The present study aimed to examine 10 housekeeping genes (HKGs), including 18s ribosomal RNA (18S), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), ribosomal protein large P0 (RPLP0), β-actin (ACTB), peptidylprolyl isomerase A (PPIA), phosphoglycerate kinase-1 (PGK1), β-2-microglobulin (B2M), ribosomal protein LI3a (RPL13A), hypoxanthine phosphoribosyl transferase-1 (HPRT1) and TATA box binding protein (TBP) in order to identify the most stable and suitable reference genes for use in expression studies in non-small cell lung cancer. The mRNA expression encoding the panel of the 10 HKGs was determined using reverse transcription-quantitative PCR (RT-qPCR) in human lung cancer cell lines. Three software programs, BestKeeper, NormFinder and geNorm, were used to ascertain the most suitable reference genes to normalize the RNA input. The present study examined three lung cancer cell lines (A549, NCI-H446 and NCI-H460). The analysis of the experimental data using BestKeeper software revealed that all 10 HKGs were stable, with GADPH, followed by 18S being the most stable genes and PPIA and HPRT1 being the least stable genes. The NormFinder software results demonstrated that PPIA followed by ACTB were the most stable and B2M and RPLP0 were the least stable. The geNorm software results revealed that ACTB and PGK1, followed by PPIA were the most stable genes and B2M and RPLP0 were identified as the least stable genes. Due to discrepancies in the ranking orders of the reference genes obtained by different analyzing software programs, it was not possible to determine a single universal reference gene. The suitability of selected reference genes requires unconditional validation prior to each study. Based on the three analyzing programs, ACTB, PPIA and PGK1 were the most stable reference genes in lung cancer cell lines.
Lung cancer is the most commonly diagnosed malignancy and is the leading cause of mortality among all types of cancer. Every year, lung cancer contributes to more than one million mortalities worldwide, among which non-small cell lung cancer (NSCLC) accounts for 85% of cases (1,2). NSCLC can be divided into three types, including adenocarcinoma, large cell lung carcinoma and squamous cell carcinoma (3). Each of these share a common set of carcinoma characteristics. Cell lines derived from each of the main lung tumor types are widely used as experimental models in lung cancer biology (4). Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) has revolutionized the field of gene expression analysis in living organisms (5). The main advantages of RT-qPCR are its superior specificity, sensitivity and broad quantification range (6,7). Despite being a useful technique, there are challenges coupled with its use, an important one being the normalization with an accurate and reliable reference gene, referred to as a housekeeping gene (HKG) (8,9). The term housekeeping gene was initially used to describe genes that are essential for cell function. Ideal HKGs are stably expressed in each cell type, do not respond to external stimuli and exhibit little or no run-to-run or sample-to-sample RT-qPCR variation. They are an internal reference to which target gene expression can be associated in order to correct unspecific variation caused by an imprecise amount of input RNA, RNA degradation or the presence of reaction inhibitors (8,10). Reference genes are often selected from the literature and are used across several experimental conditions, some of which may enhance the differences in the expression of a reference gene under certain conditions. Previous studies have indicated that certain commonly used HKGs, including β-actin (ACTB) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) are differentially expressed in various tissues (11–13). The reliability of normalized data is reliant on the robustness of reference genes. If unrecognized, unexpected changes in the expression of reference genes could result in flawed conclusions of real biological effects. Therefore, identification of stable and reliable reference genes is a prerequisite to any reliable analysis of RT-qPCR data. Numerous reference genes, including GADPH, ACTB, β-2-microglobulin (B2M) and ribosomal protein large P0 (RPLPO) have been identified, and their suitability for gene expression studies in diverse human tissue and cell types has been validated (14–17). RT-qPCR has been used in lung cancer studies to enumerate the expression of predictive and or prognostic targets (18). In the present study, three types of lung cancer cell lines (NCI-H A549, NCI-H446 and NCI-H460) were assembled and 10 common HKGs, including 18S, GAPDH, RPLP0, ACTB, peptidylprolyl isomerase A (PPIA), phosphoglycerate kinase-1 (PGK1), B2M, ribosomal protein LI3a (RPL13A), hypoxanthine phosphoribosyl transferase-1 (HPRT1) and TATA box binding protein (TBP) (Table I) were selected in order to examine their stability and suitability for RT-qPCR normalization in NSCLC using three common statistical algorithms, NormFinder, geNorm and BestKeeper. Candidate HKGs were selected on the basis of two criteria: i) their previous use as a reference gene and ii) their ability to cover a wide expression spectrum.
Materials and methodsCell lines
Human lung cancer cell lines A549 and NCI-H446 were purchased from the American Type Culture Collection (Manassas, VA, USA) and NCI-H460 was provided by the Central Gene Therapy Department of China-Japan Union Hospital, Jilin University (Changchun, Jilin, China). Cells were cultured in RPMI-1640 medium (Gibco-BRL, Carslbad, CA, USA), supplemented with 10% fetal bovine serum (Gibco-BRL) and 100 units of penicillin (Sigma-Aldrich, St. Louis, MO, USA), and maintained at 37°C in a 5% CO2 humidified atmosphere.
RNA extraction
The cell lines A549, NCI-H446 and NCI-H460 were cultured for 72 h, and total RNA was extracted from each cell using TRIzol reagent (Takara Bio, Inc., Shiga, Japan) according to the manufacturer’s instructions. Briefly, 1 ml of TRIzol reagent was used to homogenize the cells (~2×106). Samples were thoroughly mixed and incubated at room temperature for 5 min. The samples were then treated with 0.2 ml chloroform (Haodeng Industrial Co., Ltd, Shanghai, China) by reverse mixing. Phase separation was performed by placing the samples at room temperature for 5 min followed by centrifugation at 12,000 × g and 4°C for 15 min. The aqueous layer was mixed with 0.5 ml isopropanol (Haodeng Industrial Co., Ltd) to precipitate the RNA. Samples were placed at room temperature for 10 min and centrifuged at 12,000 × g and 4°C for 10 min. The RNA pellet was washed with 1 ml 75% alcohol and centrifuged at 10,000 × g and 4°C for 5 min. The pellet was air dried and resuspended with DNA/RNAase free water. The purity and concentration of RNA was determined using NanoDrop 1000 (Thermo Scientific, Waltham, MA, USA) spectrophotometry.
Complementary DNA (cDNA) synthesis
Total RNA (1 μg) from each cell group was reverse-transcribed to cDNA using a First Strand cDNA Synthesis kit (GeneCopoeia, Guangzhou, China) according to the manufacturer’s instructions. The cDNA was stored at −20°C.
Quantitative PCR
For RT-qPCR analysis, SYBR Green Premix EX Taq (Takara Bio, Inc.) was used in a reaction mixture that comprised 5 pmol of each gene-specific primer and 40 ng of cDNA sample, in a final volume of 20 μl. The primer sequences used (Table II) were synthesized by Sangon Biotech Co., Ltd. (Shanghai, China). RT-qPCR was performed using an AB Prism 7500 PCR detection system (Applied Biosystems, Foster City, CA, USA), under the following conditions: 30 sec of polymerase activation at 95°C followed by 40 cycles of denaturation at 95°C for 5 sec, annealing at 58°C for 60 sec and elongation at 72°C for 30 sec. Each assay was performed three times. The RT-PCR products were then subjected to 1% agarose gel electrophoresis containing ethidium bromide.
Statistical analysis
Data analysis was performed using ABI 7500 SDS system software (version 1.4; Applied Biosystems). All biological replicates were used to calculate the average threshold cycle (Ct) values. The stability of the 10 candidate reference genes was comprehensively evaluated using NormFinder (version 0953; http://moma.dk/normfinder-software) (16) algorithms, geNorm (version 3.4; http://medgen.ugent.be/~jvdesomp/genorm/) (19) and BestKeeper (version 1; http://www.gene-quantification.com/bestkeeper.html) (20,21). In order to enter the Ct values into geNorm and NormFinder software, the (Ct) values were converted into relative quantities using the following formula: 2−ΔCt (ΔCt=Ct-lowest Ct). The raw data was entered into the BestKeeper program and RT-qPCR efficiency was determined for each primer pair using slope analysis with a linear regression model. Relative standard curves for transcripts were performed with serial dilutions of cDNA at 1/2.5, 1/5, 1/10 and 1/20 ng. The corresponding RT-qPCR efficiencies (E) were calculated according to the following equation: E = 2 - 1/slope.
ResultsRNA purity and concentration
All RNA samples were examined for their purity and concentration. The absorbance ratio at 260/280 nm was 1.85–1.95 for each RNA sample group, reflecting high purity and concentration.
RT-qPCR efficiency of each primer pair
The RT-qPCR efficiency of each primer pair was determined by serial dilution. The results demonstrated that the efficiencies of the HKGs of interest ranged between 1.78 (HPRT1) and 2.74 (RPLP0) for each primer pair (Table II).
Candidate reference gene expression levels and ranges
In general, the 10 candidate reference genes revealed wide expression levels with mean Ct values in a range that is usually covered by HKGs, varying between 17.5 (ACTB) and 25.5 (TBP) among the three groups of lung cancer cell lines. 18S revealed the lowest variability of Ct among all groups of the three cell lines (Fig. 1). The dissociation curve of each target reference gene demonstrated one single peak, which confirmed the specific amplification of the target reference gene (Fig. 2A). The gel electrophoresis results demonstrated one single band which further confirmed the specific RT-qPCR amplification of the target reference gene (Fig. 2B).
Candidate HKG expression stability
The expression stability of each of the 10 reference genes was analyzed using three commonly used software programs, geNorm, NormFinder and BestKeeper.
geNorm analysis
The geNorm software program is an Excel based program that calculates and compares the gene expression stability measure (M) of all candidate genes, and excludes genes with an M-value >1.5. The lower the M value the higher the gene expression stability and repeats the calculations until there are two genes remaining. The M value indicates the average pairwise variation of a gene compared with all the other candidate genes. In order to determine the maximum number of genes necessary for adequate normalization in each panel of the experiment, geNorm determines pairwise variation (V) Vn/Vn + 1.V = 0.15 which is used as a cut-off value. A value <0.15 indicates the number of control genes that is sufficient for valid normalization (Fig. 3A). The results demonstrated that the M-value for each of the 10 reference genes was <1.5, thus there was no exclusion. The most stable genes were ACTB and PGK1, followed by PPIA, while the gene with the least expression stability was B2M followed by RPLP0 (Fig. 3A).
NormFinder analysis
The NormFinder software calculates the stability value based on an estimation of intra and intergroup variation for the analyzed genes. A low stability value has a low gene expression variance and indicates high stability expression. The output of this software analysis revealed that the most stable gene was PPIA followed by ACTB and PGK1. The least stable gene expression was B2M followed by RPLP0 (Fig. 4). These results were consistent with the geNorm analysis output.
BestKeeper analysis
BestKeeper is an excel based tool that assesses the stability of candidate HKGs based on the inspection of calculated variation, including the standard deviation (SD) (22) and the coefficient of variance values (Table III). According to the BestKeeper program, the lowest variations revealed the highest stability. Genes with an SD >1 are considered to have an unacceptable range of variation (Fig. 5A). The analysis demonstrated that all 10 candidate HKGs had an SD ≤1. GAPDH was the most stable, followed by 18S. HPRT1 was the least stable, followed by PPIA (Fig. 5B). The results from the BestKeeper software were therefore inconsistent with those of the geNorm and NormFinder software. A summary of the rankings produced by each of the three software programs is exhibited in Table IV.
Discussion
Lung cancer is the most common type of cancer and the most common cause of cancer-related mortality worldwide (23). NSCLC is a highly fatal disease with a poor prognosis and low survival rate (24). To increase the survival rate of patients with NSCLC, the disease must be diagnosed as early as possible. Lung tumor cell lines have been widely dispersed to and used in experimental studies, including DNA sequencing (25), microRNA and microarray analyses (26,27) and detection of genome-wide methylated sequences (28,29). Previous RT-qPCR has been demonstrated to be useful for early NSCLC diagnosis, prognosis, prediction and gene expression analysis (30). The use of RT-qPCR technology to study gene expression levels requires reliable normalization of data to avoid unspecific variability caused by the differences in cDNA quantity and/or quality, incorrect interpretation of experimental results and mistaken analyses. Although diverse methods are employed to normalize RT-qPCR, it remains one of the main challenges in the efficacy of this technique (31). The identification of internal control gene(s) is therefore essential for accurate quantification of target mRNA by RT-qPCR in a given set of experimental samples (32). Statistical software, including NormFinder, BestKeeper and geNorm has been developed to identify the stability of reference genes in a given set of biological samples. Several studies have used these software programs in the assessment of diverse HKGs to ascertain their suitability as reference genes for normalization of qPCR data (17,33). The present study examined the RNA transcription levels of 10 common housekeeping genes, including 18S, GAPDH, RPLP0, ACTB, PPIA, PGK1, B2M, RPL13A, HPRT1 and TBP (Table I) in the NSCLC cell lines NCI-H A549, NCI-H446 and NCI-H460. The three statistical softwares NormFinder, BestKeeper and geNorm (34) were used to assess the expression level stabilities of candidate reference genes. These programs use different calculation algorithms and therefore may provide different results (35,36). The present study demonstrated the following i) the purity and concentration of total RNA extracted from the abovementioned cell lines using TRIzol reagent; ii) the expression levels of the 10 reference genes determined in the above cell lines using qPCR and iii) the expression stability of the candidate reference genes in the above cell lines using geNorm, NormFinder and BestKeeper programs. In general, the present study demonstrated that almost all 10 candidate reference genes analyzed by the three independent programs could be used for future studies using lung cancer cell lines. This finding was somewhat in concordance with a previous study by Jacob et al (34). The analysis result of NormFinder was consistent with geNorm analysis output; both identified that ACTB, PGK1 and PPIA were the most stable reference genes. By contrast, B2M and RPLP0 were the least stable. BestKeeper analysis revealed that GAPDH, 18S and B2M were the most stable and RPLP0, PPIA and HPRT1 were the least stable reference genes. This was consistent with previous studies demonstrating that GADPH and 18S were the most stable reference genes in NSCLC (31,37). By contrast, another previous study using lung tissue samples demonstrated that GADPH and HPRT1 were the least stable reference genes (38). Variations obtained from these three programs were expected given their distinct statistical algorithms. NormFinder and geNorm use relative quantities transformed from Ct values for stability calculation whereas BestKeeper uses Ct values directly, which may explain the different outputs from these three software programs (38). Several previous studies on reference gene selection for lung cancer also identified discrepancies between these programs (20,37) and there was no agreement regarding which was the best method. Few experimental studies have analyzed the stability of potential reference genes in lung cancer cell lines. To the best of our knowledge, no previous study has analyzed 10 reference genes in lung cancer cell lines using three different statistical software programs. The present study concluded that ACTB, PPIA and PGK1 were the most stable reference genes analyzed by the three statistical programs geNorm, NormFinder and BestKeeper. These findings were somewhat inconsistent with those of previous studies and it was not possible to determine a single universal reference gene. Therefore, it is suggested that appropriate reference genes require selection on the basis of specific requirements and study conditions and in consideration of the characteristics of target genes in practical applications.
Acknowledgements
This study was supported in part by grants from the Scientific Research Foundation of Jilin Province (nos. 20100942, 20110740 and 2013727038YY) and a grant from the Scientific Research Foundation of Jilin Province Development and Reform Commission (no. 2013c014-4).
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Mean Ct values of the candidate reference genes among experimental samples. Ct, threshold cycle; ACTB, β-actin; GADPH, glyceraldehyde-3-phosphate dehydrogenase; RPL13A, ribosomal protein LI3a; PPIA, peptidylprolyl isomerase A; RPLP0, ribosomal protein large P0; B2M, β-2-microglobulin; PGK1, phosphoglycerate kinase-1; HPRT1, hypoxanthine phosphoribosyl transferase-1; TBP, TATA box binding protein.
Reverse transcription-quantitative polymerase chain reaction product of each of the 10 target reference genes. (A) Dissociation curves of each of the 10 target reference genes. (B) 1% agarose gel electrophoresis of amplified fragments. Bp, base pairs; ACTB, β-actin; GADPH, glyceraldehyde-3-phosphate dehydrogenase; RPL13A, ribosomal protein LI3a; PPIA, peptidylprolyl isomerase A; RPLP0, ribosomal protein large P0; B2M, β-2-microglobulin; PGK1, phosphoglycerate kinase-1; HPRT1, hypoxanthine phosphoribosyl transferase-1; TBP, TATA box binding protein.
Stability values of the candidate control genes analyzed by geNorm. (A) Determination of the optimal number of control genes. The software calculates the normalization factor from the least two stable genes at which the variable defines the pairwise variation between two sequential normalization factors. (B) M value of the 10 candidate reference genes analyzed by geNorm software. The x-axis from left to right indicates the ranking of the genes according to their stability; lower M values indicate higher stability. M, gene expression stability measure; V, variation; ACTB, β-actin; GADPH, glyceraldehyde-3-phosphate dehydrogenase; RPL13A, ribosomal protein LI3a; PPIA, peptidylprolyl isomerase A; RPLP0, ribosomal protein large P0; B2M, β-2-microglobulin; PGK1, phosphoglycerate kinase-1; HPRT1, hypoxanthine phosphoribosyl transferase-1; TBP, TATA box binding protein.
Stability values of the candidate reference genes calculated by NormFinder software. The lower stability values indicate higher stability. ACTB, β-actin; GADPH, glyceraldehyde-3-phosphate dehydrogenase; RPL13A, ribosomal protein LI3a; PPIA. peptidylprolyl isomerase A; RPLP0, ribosomal protein large P0; B2M, β-2-microglobulin; PGK1, phosphoglycerate kinase-1; HPRT1, hypoxanthine phosphoribosyl transferase-1; TBP, TATA box binding protein.
BestKeeper results for candidate reference genes. (A) SD was plotted on the y-axis. Genes with an SD >1.00 were excluded from further analysis. (B) Coefficient of correlation was plotted on the y-axis. A higher coefficient of correlation indicates a more stably expressed gene. SD, standard deviation; ACTB, β-actin; GADPH, glyceraldehyde-3-phosphate dehydrogenase; RPL13A, ribosomal protein LI3a; PPIA, peptidylprolyl isomerase A; RPLP0, ribosomal protein large P0; B2M, β-2-microglobulin; PGK1, phosphoglycerate kinase-1; HPRT1, hypoxanthine phosphoribosyl transferase-1; TBP, TATA box binding protein.
Candidate reference genes and their respective symbols and functions used in the present study.
Symbol
Name
Function
18S
18s ribosomal RNA
Component of the 40s ribosomal subunit
GAPDH
Glyceraldehyde-3-phosphate dehydrogenase
Oxidoreductase in glycolysis and gluconeogenesis
RPLP0
Ribosomal protein large P0
Component of the 60s ribosomal subunit
ACTB
β-actin
Protein involved in various types of cell motility
PPIA
Peptidylprolyl isomerase A, cydophilin A, romatase A
Accelerates the folding of proteins, catalyzes the cis-tans isomerization of proline imidic peptide bonds in oligopeptides
PGK1
Phosphoglycerate kinase-1
Enzyme involved in glycolysis
B2M
β-2-microglobulin
Component of major histocompatibility complex class 1 molecules
RPL13A
Ribosomal protein LI3a
Structural component of the large 60S ribosomal subunit
HPRT1
Hypoxanthine phosphoribosyl transferase-1
Enzyme involved in purine synthesis in salvage pathway
TBP
TATA box binding protein
General RNA polymerase 11 transcription factor
Primer sequences, product sizes and PCR efficiencies of candidate reference genes.