Detection of long-chain non-encoding RNA differential expression in non-small cell lung cancer by microarray analysis and preliminary verification
- Authors:
- Published online on: November 13, 2014 https://doi.org/10.3892/mmr.2014.2944
- Pages: 1925-1932
Abstract
Introduction
The global lung cancer mortality rate is the highest among all types of cancer and its incidence is gradually increasing (1). Non-small cell lung cancer (NSCLC) is the most common type of lung cancer (accounting for 80% of all cases), and includes squamous cell carcinoma, adenocarcinoma and large cell carcinoma. Although surgical resection, radiation therapy and chemotherapy technology continue to improve gradually, patients with lung cancer remain exceedingly vulnerable to relapse and mortality (2). The global cure rate of lung cancer is low and the average 5-year survival rate is <15% (3–6). However, the mechanisms of NSCLC have not been elucidated, and hence the study of NSCLC is crucial.
Long-chain non-coding RNAs (long non-coding RNAs, lncRNAs) are RNA molecules with a transcript longer than 200 nucleotides in the nucleus or cytoplasm (7). LncRNAs are usually divided into five categories: Sense, antisense, bidirectional, introns and intergenic lncRNAs. In recent years, a large number of lncRNAs have been identified and a human lncRNA database providing details of lncRNA expression and other significant information has been established (8). Numerous studies have linked the lncRNAs with diseases, and abnormal expression has been noted in a range of diseases, including cancer (9,10).
Studies have demonstrated that lncRNAs are differentially expressed in normal cells and tumor cells, and since lncRNAs are a significant regulatory factor of gene expression, their aberrant expression will inevitably lead to abnormalities in gene expression and tumorigenesis. LncRNA disorders are also a feature of several types of cancer and promote the development, invasion and metastasis of tumors by a variety of mechanisms (9,11). LncRNAs regulate the transcriptional expression at the epigenetic, transcription and post-transcription levels (12–14).
Previous studies have demonstrated that lncRNAs are involved in the development and progression of NSCLC. However, research into lncRNAs in NSCLC is in its infancy and only a small number of NSCLC-associated lncRNAs have been identified, including lcRNA HOTAIR, lcRNA H19, lcRNA ANRIL, lcRNA MALAT1 (15,16) and lcRNA SCAL1 (17), lncRNA AK126698 (18) and lncRNA GAS6-AS1 (19). However, lncRNAs of NSCLC require further study to elucidate their mechanism of action.
In this study, we detected the lncRNA and mRNA expression patterns in NSCLC samples compared with corresponding adjacent normal tissue (NT) samples, several of which were evaluated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in a total of 90 pairs of tissues. The results revealed that lncRNA expression patterns may provide new molecular biomarkers for the diagnosis of NSCLC.
Materials and methods
Patient samples
NSCLC and corresponding NT samples were prospectively collected from 105 patients at The First Affiliated Hospital of Wenzhou Medical University, China, from April 2012 to August 2013. Samples from 15 of the patients were used for microarray analysis of lncRNAs and those from the remaining 90 were used for additional evaluations (Table I). The diagnosis of adenocarcinoma was confirmed by the histopathological results. The NSCLC and matched NT samples were snap-frozen in liquid nitrogen immediately after resection. The study was approved by the Institutional Ethics Review Board of The First Affiliated Hospital of Wenzhou Medical University, and all patients provided written informed consent for this study.
RNA extraction
NSCLC cells were obtained by laser microdissection; the proportion of cancer cells in the tissue sections was 100%. The 15 NSCLC specimens were divided into three groups; namely, every five samples from NSCLC were combined into a group. Next, 15 of the corresponding NT samples were mixed into one group. The four groups were subjected to RNA extraction. Total RNA was extracted using TRIzol reagent (Invitrogen Life Technologies, Carlsbad, CA, USA), according to the manufacturer’s instructions. The integrity of the RNA was assessed by electrophoresis on a denaturing agarose gel. An ND-1000 spectrophotometer (NanoDrop Technologies, Inc., Wilmington, DE, USA) was used for the accurate measurement of RNA concentration (OD260), protein contamination (OD260/OD280 ratio) and organic compound contamination (OD260/OD230 ratio).
Microarray and computational analysis
An Agilent array platform (Agilent Technologies, Inc., Santa Clara, CA, USA) was employed for microarray analysis. The sample preparation and microarray hybridization were performed according to the manufacturer’s instructions with minor modifications. Briefly, mRNA was purified from total RNA following the removal of rRNA using an mRNA-ONLY™ eukaryotic mRNA isolation kit (Epicentre Biotechnologies, Madison, WI, USA). Subsequently, each sample was amplified and transcribed into fluorescent cRNA along the entire length of the transcripts without 3′ bias using a random priming method. The labeled cRNAs were hybridized onto a Human lncRNA Array v3.0 (8×60 K; Arraystar, Rockville, MD, USA), designed for 30,586 lncRNAs and 26,109 coding transcripts. The lncRNAs were carefully constructed using the most highly respected public transcriptome databases, including Refseq (http://www.ncbi.nlm.nih.gov/refseq/), UCSC Known Genes (http://www.biomedsearch.com/nih/UCSC-Known-Genes/16500937.html) and GENCODE (http://www.gencodegenes.org/) as well as landmark publications (20–22). Each transcript was accurately identified by a specific exon or splice junction probe. Positive probes for housekeeping genes and negative probes were also printed onto the array for hybridization quality control. After washing the slides, the arrays were scanned using the G2505C scanner (Agilent Technologies, Inc.), and the acquired array images were analyzed with the Feature Extraction software (version 11.0.1.1, Agilent Technologies, Inc.). Quantile normalization and subsequent data processing were performed using the GeneSpring GX v12.0 software package (Agilent Technologies, Inc.). The microarray was performed by KangChen Bio-tech, Shanghai, China.
Functional group analysis
Gene ontology (GO) analysis was derived from Gene Ontology (www.geneontology.org), which provides three structured networks of defined terms that describe gene product attributes. The P-value denotes the significance of GO term enrichment in the differentially expressed mRNA list (P≤0.05 was considered to indicate a statistically significant difference). Pathway analysis was also carried out for the differentially expressed mRNAs based on the latest Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/) database. This analysis allowed us to determine the biological pathway for which a significant enrichment of differentially expressed mRNAs existed.
RT-qPCR
Total RNA was extracted from frozen NSCLC tissues with TRIzol reagent (Invitrogen Life Technologies) and then reverse transcribed using an RT reagent kit (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s instructions. LncRNA expression in NSCLC tissues was measured by qPCR using SYBR Premix Ex Taq (Thermo Fisher Scientific) with an ABI 7000 instrument (Applied Biosystems, Inc., Foster City, NJ, USA). Two lncRNAs that were significantly expressed (RP11-385J1.2 and TUBA4B) were evaluated in all of the patients included in this study. Total RNA (2 mg) was transcribed to cDNA. PCR was performed in a total reaction volume of 20 μl, including 10 μl SYBR Premix (2X), 2 μl cDNA template, 1 μl PCR forward primer (10 mM; (5′-TGTCAGACTCTCGGGACCAT-3′ for RP11-385J1.2 and 5′-AAAGTGCAACGTGCCATGTG-3′ for TUBA4B), 1 μl PCR reverse primer (10 mM; 5′-GATGCCACTGGAGTGTTGGA-3′ for RP11-385J1.2 and 5′-CTCCACACTATCCATGCCCA-3′ for TUBA4B) and 6 μl double-distilled water. The qPCR reaction was performed with an initial denaturation step of 10 min at 95°C, then 95°C (5 sec) and 60°C (30 sec) for a total of 40 cycles, with a final extension step at 72°C for 5 min. All experiments were performed in triplicate and all samples were normalized to GAPDH. The median in each triplicate was used to calculate the relative lncRNA concentrations (ΔCt = Ct median lncRNAs - Ct median GAPDH). The fold changes in expression were calculated (23).
Statistical methods
The Shapiro-Wilk test was used to evaluate the distribution. Comparisons between two groups were tested using the Mann-Whitney U test for non-normal distribution. The fold change and Student’s t-test were analyzed for statistical significance of the microarray results. The false discovery rate was calculated to correct the P-value. The threshold value used to designate differentially expressed lncRNAs and mRNAs was a fold change of ≥2.0 or ≤0.5. P<0.05 was considered to indicate a statistically significant difference. SPSS version 18.0 (SPSS Inc., Chicago, IL, USA) was used for statistical analysis.
Results
Overview of lncRNA expression profiles
To study the potential biological functions of lncRNAs in NSCLC, we examined the lncRNA and mRNA expression profiles in human NSCLC using microarray analysis (Fig. 1). For this analysis, authoritative data sources containing >30,586 lncRNAs were used. The expression profiles of 1,242 lncRNAs indicated that they were differentially expressed (fold change ≥2.0 or ≤0.5; P<0.05) between NSCLC and normal lung samples. Among these, 541 lncRNAs were observed to be upregulated >2-fold in the NSCLC group compared with the normal lung group, while 701 lncRNAs were downregulated >2-fold (P<0.05; Table II, Fig. 1A and B, Fig. 2A).
LncRNA classification and subgroup analysis
The expression profiles of 343 intergenic lncRNAs indicated that they were differentially expressed (fold change ≥2.0, P<0.05) between NSCLC and normal lung samples. Among these, 167 were upregulated and 176 were downregulated. Nearby coding genes that may be regulated by these lncRNAs were also identified (Table III). LncRNAs with enhancer-like function (lncRNA-a) were identified using GENCODE annotation. The expression profiles of 18 enhancer-like lncRNAs indicated that they were differentially expressed (fold change ≥2.0, P<0.05) between NSCLC and normal lung samples. Among these, seven were upregulated and 11 were downregulated. Nearby coding genes that may be regulated by these enhancer-like lncRNAs were also identified (Table IV). Hox lncRNAs (lncRNAs transcribed from Hox loci lncRNAs) profiles: This table contains 83 HoxlncRNA clusters (data not shown).
 | Table IIIUpregulated and downregulated long-chain non-coding RNAs (lncRNAs) in non-small cell lung cancer and nearby encoding genes regulated by lncRNAs. |
| Table IVEnhancer-like long-chain non-coding RNAs (lncRNAs) in non-small cell lung cancer and nearby encoding genes regulated by lncRNAs. |
Overview of mRNA expression profiles
In total, 1,102 mRNAs were noted to be differentially expressed between NSCLC and normal lung samples, including 271 upregulated mRNAs and 831 downregulated mRNAs (Fig. 1C and D, Fig. 2B).
GO analysis
The genes corresponding to the downregulated mRNAs included 278 genes involved in biological processes, 75 genes involved in cellular components and 59 genes involved in molecular functions. The genes corresponding to the upregulated mRNAs included 246 genes involved in biological processes, 58 genes involved in cellular components and 66 genes involved in molecular functions.
Pathway analysis
Eleven upregulated pathways were identified, including ethanol metabolism, systemic lupus erythematosus, transcriptional misregulation in cancer and cell cycle pathways (Table V). Twenty-eight downregulated pathways were identified, including malaria, African trypanosomiasis and allograft rejection (Table VI).
RT-qPCR validation
According to factors including the fold difference, gene locus and nearby encoding gene, we initially identified a number of significant candidate lncRNAs (including GUCY1B2, RP11-385J1.2, AC018865.8, RP11-909N17.3, GNAS-AS1, TUBA4B, Z82214.3, XLOC_000371, AC013264.2 and RP1-317E23.3) and verified the expression of these lncRNAs by RT-qPCR with GAPDH as the reference gene, by calculating the 2−ΔΔCT values. We observed that multiple lncRNAs in the microarray were consistent with the results of the RT-qPCR; see Fig. 3. RP11-385J1.2 and TUBA4B were the most markedly changed of these candidate lncRNAs from 90 NSCLC and normal lung tissue samples. As shown in Fig. 4, RP11-385J1.2 expression in NSCLC was significantly higher than in the adjacent tissues (Mann-Whitney U test=151.23, P=0.012), while TUBA4B expression in NSCLC was significantly lower than in the adjacent tissues (Mann-Whitney U test=168.43, P=0.007).
Discussion
According to the 2012 China Oncology Annual Report, the 2009 incidence and mortality of lung cancer was the highest among all cancers in male patients and the second highest among all cancers in female patients in China. LncRNAs play a significant role in a number of biological processes, including X-chromosome inactivation, gene imprinting and stem cell maintenance (24,25). It has been confirmed that lncRNAs are one of the most significant factors controlling gene expression in cancer (26). LncRNAs including HOTAIR have been shown to play a crucial role in the development and progression of tumors (9). It has also been demonstrated that lncRNAs are differentially expressed in normal and tumor cells (27,28). As lncRNAs constitute an essential class of gene expression regulatory factors, their aberrant expression would inevitably lead to abnormal gene expression levels, which may result in tumorigenesis.
In this study, we analyzed the lncRNA expression profile in the tissue of NSCLC patients to elucidate the potential role of lncRNAs in the pathogenesis of this disease. High-throughput microarray techniques revealed a set of differentially expressed lncRNAs, with 541 of those upregulated and 701 downregulated in NSCLC tissue compared with normal lung tissue. LncRNAs are usually divided into five categories: Sense, antisense, bidirectional, intronic and intergenic (29). LncRNAs are known to function via a variety of mechanisms; however, a common and significant function of lncRNAs is to alter the expression of nearby encoding genes by affecting the process of transcription (30) or directly playing an enhancer-like role (31,32). In the present study, we increased the accuracy of target prediction by comparing differentially expressed mRNAs with differentially expressed lncRNAs. The lncRNA expression profiles indicated that 343 lncRNAs were differentially expressed (167 upregulated and 176 downregulated) between NSCLC and normal lung samples. The expression profiles included 18 differentially expressed enhancer-like lncRNAs, with seven upregulated and 11 downregulated. Nearby coding genes that may be regulated by lncRNAs and enhancer-like lncRNAs were also identified. In addition, we performed HOX cluster profiling of lncRNAs and coding transcripts.
In order to obtain insights into lncRNA target gene function, GO analysis and KEGG pathway annotation were applied to the lncRNA target gene pool. GO analysis revealed that the number of genes corresponding to downregulated mRNAs was larger than that corresponding to upregulated mRNAs. KEGG annotation revealed 11 upregulated pathways (including ethanol metabolism, systemic lupus erythematosus, transcriptional misregulation in cancer and cell cycle pathways) and 28 downregulated pathways (including malaria, African trypanosomiasis and allograft rejection). These pathways may play significant roles in the occurrence and development of NSCLC. Ten lncRNAs identified in the microarray analysis were confirmed by RT-qPCR to be aberrantly expressed in NSCLC tissues. Among these lncRNAs, RP11-385J1.2 was the most markedly upregulated and TUBA4B was the most markedly downregulated. This result suggests that RP11-385J1.2 and TUBA4B may contribute to the development of NSCLC; further study of the biological function of RP11-385J1.2 and TUBA4B will be required to confirm this.
In conclusion, the present study revealed a set of lncRNAs with differential expression in NSCLC compared with normal lung tissue. Furthermore, it was demonstrated that RP11-385J1.2 and TUBA4B may contribute to the development of NSCLC. Further investigation of the lncRNAs identified in this study will likely provide insights into their biological functions and their association with NSCLC.
Acknowledgements
This study was financially supported by the National Natural Science Foundation of China (81401736 and 81271906) and Wenzhou Municipal Science and Technology Bureau, China (Y20110041 and Y20130170). The authors thank all donors who donated to the microarray service at KangChen Bio-tech, Shanghai, China.
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