Genome-wide profiling of long non-coding RNA expression patterns in the EGFR-TKI resistance of lung adenocarcinoma by microarray
- Authors:
- Published online on: April 20, 2016 https://doi.org/10.3892/or.2016.4758
- Pages: 3371-3386
Abstract
Introduction
Lung cancers with epidermal growth factor receptor (EGFR) gene mutations are a well-understood subgroup of lung adenocarcinomas (LACs). This subgroup is characterized by sensitivity to EGFR tyrosine kinase inhibitors (TKIs; e.g. gefitinib or erlotinib), and has high occurrence rates in females, non-smokers and Asians (1). However, a median therapy (10–14 months) with EGFR TKI may aggravate the disease progression in most patients in this subgroup. The mechanisms of EGFR-TKI resistance are multi-factorial and several of them have been reported, such as T790M mutation in exon 20 of EGFR, MET amplification and depletion of phosphatase and tensin homolog (PTEN) (2–4). However, nearly 30% of resistance mechanisms are unknown. Thus, elucidating the molecular mechanisms of EGFR-TKI resistance is essential for the identification of key biomarkers.
In the last two decades, non-coding RNAs (ncRNAs) have been gradually accepted as key factors in the process of epigenetic regulation rather than 'transcription noise' (5). As recently reported, ncRNAs take part in the pathogenesis of non-small cell lung cancer (NSCLC), providing new biological insights into this disease (6,7). Long non-coding RNAs (lncRNAs), which are non-protein coding transcripts with length >200 nucleotides (nt), are involved in regulating the occurrence, invasion, metastasis and chemotherapeutic resistance of lung cancer (6,8,9). lncRNAs also take part in the regulation of EGFR-TKI resistance. GAS5, one lncRNA, was found to be overexpressed in EGFR-TKI-sensitive cells compared with its expression in resistant cells, and was found to enhance gefitinib-induced cell death in innate EGFR-TKI-resistant LAC cells with wild-type EGFR via downregulation of IGF-1R expression (10). These findings indicate that lncRNAs may be promising biomarkers as diagnostic and therapeutic targets in the resistance of EGFR-TKIs.
The present study presents the lncRNA expression profiles in 3 replicate gefitinib-sensitive HCC827 and gefitinib-resistant HCC827-8-1 cells in pairs by microarray. Then, 7 of the differentially expressed lncRNAs were validated by real-time quantitative PCR (RT-qPCR) in HCC827 and HCC827-8-1 cells. We also predicted the functions of differentially expressed lncRNAs through their co-expressed protein-coding genes.
Materials and methods
Cell culture
The human NSCLC H827 cell line harboring the EGFR exon 19 deletion (Del E746-A750) was obtained from the Shanghai Institutes for Biological Sciences, Chinese Academy of Cell Resource Center. We generated a gefitinib-resistant cell line by exposing HCC827 cells to increasing concentrations of gefitinib as described in our previous study (11). One individual clone HCC827-8-1 was isolated and independently confirmed to be resistant to gefitinib. HCC827-8-1 cells were 348-fold more resistant to gefitinib than the parental HCC827 cells. The cells were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS) (Gibco, Carlsbad, CA, USA) at 37°C, in a 5% CO2 and humidified atmosphere.
RNA extraction
HCC827 and HCC827-8-1 cells were seeded in 6-well plates (1×105 cells/well) for 72 h in 3 replicate wells, and then resuspended in 500 μl lysis buffer. Total RNA was extracted from lysis buffer using the mirVana miRNA Isolation kit procedure (Applied Biosystem, Foster City, CA, USA), according to the manufacturer's specifications, and eluted with 100 ml of nuclease-free water. The yield of RNA was quantified by the NanoDrop ND 2000 (Thermo Scientific, Waltham, MA, USA) and the RNA integrity was assessed using Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA).
lncRNA and mRNA microarray expression profiling
Total RNA was transcribed to double-stranded cDNA, and then synthesized into cRNA and labeled with cyanine 3-CTP. The labeled cRNAs were hybridized onto the microarray. The Agilent human lncRNA array (4×180K) contains 32,776 human mRNAs and 78,243 human lncRNAs, which are derived from authoritative databases, including RefSeq, Ensemble, GenBank and the Broad Institute. After washing, the arrays were scanned by the Agilent Scanner G2505C (Agilent Technologies). Raw datum was extracted using Feature Extraction (version 10.7.1.1; Agilent Technologies). The microarray profiling was conducted in the laboratory of the OE Biotechnology Co. (Shanghai, China).
RT-qPCR validation of 7 differentially expressed lncRNAs
Total RNA was extracted from HCC827 and HCC827-8-1 cells using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's instructions. One microgram of total RNA was reverse transcribed in a final volume of 20 μl using PrimerScript RT Master Mix (Takara, Dalian, China). The reverse transcription reaction was carried out under the following conditions: 37°C for 15 min; 85°C for 5 sec, and then hold on 4°C. The RT-qPCR was performed using SYBR-Green PCR Mix (Roche, Mannheim, Germany) on the ABI 7900 system (Applied Biosystems) according to the manufacturer's instructions. The primer sequences are listed in Table I. β-actin was used as an internal control to normalize the amount of total RNA in each sample. Each sample was run in triplicate for analysis. At the end of the PCR cycles, melting curve analysis was performed to validate the specific generation of the expected PCR product. The expression levels of lncRNAs were normalized to internal control gene β-actin and were calculated using the 2−ΔΔCt method (12).
Differential expression level of mRNAs and lncRNAs from the microarray
After quantile normalization, raw signals from the microarray were log2 transformed. Differential expression of an mRNA or lncRNA was defined by the absolute value of fold-change (FC) >2 (gefitinib-sensitive HCC827 cells=1) and P-value <0.05 (Student's t-test), with the 3 parallel samples in the HCC827 or HCC827-8-1 group having detectable signals compared with the background. The differentially expressed mRNAs were submitted to the NCBI (gene_go_information) and KEGG database analyzed by Python program to be classified into different Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation groups.
Co-expression of lncRNAs with mRNAs and functional prediction
Most of the lncRNAs in the current databases have not yet been functionally annotated. Thus, the prediction of their functions is based on the functional annotations of their co-expressed mRNAs. This method was originally described by Guttman et al (13). In brief, first, for every dysregulated lncRNA, Pearson correlation coefficient (PCC) of its expression with that of each dysregulated mRNA was calculated to find its co-expressed mRNAs, with PCC >0.7 or ≤0.7 and P-value of PCC <0.05 being statistically significant. Then, a functional enrichment analysis of the co-expressed mRNAs was conducted using the hypergeometric cumulative distribution function, and the enriched GO/KEGG pathway annotations were assigned to the lncRNA as its predicted functions. The threshold of statistical significance is set as a P-value <0.05 and false discovery rate (FDR) <0.05 [under the control of the Benjamini and Hochberg procedure (14)].
In addition, on the basis of co-expression, we further explored how these dysregulated lncRNAs may exert their functions through cis- and/or trans-regulating protein-coding genes. We defined cis-regulated genes as protein-coding genes co-expressed with one dysregulated lncRNA and within 300 kb in genomic distance in the same allele.
We potentially defined trans-regulated protein-coding genes as co-expressed and beyond 300 kb in genomic distance from, or, on the other allele of, differentially expressed lncRNAs. In other words, for any protein-coding gene co-expressed with an lncRNA, if it did not fit the criteria of cis-regulated, it was categorized as potentially trans-regulated.
According to Guttman et al (13,15), specific lncRNAs participating in certain biological pathways are transcriptionally regulated by key transcription factors (TFs) that regulate these pathways. Thus, to categorize lncRNAs that possibly have trans-regulating functions, we compared the mRNAs that co-expressed with these lncRNAs with the mRNAs that are regulatory targets of certain TFs. If the intersection of these 2 groups is large enough (P<0.05; calculated by hypergeometric cumulative distribution function and FDR <0.05, under the control of the Benjamini and Hochberg procedure), then, we predict that these lncRNAs possibly participate in pathways regulated by these TFs. The lncRNA-TF network was constructed using hypergeometric cumulative distribution function with the help of Perl. The graph of the lncRNA-TF network was drawn with the help of Cytoscape 3.01.
Results
General expression profiles of the differentially expressed lncRNAs and mRNAs
We found that 1,476 lncRNA transcripts were differentially expressed between the HCC827-8-1 and paired HCC827 cells, with 703 being upregulated and 773 being downregulated. Among the dysregulated lncRNA transcripts, ENST00000464359 (probe CUST_89973_PI429545380) was the most upregulated, with an FC of 136.11, whereas ENST00000602301 (probe CUST_1064_PI429545384) was the most downregulated, FC being 81.46. According to the absolute value of FC, the dysregulated lncRNA transcripts were stratified into 4 groups: 2 transcripts with FCs >100, 38 transcripts between 10 and 100, 117 transcripts between 5 and 10 and 1,319 transcripts between 2 and 5.
Using the same criteria of lncRNAs, we found that 1,026 mRNA transcripts were dysregulated, with 516 being upregulated and 510 being downregulated. The most upregulated and downregulated mRNA transcripts were FOXP2 (A_24_P16559) and MCOLN2 (A_23_P23639), with FCs of 74.25 and 31.03, respectively. Table II lists the top 20 upregulated and downregulated lncRNAs and mRNAs from our microarray.
 | Table IITop 20 upregulated and downregulated lncRNAs and mRNAs in the HCC827 cells compared with the gefitinib-resistant HCC827-8-1 cells. |
In the GO pathway analysis, the most common pathways that the dysregulated mRNAs were involved in included chondroitin sulfate metabolic process (GO, biological processes), extracellular space (GO, cellular components) and receptor binding (GO, molecular functions). The most common KEGG pathways involved were graft-versus-host disease, type I diabetes mellitus and viral myocarditis.
In unsupervised hierarchical clustering analysis, the differentially expressed lncRNAs were used to generate a heat map; and they clearly self-segregated into HCC827 and HCC827-8-1 clusters, as shown in Fig. 1.
RT-qPCR validation
To validate the results of the microarray, we chose a total of 7 differentially expressed lncRNA transcripts for RT-qPCR. They could be divided into 2 groups: the first group, including FR165245, which was randomly chosen. In the other group, NONHSAT107900 and NONHSAT082241 were chosen since they were predicted to have cis-regulating potential. ENST00000434951 and ONHSAG031748 were chosen since they were predicted to have trans-regulating potential and involved in constituting the top 100 lncRNA-TF pairs with the most credentiality. ENST00000464359 and ENST00000602301 were chosen for being the most upregulated and downregulated lncRNAs (Table II).
The RT-qPCR results were consistent with that of the microarray, in that all 7 lncRNA transcripts were differentially expressed with the same trend (upregulated or downregulated) and reached statistical significance (P<0.05 for each lncRNA; Student's t-test) as shown in Fig. 2.
lncRNA and mRNA co-expression profiles and lncRNA function prediction
Hundreds of lncRNAs were co-expressed with thousands of mRNAs. For example, ENST00000412387 (CUST_86592_PI429545380) was co-expressed with 3,487 mRNA transcripts and NONHSAT060786 (CUST_61584_PI429545406) with 4,642 mRNA transcripts.
The functions of the differentially expressed lncRNAs were predicted by the GO and KEGG pathway annotations of their co-expressed mRNAs. The lncRNAs were clustered into hundreds of GO and KEGG pathway annotations. Certain pathways are known to be involved in the mechanism of gefitinib resistance, including focal adhesion, cell cycle, cell proliferation and apoptosis (16–18). For example, 186 lncRNAs were clustered into apoptosis and 79 in the focal adhesion. As expected, one lncRNA can participate in more than one GO/KEGG pathway involved in the mechanism of gefitinib resistance. For example, ENST00000412387 (CUST_86592_PI429545380) was predicted to have functions in apoptosis, cell cycle, focal adhesion and pathways in cancer.
From the matrix of the lncRNAs and their corresponding KEGG pathway annotations of co-expressed protein-coding genes, we counted and summarized the top 200 annotations with the most credentiality (the lowest P-values). The most frequently predicted functions of the differentially expressed lncRNAs were metabolic pathways, glyoxylate and dicarboxylate metabolism and N-glycan biosynthesis as shown in Fig. 3.
cis-regulation of lncRNAs
A total of 149 lncRNA transcripts with their predicted cis-regulated protein-coding genes were found through accurate genomic mapping, using the above mentioned criteria. Table III lists all the lncRNA transcripts and their potentially cis-regulated mRNA transcripts. For instance, lncRNA NONHSAG010348 was predicted to cis-regulate five mRNAs (corresponding to five mRNA transcripts): COPS7A, TPI1, EMG1, USP5 and LRRC23.
Trans-regulation of the lncRNAs
Using the threshold of P<0.05 and FDR <0.01, 3,429 lncRNA-TF pairs were found, corresponding to 48 TFs. Then, we generated a core network using the top 100 lncRNA-TF pairs with the most credentiality (lowest P-values and FDRs), as shown by Fig. 4 and are listed in Table IV. We can see that most of these potential trans-regulatory lncRNAs participate in pathways regulated by four TFs: E2F transcription factor 1 (E2F1), E2F transcription factor 4 (E2F4), upstream transcription factor 1 (USF1) and transcription factor AP-2γ (TFAP2C). In the core network of lncRNA-TF pairs, E2F1 participates in 31 of the 100 pairs, E2F4 in 10 pairs, USF1 in 17 pairs and TFAP2C in 30 pairs.
Discussion
We assessed genome-wide lncRNA expression patterns in gefitinib-resistant human NSCLC HCC827-8-1 cells, compared with their parental HCC827 cells by microarray and explored their possible functions by analyzing their co-expressed protein-coding mRNAs. HCC827-8-1 is an individual clone isolated from resistant HCC827 cells, which is 348-fold more resistant to gefitinib than HCC827 cells with high stability (11). We used HCC827-8-1 cells to study the EGFR-TKI resistance mechanisms and to obtain more accurate results.
The microarray showed that 1,476 lncRNA and 1,026 mRNA transcripts were dysregulated. Then we chose and validated 7 of the differentially expressed lncRNAs by RT-qPCR. Hundreds of lncRNAs were co-expressed with thousands of mRNAs, and some of the lncRNAs may be active in the mechanism of gefitinib resistance through cis-regulation and/or in trans-regulation of these mRNAs.
We predicted the roles of lncRNAs through the co-expressed mRNAs. The most widely used technique for function prediction is the 'guilt by association' method, which aims to interrogate the co-expressed protein-coding genes and relevant bio-pathways (13). Among the hundreds of pathways as-predicted, some are key pathways in the mechanisms of gefitinib resistance, such as focal adhesion, cell proliferation, cell cycle and apoptosis (16–18). We compared the differentially expressed mRNAs with other studies, and the high consistency strongly supported our predictions. For example, in the mRNA expression analysis using Agilent SurePrint G3 Human Gene Expression 8×60K array of a gefitinib-resistant lung cancer cell line (19), IGFBP3 was found to be expressed differentially, which is in accordance with our results. Additionally, the constitutive activation of EGFR tyrosine kinase receptor caused by mutation and/or amplification is closely correlated with the development, progression and poor prognosis of NSCLC, through the activation of various downstream signaling pathways. These pathways include the RAS/RAF/MAPK pathway that induces signals associated with proliferative activity, and the PI3K/AKT pathway that results in an anti-apoptotic effect (16). In the present study, the expression levels of AKT and RAS were higher in the HCC827-8-1 cells than that in the HCC827 cells.
cis-regulation is the intrinsic property of ncRNAs, since mRNAs are only effective in dissociation, transportation and translation (5). Moreover, the cis-regulation of nearby protein-coding genes is a common mechanism of lncRNAs (20). In the present study, we identified 149 lncRNA transcripts that were able to cis-regulate the nearby protein-coding genes. Although most of the 149 lncRNAs were uncharacterized, several of the lncRNA-mRNAs attracted our interest. lncRNAs that are related to dysregulated mRNAs known to be involved in EGFR-TKI resistance are valuable for further study. For example, one lncRNA is NONHSAT021952 (CUST_7952_PI429545402), which probably cis-regulates VEGF-B mRNA. As reported, VEGF-B plays important roles in promoting cancer metastasis through the remodeling of microvasculature of NSCLC, which is related to EGFR-TKI resistance (21). NONHSAT021952 (chr11: 63988306-63990592+) has no annotated functions and lies near VEGF-B (chr11: 64006111-64006170+) in the same chromosome. Thus, there may be a new way that nearby lncRNAs regulate the expression of VEGF-B.
Alhough some lncRNAs are proven as cis-regulators, the vast majority of lncRNAs with known functions are actually trans-regulators (22). In contrast to cis-regulation, the trans-regulatory lncRNAs, after isolation from the synthesis site, affect the genes at a long genomic distance away, accumulate and take effect in large regulatory networks (5). We predicted the functions of trans-regulatory lncRNAs through the relevant expression-regulating TFs. In the core network of lncRNA-TF pairs, the lncRNAs were divided into four groups of pathways that are controlled by E2F4, E2F1, USF1 and TFAP2C, respectively.
E2F4 and E2F1 which belong to the E2F family are proposed to be primary transcription repressors. The E2F family is pivotal in the regulation of cell cycle and the action of tumor suppressor proteins. The E2F family is also a target of the transforming proteins of small DNA tumor viruses (23).
E2F1 regulates the transcription of DNA synthesis-related genes and thus, the binding of hypophosphorylated pRB to E2F1 will arrest G1 phase cells (24). Gefitinib treatment downregulates the expression of E2F1 mRNA and protein. The antiproliferative effect of gefitinib is attributed, or at least partially, to the inhibition of E2F1 expression and increases the proportion of G1 phase cells in human LACs (A431 and A549) (25). E2F1 is also downregulated by gefitinib treatment in gefitinib-resistant NSCLC cells with MET amplification, but not in resistant NSCLC cells harboring the T790M mutation of EGFR (26). The above studies indicate that E2F1 is closely associated with the resistance to EGFR-TKIs. The lncRNAs predicted as regulatory targets of E2F1 may take part in the acquisition of gifitinib resistance, which should be validated by further functional tests.
E2F4 takes part in diverse functions, such as cell cycle control, DNA damage repair, apoptosis, mRNA processing and ubiquitination (27,28). The upregulation of E2F4 nuclear expression in breast cancer is related to poor prognosis in patients with larger tumors, recurrence, distant metastasis and poorer outcome (29). E2F4 protein expression in human colorectal tumors is also upregulated by affecting the G1/S phase transition and the proliferation of colon cancer cells (30). In comparison, E2F4 protein expression is down-regulated in sporadic Burkitt lymphoma (sBL) cells but not in immortal B-cells. Besides the ability to reduce E2F1 levels, E2F4 expression in BL cells also selectively weakens the tumorigenic properties and BL cell proliferation and increases the proportions of G2/M cells (31). Since E2F4 has diverse effects in the regulation of tumor progression, it may also play a role in lung cancer.
USF1 is a basic helix-loop-helix (bHLH) TF encoded by distinct genes that are heterodimerized and functionally overlapped (32). USF1 also mediates the transcription of many neuropeptides (33–36), surfactant protein A in the lung (37), genes in lung tumors (38) which include hTERT as the immortalizing telomerase subunit (39,40) and CDK4 as the cell cycle regulator (41). USF1 can also regulate neuropeptide genes in lung cancer, particularly arginine vasopressin (AVP) (42,43). USF1 mRNA expression is downregulated in LACs compared with that in normal tissues (44). In lung cancer, USF1 and USF2 dimerize to mediate the transcription via E-box motifs in target genes (45). Moreover, with the E-box motif, USF1 activates GATA5 gene expression (46), which is pivotal in cell differentiation and tissue development in lung cancer (47,48). Since USF1 is closely related to lung cancer occurrence, USF1-regulated lncRNAs may be involved in EGFR-TKI resistance.
TFAP2C, also known as AP2-γ, belongs to the AP-2 TF family, which is composed of five closely related 50-kDa isoforms (49). By regulating the expression of many downstream genes, TFAP2C mediates various cellular processes, including induction, differentiation, survival, proliferation and apoptosis under diverse developmental contexts (50–54). The internalization of activated EGFR involves the AP-2 complex (55). TFAP2C is pivotal in human cancer development. For example, through transcriptional regulation of EGFR, TFAP2C mediates the tumor development, cell growth and survival of HER2-amplified breast cancer (56). Moreover, after 10 years of diagnosis, higher TFAP2C scores are correlated with poor overall survival in ERα-positive and endocrine therapy-treated patients (57). Since TFAP2C affects tumorigenesis by mediating EGFR expression, TFAP2C may be an important cause of EGFR-TKI-resistant lung cancer. Thus, the lncRNAs that take part in the TFAP2C-regulated pathways, as predicted, are candidate participants in the mechanisms of EGFR-TKI resistance.
The present study has also a few limitations. Firstly, we only used one cell line to study the mechanisms of EGFR-TKI resistance in vitro. Our results are not integrated but typical, and thus further studies with larger sample size in vivo are needed. Second, we only predicted lncRNA functions indirectly through network and pathway analyses of co-expressed protein-coding genes. The reason is that the majority of the lncRNAs identified to date are not functionally characterized (58). The 'guilt by association'' method for hypothesis generation is a key primary step for functional studies in the future, such as loss/acquisition of functions (59).
In conclusion, nearly 1,500 lncRNAs were differently expressed between the gefitinib-sensitive HCC827 cells and the gefitinib-resistant HCC827-8-1 cells. Many of these lncRNAs play important roles in regulating EGFR-TKI resistance through the cis- and/or trans-regulation of target protein-coding genes. The present study underlies future functional studies on lncRNAs related to EGFR resistance and provides a candidate reservoir for their application as diagnostic and therapeutic targets.
Acknowledgments
The present study was supported by grants from the National Natural Science Foundation of China (81372396).
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