.09. It was also found that 1,218 mRNAs were dysregulated, with 660 upregulated and 558 downregulated mRNAs (Fig. 1C). The most upregulated and downregulated mRNAs were MPEG1 and CYTL1, with FCs of 257.13 and 1,169.37, respectively. The top 20 upregulated and downregulated lncRNAs and mRNAs are listed in Table III.
 | Table III.Top 20 upregulated and downregulated lncRNAs and mRNAs in patients with APL with induction therapy. |
Verification of dysregulated expression of lncRNAs and mRNAs
To verify the dysregulated expression of lncRNAs and mRNAs associated with ATRA-induced APL differentiation, RT-qPCR analysis was performed to examine the upregulation or downregulation of the genes. A total of 10 dysregulated lncRNA transcripts were selected for RT-qPCR analysis. These 10 lncRNAs consisted of four randomly selected lncRNAs, including NONHSAT121385, ENST00000469418, ENST00000424415, and NR_003186, and six specifically selected lncRNAs, including ENST00000536425, NONHSAT061249, TCONS_00017553-XLOC_008249, TCONS_l2_00030950-XLOC_l2_015963, NONHSAT076891, and TCONS_00022632-XLOC_010933. ENST00000536425 and NONHSAT061249 were selected as a result of their predicted cis-regulating potential. TCONS_00017553-XLOC_008249 and TCONS_l2_00030950-XLOC_l2_015963 were selected due to their predicted trans-regulating potential and their presence among the top 100 lncRNA-TF pairs. NONHSAT076891 and TCONS_00022632-XLOC_010933 were selected as they were the most upregulated and downregulated, respectively (Table III). The RT-qPCR results were in agreement with the results obtained from the microarray chip analysis (Fig. 2). As shown in Fig. 2, all selected lncRNAs were dysregulated and exhibited the same trend of upregulation or downregulation (P<0.05 for each lncRNA, Student's t-test).
lncRNA and mRNA co-expression profiles and lncRNA function prediction
Several hundred lncRNAs were co-expressed with hundreds of mRNAs. For example, ENST00000419668 was co-expressed with 515 mRNAs and TCONS_00022632-XLOC_010933 with 488 mRNAs. The GO and KEGG pathway annotations of the co-expressed mRNAs were used to predict the functions of the dysregulated lncRNAs. The lncRNAs were clustered into hundreds of GO and KEGG terms with more functional annotations. In the corresponding association between the ‘lncRNA name’ and ‘functional prediction term’, the top 200 predicted associations were selected to reflect the functional distribution of the dysregulated lncRNAs. Among the GO terms, the most common biological processes for the dysregulated lncRNAs were cell cycle phase transition, regulation of spindle checkpoint, negative regulation of viral genome replication, DNA damage checkpoint, and attachment of spindle microtubules to kinetochore (Fig. 3A). The most common KEGG terms were DNA replication, spliceosome, NF-κB signalling pathway, mismatch repair, primary immunodeficiency, nucleotide excision repair, and cell cycle (Fig. 3B). Therefore, according to the enrichment, cell cycle phase transition was the most enriched GO term and DNA replication was the most enriched KEGG term (Fig. 3).
Analysis of ‘cis’ lncRNAs and their adjacent co-expressed mRNAs
Evidence shows that a number of lncRNAs may cis-regulate the transcription of themselves and their adjacent mRNAs by recruiting remodelling factors to local chromatin (21). To determine the potential ‘cis’ mRNAs associated with a specific lncRNA, the co-expressed mRNAs 300 kb upstream and downstream of the dysregulated lncRNAs were analyzed. In total, 48 lncRNAs and their predictively cis-regulated mRNAs were identified using accurate genomic mapping based on the criteria mentioned above. The lncRNAs and their underlying cis-regulated mRNAs are listed in Table IV. The results suggested that lncRNA ENST00000536425 cis-regulates one mRNA, early endosome antigen 1 (EEA1) (Fig. 4A), whereas lncRNA NONHSAT061249 cis-regulates two mRNAs, including zinc finger protein 564 (ZNF564) and zinc finger protein 44 (ZNF44) (Fig. 4B).
Dysregulated lncRNA ‘trans’ mechanism and construction of the TF-lncRNA-target gene network
As numerous dysregulated lncRNAs were involved in mRNA regulation, a ‘TF-lncRNA’ network is likely to be large and complex. Consequently, the top 100 associations were selected to generate a core TF-lncRNA network, visualized by hypergeometric distribution analysis (Fig. 5). The core TF-lncRNA network map for patients with APL post-induction, vs. matched controls at diagnosis is shown in Fig. 5A. The majority of potential trans-regulation lncRNAs were found to be involved in pathways regulated by three TFs, including E2F transcription factor 1 (E2F1), E2F transcription factor 6 (E2F6), and early B cell factor 1 (EBF1) (Fig. 5B). In the core network of lncRNA-TF pairs, E2F1, E2F6, and EBF1 regulated the expression of 16 lncRNAs, 13 lncRNAs and 10 lncRNAs, respectively.
Based on the lncRNA co-expression results, the target genes were incorporated into a ‘TF-lncRNA’ network to determine the TF-lncRNA-target network. Due to the large and complex networks, the top 10 associations were selected to produce a core TF-lncRNA-target network map (Fig. 6). The core TF-lncRNA-target gene association for patients with APL post-induction therapy, vs. pre-induction therapy is shown in Fig. 6, and includes 10 dysregulated lncRNAs (TCONS_l2_00006587-XLOC_l2_002952, ENST00000536425, NR_002174, TCONS_00002901-XLOC_001489, ENST00000558044, NONHSAT101365, TCONS_00017553-XLOC_008249, TCONS_00000490-XLOC_000746, NONHSAT034763, and TCONS_l2_00030950-XLOC_l2_015963), 247 target genes, and eight TFs (TAF1, GTF2B, E2F4, ZBTB7A, E2F1, NFYB, MYC, and E2F6) in the core map. As shown in Fig. 6, the core TF MYC association regulated two lncRNAs (TCONS_00017553-XLOC_008249 and TCONS_l2_00030950-XLOC_l2_015963) and 97 target genes. As observed for ‘MYC-TCONS_00017553-XLOC_008249-RIOK1’ in this map, target genes, including RIOK1, were co-expressed for TCONS_00017553-XLOC_008249. As observed for ‘MYC- TCONS_l2_00030950-XLOC_l2_015963-RPLP2’ in the map, target genes, including RPLP2, were co-expressed for TCONS_l2_00030950-XLOC_l2_015963-RPLP2. Therefore, these maps provided vital information on the lncRNAs, TFs and target genes.
Discussion
In the present study, the expression patterns of genome-wide lncRNAs were first evaluated in BM samples from patients with APL post-induction and corresponding BM samples from patients with APL at diagnosis using microarray analysis. Their potential functions were then examined by analysing their co-expressed mRNAs. The experimental results showed that 825 lncRNAs and 1,218 mRNAs were dysregulated. Furthermore, 10 selected dysregulated lncRNAs were validated by RT-qPCR analysis. Several hundred lncRNAs were co-expressed with hundreds of mRNAs, and a number of these may contribute to ATRA/ATO-induced intracorporeal myeloid differentiation by affecting these mRNAs in trans and/or in cis. The present study not only clarified the contributions of lncRNAs to myeloid differentiation in APL and/or intracorporeal therapy response, but also elucidated possible dysregulated lncRNA expression mechanisms associated with ATRA-induced APL differentiation.
APL has shifted from a complex problem in the past into a paradigm with successful targeted therapies, and a series of published randomized clinical trials in patients with APL have all demonstrated efficacy on the frontline of ATRA/ATO association. The identification of a novel class of lncRNAs has attracted attention and may have encouraged investigations focused on characterizing lncRNAs associated with myeloid differentiation and responses to APL therapy. For example, several lncRNAs, including NEAT1 and HOTAIRM1, are indispensable during APL differentiation induced by ATRA (10,12,22). However, numerous lncRNAs and their roles in APL differentiation remain to be fully elucidated. A systematic analysis of the ATRA/ATO-induced intracorporeal myeloid differentiation transcriptome may reflect the complicated and dynamic intracorporeal synergy between ATRA and ATO in patients with APL. lncRNA and mRNA analysis in BM from patients with APL is expected to reflect real changes.
To examine global lncRNA and mRNA profiling in the present study, the Human Transcriptome Array 2.0 was used to screen the dysregulated lncRNAs in three patients with APL post- and pre-induction therapy. The results showed that 825 lncRNAs and 1,218 mRNAs were dysregulated, including 410 upregulated lncRNAs, 415 downregulated lncRNAs, 660 upregulated mRNAs, and 558 downregulated mRNAs, suggesting that these dysregulated lncRNAs may be involved in ATRA-induced myeloid differentiation. Consequently, the present study may assist in determining whether these dysregulated lncRNAs and mRNAs can be applied for the early assessment of APL therapy response or efficacy. The present study also cross-validated the dysregulated gene results from the GeneChip with the results of RT-qPCR analysis; this comparison showed consistency between the microarrays and RT-qPCR results, which supports further predictions.
An increasing number of lncRNAs have been recognized as critical factors in gene programs to control cell differentiation and function, and serving as scaffolds or decoys at the transcriptional and translational levels (23). Although the number of novel and known lncRNAs is increasing exponentially, only a small subset of these have functional annotations, and the most commonly used method to predict lncRNA function is by investigating their co-expressed mRNAs and associated biological pathways. In the present study, it was found that thousands of mRNAs are co-expressed with the dysregulated lncRNAs. Through functional prediction with the co-expressed mRNAs, the present study identified lncRNAs involved in DNA replication associated with cell cycle phase transition as the most affected by ATRA-induced myeloid differentiation. When stabilized and inhibited via cell cycle phase transitions, lncRNAs function as a vital factor for regulating cell cycle during the course of myeloid maturation in NB4 APL cells (13). Therefore, lncRNAs may be a potential modulator of DNA replication, in addition to certain regulatory TFs.
Compared with mRNAs, lncRNAs have the inherent characteristic of cis-regulation (24) and can cis-regulate their adjacent mRNAs (25). The epigenetic upregulation of lncRNAs is associated with cis-downregulation of a functional gene cluster in leukemia (26). In the present study, 48 lncRNAs were found to potentially promote cis-regulation of their adjacent mRNAs. Although the majority of the identified lncRNAs have not been characterized, two sets of lncRNA-mRNAs, including ENST00000536425 and EEA1 mRNA and NONHSAT061249 and ZNF564 and ZNF44 mRNAs, were identified. A previous study revealed that EEA1 serves as an identifying marker of early endosomes for neddylation of type II receptor, and aberrant neddylation results in the development of leukemia (27). The biological processes associated with ZNF564 and ZNF44 are involved in transcription and transcription regulation, respectively. These findings suggest that lncRNA-mRNA networks may contribute to the regulation of cell responses to ATRA-induced APL differentiation.
Certain lncRNAs have been demonstrated to be involved in cis-regulation; however, the majority of characterized lncRNAs are functionally trans-regulating (18,24). The present study predicted the trans-regulatory functions of lncRNAs through TFs. In the central network of lncRNA-TF pairs, the potential trans-regulatory lncRNAs were mainly trans-regulated by E2F1, E2F6, and EBF1. E2F1 and E2F6 are TFs that contribute to controlling cell cycle. Several studies have associated the activity of E2F with cell-cycle control (28,29). The E2F1-C/EBPa feedback loop regulates the expression of the oncogene tribbles 2, which is important for AML cell proliferation control (30). E2F6 may transcriptionally regulate cell-cycle G1/S genes via recruiting BRG1 (29). EBF1, a TF that is critical for normal and malignant B-lymphocyte development, controls DNA repair in a dose-dependent manner, which may explain the reason for the frequent loss of the EBF1 gene in leukemia (31). In the present study, the dysregulated lncRNAs involved in the pathways mainly regulated by E2F1, E2F6, and EBF1 were candidate participants in ATRA-induced myeloid differentiation. Consequently, the ‘trans’ analysis provides a method to interpret the functions of lncRNAs and their biological processes in APL therapy.
The lncRNA-TF analysis identified novel lncRNAs and three TFs for enriched dysregulated mRNAs that contributed to APL treatment. The results of the ‘cis’ and ‘trans’ analyses provided essential clues on the modular regulation of lncRNAs. The data obtained may promote future investigations on therapeutic mechanisms in APL. However, the present study had several limitations. First, the sample size was small; thus, investigations with larger sample sizes are required. Second, as the functions of several of the identified lncRNAs have not been annotated, it was only possible to predict the functions of lncRNAs through network and pathway analyses with their co-expressed mRNAs. Therefore, the biological functions of these lncRNAs require further validation.
In conclusion, the present study offered insight into the genome-wide patterns of lncRNA expression during the course of ATRA-based APL therapy. A set of dysregulated lncRNAs were identified in patients with APL who received ATRA-based therapy compared with untreated matched controls. Several lncRNAs may be involved in biological pathways associated with ATRA-induced myeloid differentiation through the cis and/or trans regulation of mRNAs. Furthermore, targeting aberrantly activated pathways in APL cells may offer strategies to circumvent or mitigate disease. The present study provides a foundation for future investigations on lncRNAs associated with ATRA-based APL therapy as therapeutic and diagnostic targets by supplying candidate genes. Additionally, the results provide a platform for systematically evaluating a large number of lncRNAs and mRNAs to identify pathways critical for APL elimination using primary APL patient specimens prior to and following targeted therapy.
Acknowledgements
We thank Professor Yan Li and Professor Yongqing Tong (Department of Clinical Laboratory, Renmin Hospital of Wuhan University) for their selfless assistance. We thank Shanghai OE Biotech Co., Ltd. (Shanghai, China) for providing lncRNA chip analyses and American Journal Experts (AJE) for providing language help.
Funding
The present study was supported by the Medical and Health Research Science and Technology Plan Project of Zhejiang Province (grant nos. 2017KY112 and 2018KY523), the Public Welfare Science and Technology Plan Project of Wenzhou City (grant no. Y20170201), the National Nature Science Foundation of China (grant no. 81701426) and the PhD Research Launching Fund Project of the Second Affiliated Hospital of Wenzhou Medical University (grant no. FEY001).
Availability of data and materials
The datasets used during the present study are available from the corresponding authors upon reasonable request.
Authors' contributions
ZGC, JCR and XQZ designed and supervised the project. YYB and JJY provided the samples and clinical information. JY and XLG performed the experiments. YYB and XQZ provided instructions for the experiments. ZGC and JY analyzed the data and wrote the manuscript. ZGC and XLG edited the manuscript. All authors read and approved the manuscript and agree to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Ethics approval and consent to participate
Written informed consent was collected from all the patients in conformity with the Declaration of Helsinki, and the present study obtained permission from the Ethics Committee of Wenzhou Medical University.
Patient consent for publication
Written informed consent was collected from all the patients.
Competing interests
The authors declare that they have no competing interests.
Glossary
Abbreviations
Abbreviations:
|
APL |
acute promyelocytic leukemia |
|
lncRNAs |
long non-coding RNAs |
|
ATRA |
all-trans-retinoic acid |
|
mRNA |
messenger RNA |
|
BM |
bone marrow |
|
RT-qPCR |
reverse transcription-quantitative polymerase chain reaction |
|
ATO |
arsenic trioxide |
|
AML |
acute myeloid leukemia |
|
MBMCs |
mononuclear bone marrow cells |
|
TFs |
transcription factors |
|
FC |
fold change |
|
KEGG |
Kyoto Encyclopedia of Genes and Genomes |
|
GO |
Gene Ontology |
|
EEA1 |
early endosome antigen 1 |
|
ZNF564 |
zinc finger protein 564 |
|
ZNF44 |
zinc finger protein 44 |
|
E2F1 |
E2F transcription factor 1 |
|
EBF1 |
early B cell factor 1 |
|
E2F6 |
E2F transcription factor 6 |
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