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  • Research article
  • Open Access

A strategy to identify housekeeping genes suitable for analysis in breast cancer diseases

  • 1,
  • 2,
  • 3, 4 and
  • 1Email author
BMC Genomics201617:639

https://doi.org/10.1186/s12864-016-2946-1

  • Received: 14 July 2015
  • Accepted: 18 July 2016
  • Published:

Abstract

Background

The selection of suitable internal control genes is crucial for proper interpretation of real-time PCR data. Here we outline a strategy to identify housekeeping genes that could serve as suitable internal control for comparative analyses of gene expression data in breast cancer cell lines and tissues obtained by high throughput sequencing and quantitative real-time PCR (qRT-PCR).

Methods

The strategy proposed includes the large-scale screening of potential candidate reference genes from RNA-seq data as well as their validation by qRT-PCR, and careful examination of reference data from the International Cancer Genome Consortium, The Cancer Genome Atlas and Gene Expression Omnibus repositories.

Results

The identified set of reference genes, also called novel housekeeping genes that includes CCSER2, SYMPK, ANKRD17 and PUM1, proved to be less variable and thus potentially more accurate for research and clinical analyses of breast cell lines and tissue samples compared to the traditional housekeeping genes used to this end.

Discussion

These results highlight the importance of a massive evaluation of housekeeping genes for their relevance as internal control for optimized intra- and inter-assay comparison of gene expression.

Conclusion

We developed a strategy to identify and evaluate the significance of housekeeping genes as internal control for the intra- and inter-assay comparison of gene expression in breast cancer that could be applied to other tumor types and diseases.

Keywords

  • Breast Cancer
  • High Throughput Sequencing
  • Breast Cell Line
  • High Throughput Sequencing Data
  • Bench Experiment

Background

As is well characterized at the cellular level, one of the main features of cancer intrinsically involves complex signaling pathways [1]. The identification of dysregulated genes involved in the carcinogenesis and tumor progression as well as their control poses challenges that mobilize the cancer research community worldwide. High-throughput technologies now allow genome-wide expression profiling, which is already providing important insights into complex regulatory networks, enabling the identification of new or under-explored biological processes, and helping to uncover the genes that are involved in various pathological processes as is the case with cancer [2, 3]. Highly sensitive investigative transcriptome profiling is now carried out by high throughput sequencing (HTS). However, because of reduced cost, clinical diagnoses rely on a set of target genes (demonstrated to be relevant in the case analyzed in a previous investigative step) and, thus, involve quantitative Real-Time RT-PCR (qRT-PCR) or AmpliSeq [4]. In this context, qRT-PCR has already been incorporated into clinical and translational science practice as a result of redefining the classification criteria of breast tumor diagnosis and prognosis by the incorporation of molecular factors in state-of-the-art protocols [58]. The successful transfer of knowledge from basic research to clinical diagnosis necessarily involves the demonstration that the results obtained with the latter are statistically consistent with those obtained with the former.

Statistical consistency involves experimental reproducibility and, from a general viewpoint, reproducibility is an absolute prerequisite for reliable inference, especially when investigating the biological significance of subtle differences in gene expression [9]. Experimental reproducibility is generally linked to the concept of robustness that is understood as the stability of a system output (here, the gene expression) with respect to stochastic perturbations. When comparing data from one transcriptome profile to another, one performs normalization of gene expression at the level of sequence and sample sizes. The process of normalization itself increases the robustness of an inference drawn from an experiment because it decreases intra- and inter-sample variances. Cancer is a multifactorial disease whose dimensionality (understood in terms of the relevant parameter space) may vary in time and space. Thus, internal controls with the highest possible robustness of gene expression are necessary to compare independent experiments and to maximize the confidence of inferences drawn from independent assays. In terms of gene expression, the genes with the highest level of expression stability (or expression robustness) over time and space are called housekeeping genes (HKG), simply because these genes perform functions that are essential to any cells in any states. The main concept associated with HKGs when dealing with transcriptome profiling is the notion that their expression level should not: (i) be affected under pathological conditions, (ii) differ between tissues and cell types, and (iii) be altered in response to experimental treatments. As a consequence, HKGs are generally regarded as the best gene candidates for internal controls when comparing transcriptome profiles obtained independently. Thus, the choice of HKGs is essential to the success of the experiment performed, especially when transcriptome profiling is carried out on the basis of high throughput sequencing, where any differences of gene expression may have significant meaning according to the expression robustness of reference genes (the HKGs) [1013].

In a previous study, we described a strategy for the selection of protein targets suitable for drug development against neoplastic diseases taking the case of breast cancer (BC) as a particularly pertinent example [14]. We extracted the sub-networks of down- and up-regulated human genes by comparing malignant and control cell lines and identified proteins that act as connectivity hubs representing suitable targets for disease control in terms of pharmacological agents. Surprisingly, this analysis revealed that the most frequently used traditional HKGs (tHKGs) such as GAPDH, ACTB and TUBA1A appeared significantly altered in their expression level from one sample to the other, which raises significant concerns regarding their uses as internal controls. To address this issue, we propose a strategy to identify potential novel HKGs (nHKGs) and also to validate tHKGs that may serve as internal controls in BC investigations based on HTS and qRT-PCR. First, we identified the genes with the highest level of expression stability in transcriptome data, and second, we confirmed that these genes were effectively the most stably expressed in qRT-PCR experiments of mRNA extracted from axenic cultures of the same cell lines. In cancer research, only a few studies attempted to investigate the variation of HKGs’ expression rates over different tissues and samples. Here, we used transcriptome and microarray data available from the ICGC consortium, TCGA and GEO to assess nHKG and tHKG candidates over different breast cancer tissue samples. We identified CCSER2, SYMPK, ANKRD17 and PUM1 as the top-four best candidates of HKGs for BC.

Methods

Interactome data

The protein connectivity inferences described below are based on the protein interactions given in the file intact-micluster.zip available from ftp://ftp.ebi.ac.uk/pub/databases/intact/current/psimitab/ (accessed on 04.04.2014) as described by Carels et al. [14]. Briefly, our resulting file contained 308,314 protein pairs. This interaction file was then processed to form a non-redundant list of Uniprot identifiers (UID) used to retrieve the corresponding protein sequences (68,504) by querying UniprotKB at http://www.uniprot.org/help/uniprotkb. The equivalence between UID and human genes was obtained by homology search (tBLASTn) of protein sequences (68,504) found as queries and human coding sequences (CDS) used as subjects from the dataset (hs37p1.EID.tar.gz) of Fedorov’s laboratory (available at http://bpg.utoledo.edu/~afedorov/lab/eid.html) [15]. Homologies were considered significant when their score was ≥120, E-value ≤10−4 and identity rate ≥80 % over ≥50 % of query size.

Transcriptome data

We recovered transcriptome datasets of breast cell lines (MCF10A, BT-20, BT-474, MDA-MB-231, MDA-MB-468, MCF-7, T-47D, ZR-75-1, see information at http://www.atcc.org/) from http://www.illumina.com/science/data_library.ilmn. We retrieved 433 transcriptome datasets relative to breast cancers from the ICGC portal. All raw data analyzed can be accessed and downloaded via the ICGC data portal (http://dcc.icgc.org/). The data samples were generated from patients that presented distinct histological subtypes, ages, tumor stages and sizes, grades and menoposal status, in order to perform a blind validation experiment. Additionally, we retrieved 95 paired transcriptome datasets relative to BC and their non-tumoral samples from TCGA (http://cancergenome.nih.gov/), considering Luminal A, Luminal B, Triple Negative and HER2+. The gene expression profiles for cell lines and tumors were assessed through a homology search with the human CDS sample of the Fedorov laboratory. The sequences from transcriptome tags were used as queries in searches for the best homologies (BLASTn) with human CDSs. The homology redundancy in the BLASTn output file gave us the tag count per gene i.e., a profile of human gene expression for each sample considered. Homologous hits were considered significant when covering at least 50 % of their size.

Each gene expression profile (tag count per gene) was normalized according to the CDS size and whole tag count using the formula (109*C)/(N*L), where 109 is a correction factor, C is the number of reads that match a gene, N is the total number of mappable tags in the experiment, and L is the CDS size [16]. When tags were counted for more than one gene isoform (alternative splicing forms), we cumulated counts and allocated them to just one form (the largest one); this strategy means that we looked for gene expression and not isoform expression. To allow the comparison between independent gene expression profiles, we further applied Quantil-normalization (Q-norm) [17]. The normalization of tag samples according to the CDS size and tag number is necessary to avoid values of gene expression that may differ from one sample to the other. The distribution of tag counts from transcriptome data is typically a decreasing curve where the lowest expressed genes are the most frequent ones. The size of the human transcriptome used was 4379 genes common to the eight cell lines investigated in our experiment.

Microarray analysis

We retrieved three microarray datasets of breast cancer (GSE9574, GSE20437 and GSE6434) from the Gene Expression Omnibus (GEO) repository (http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/geo/). GSE9574 includes 29 samples from histologically normal micro-dissected breast epithelium with 14 samples from epithelium adjacent to a breast tumor and 15 samples obtained from patients undergoing reduction mammoplasty without apparent breast cancer. GSE20437 includes 42 samples from laser capture micro-dissection (LCM) of normal breast tissue samples analyzed with the Affymetrix HU133A microarrays to show that histologically normal epithelium from breast cancer patients and cancer-free prophylactic mastectomy patients share a similar expression profile. Among these 42 samples (i) 36 were from the same age group with 18 from reduction mammoplasty and 18 from histologically normal epithelial samples of breast cancer patients from which 9 were ER+ and 9 ER- and (ii) 6 were histologically normal epithelial samples from prophylactic mastectomy patients. GSE6434 includes 24 BC patients sensitive or resistant to docetaxel that were analyzed with the Affymetrix Human Genome U95 Version 2 Array.

HKGs

We selected 10 tHKGs among the genes most commonly used as internal control in expression experiments to evaluate their expression variance by HTS and qRT-PCR (see Table 1 for gene name, uniprotkb, function; and see Additional file 1: Table S1 for primer sequence). The strategy used to identify nHKGs is outlined in Fig. 1. We searched for candidate HKGs whose expression was detected in the transcriptomes of eight cell lines (MCF10A, BT-20, BT-474, MDA-MB-231, MDA-MB-468, MCF-7, T-47D, ZR-75-1) using 0’s proportion in transcriptome datasets. 0’s proportion is defined as the proportion of different cell lines in which a given gene is not expressed and was calculated as follows:
Table 1

Features of nHKGs and tHKGs

Uniprotkb

Protein name

Gene

Mean

CV (%)

Biological process

nHKGs

 Q92575

UBX domain-containing protein 4

UBXN4

81.75

11.45

Response to unfolded protein

 Q08211

ATP-dependent RNA helicase A

DHX9

158.25

12.42

ATP catabolic process, DNA duplex unwinding

 P17152

Transmembrane protein 11, mitochondrial

TMEM11

68.12

12.83

Mitochondrion organization

 Q6PKG0

La-related protein 1

LARP1

137.87

13.10

Cell proliferation

 Q13190

Syntaxin-5

STX5

65.37

13.33

Vesicle transport

 O75179

Ankyrin repeat domain-containing protein 17

ANKRD17

33.62

14.03

Blood vessel maturation

 Q92797

Symplekin

SYMPK

60.75

14.29

Cell adhesion

 Q6P1X5

Transcription initiation factor TFIID subunit 2

TAF2

23.87

14.41

G2/M transition of mitotic cell cycle

 Q9H7U1

Serine-rich coiled-coil domain-containing protein 2

CCSER2

20.00

15.12

Microtubule bundle formation

 Q6NZ67

Mitotic-spindle organizing protein 2B

MZT2B

179.62

15.69

tHKGs

 Q14671

Pumilio homolog 1

PUM1

45.50

24.56

Vesicle-transport, translation

 P40429

60S ribosomal protein L13a

RPL13A

3028.62

25.51

Translation

 P00558

Phosphoglycerate kinase 1

PGK1

488.37

30.91

Glycolysis

 P08236

Beta-glucuronidase

GUSB

70.00

36.56

Metabolic processes

 P60709

Actin, cytoplasmic 1

ACTB

9500.25

37.06

Protein folding, chromatin remodeling

 Q9UNQ2

Probable dimethyladenosine transferase

DIMT1

26.62

41.82

rRNA processing

 Q71U36

Tubulin alpha-1A chain

TUBA1A

658.00

47.50

Protein folding, G2/M transition of cell cycle

 P04406

Glyceraldehyde-3-phosphate dehydrogenase

GAPDH

3580.12

66.79

Metabolic process, protein folding

 P61769

Beta-2-microglobulin

B2M

1530.12

93.47

Immunity

Fig. 1
Fig. 1

Flowchart of a novel identification strategy for housekeeping genes (nHKGs) in breast cancer. A list of 7780 genes was first obtained by selecting genes with low 0’s proportion and coefficient of variation (CV). 0’s proportion is defined as the proportion of different cell lines in which a given gene is not expressed; and CV is the ratio of the standard variation to the mean. Among the nHKG candidates, 10 with the lowest CVs were further validated by qRT-PCR and for their 0’s proportion in a large human tissue dataset from ICGC. For the sake of comparison, we included traditional housekeeping genes (tHKGs) selected from the literature, i.e., PUM1, RPL13A, PGK1, GUSB, ACTB, DIMT1, TUBA1A, GAPDH, B2M, 18S

$$ 0'\mathrm{s}\ \mathrm{proportion}=\frac{\mathrm{Number}\ \mathrm{of}\ \mathrm{cell}\ \mathrm{lines}\ \mathrm{not}\ \mathrm{expressing}\ \mathrm{the}\ \mathrm{gene}}{\mathrm{Total}\ \mathrm{number}\ \mathrm{of}\ \mathrm{cell}\ \mathrm{lines}} $$
(1)

A 0 value for the 0’s proportion indicates that the gene is expressed in the eight cell lines, and a value between 0 and 1 indicates that the gene is not expressed in at least one cell line.

To select nHKGs, we (i) eliminated the genes that were not-expressed in all cell lines (0’s proportion =1) from the list; (ii) evaluated the coefficient of variation (CV) for each gene, which is the ratio of the standard variation and the mean; (iii) further filtered out potential nHKGs by keeping the 10 genes with the lowest CV among the 4379 genes common to the eight cell line transcriptomes (tumoral and non-tumoral cell lines).

In order to annotate HKGs, we searched for their homologies with nr (GenBank, rel 181) using the BLAST to gene ontology - Blast2GO [18]. We also looked for the most common transcription factors (TFs) involved in BC signaling pathways that could regulate HKG expression by searching the literature, and selected the following ones: AP1, NFKB, GATA3, FOXA1, ER, Elk1, STAT3, STAT5, HIF, NOTCH, SP1, TP53, MYC [19]. In order to crosscheck the information available as far as possible, we also compared our data with three reference databases: (i) STRING (http://string-db.org/), which includes direct and indirect associations derived from four sources: genomic context, high-throughput experiments, (conserved) co-expression and previous knowledge, (ii) CCSB interactome (http://interactome.dfci.harvard.edu/) and (iii) cancer-systemsbiology (http://www.cancer-systemsbiology.org/). In order to determine the degree of interdependence associated to HKGs, we graphically analyzed their sub-networks formed with TFs in the GEPHI (http://gephi.github.io/) environment by pasting data in the input node file and using the toolbox of this program to automatically calculate and represent protein connectivity (i.e., the relative number of edges per node).

Cell culture, cDNA preparation and qRT-PCR

To validate our in silico inferences, we used four breast tumoral cell lines: MCF-7 (Luminal A), T47D (Luminal A), MDA-MB-231 (Triple Negative), MDA-MB-468 (Triple Negative), and a non-tumoral breast cell line, MCF-10A. All cell lines were cultured in standard conditions as recommended by ATCC, supplemented with 10 % fetal bovine serum (FBS), 100 IU/ml penicillin and 100 mg/ml streptomycin in a humidified environment containing 5 % CO2 at 37 °C.

We isolated total RNA from breast cell lines using a PureLink RNA Mini Kit (Ambion) according to the manufacturer’s instructions. Total RNA was eluted in 40 μl of RNase-free H2O and stored at −80 °C. Extracted RNAs were quantified using NanoDrop ND-1000 (NanoDrop Technologies) and the absorbance ratios at 260/280 and 260/230 were measured to assess RNA purity. The ratios of optical densities (OD) at 260 vs. 280 nm (260/280) were between 1.8 and 2.0 for all samples. First-strand cDNA synthesis was carried out with 1 μg total RNA using oligo(dT) primers and Superscript II reverse transcriptase (Invitrogen Life Technologies) following manufacturer’s instructions. PCR assays were performed using the primers listed in Additional file 1: Table S1. All oligonucleotides were analyzed for potential secondary structure and dimerization using OligoAnalyzer 3.1. qRT-PCR was performed on a StepOne Plus System (Applied Biosystems) using Power SYBR Green PCR Master Mix (Applied Biosystems). PCR was done using the following protocol: 50 °C for 2 min, initial denaturation 94 °C for 5 min, then 40 cycles at 94 °C for 30 s, 60 °C for 30 s, 72 °C for 45 s; and 72 °C for 15 min. To verify that the used primer pair produced only a single product, a DNA melting curve analysis was added after thermocycling, determining dissociation of the PCR products from 60 to 90 °C (with a heating rate of 0.2 °C and continuous fluorescence measurement). The amplification efficiency of each set of oligonucleotides was determined by plotting the cycle threshold (Ct) values obtained for four cDNA dilutions (1:100, 1:200, 1:400, 1:800) (Additional file 2: Figure S1).

Results

Identification of HKGs from transcriptome data

Table 1 shows the list of top-10 candidates of nHKGs obtained from the analysis of the eight breast cell lines selected. Among genes with low CV (%) values across breast cell lines, some may have either a low or a large average expression level. Because of their ease of detection, the HKGs with large average expression levels are suitable for gene expression characterization by RT-PCR, microarrays and/or HTS. The top-10 nHKGs (DHX9, MZT2B, UBXN4, LARP1, TAF2, CCSER2, STX5, SYMPK, TMEM11 and ANKDR17) with the smallest expression variability identified here have not been used yet as internal control in expression experiments and have independent functions in cellular maintenance (Table 1). Interestingly, GAPDH, ACTB and TUBA1A, the most commonly reported reference genes for comparative expression experiments, did not meet the parameters applied by us for the selection of nHKGs. However, for the sake of comparison, we included the nine tHKGs most commonly found in the literature (PUM1, RPL13A, PGK1, GUSB, ACTB, DIMT1, TUBA1A, GAPDH and B2M). The tHKGs did not belong to the list of top-100 genes with the lowest coefficient of variation (the standard deviation over the average of a random variable) of gene expression.

The average expression level of nHKGs is 82.92 (for a range of 20.00 to 179.62), and that of tHKGs is 2323.87 (range of 26.62 to 9500.25) (Fig. 2a and Table 1). The average expression of nHKGs was an order of magnitude lower than that of tHKGs. Figure 2b shows the CV for each gene over the eight breast cell lines. tHKGs shows a ~10 time larger CV (44.91, on average) than nHKGs (3.67, on average), supporting the notion that nHKGs are generally expressed more stably and at lower levels than tHKGs.
Fig. 2
Fig. 2

Pattern and variation of candidate nHKG and tHKG expression in breast cell lines. a Comparison of gene expression between nHKG candidates and tHKGs across all cell lines. The x-axis represents the mean gene expression levels. Boxes delimit lower and upper quartiles while the vertical lines within the boxes indicate median expression values. Lateral whiskers provide lower and higher values as left and right ticks, respectively. b Housekeeping genes (nHKGs and tHKGs, x-axis) ordered by an increasing average of CVs for each gene over eight cell lines (y-axis) across breast cell lines

For the purpose of challenging the robustness of nHKG to expression variation, we tested the co-regulation between HKGs by examining the network of transcription factors involved in BC signaling pathways in nHKGs as well as in tHKGs (Fig. 3 and Additional file 3: Figure S2). The interpretation of resulting networks in the light of the version of human interactome that we used provides evidence that estrogen receptor (ER) directly regulates DHX9, LARP1, ACTB, GAPDH and RPL13A expression; whereas MYC regulates DIMT1, MZT2B and TAF2; TP53 regulates STX5; and AP1 regulates TUBA1A. We also found interactions between GAPDH, PGK1, ACTB and TUBA1A; and also between DHX9 and LARP1, indicating that these genes present regulation processes that share common routes. These interactions are also reported in STRING (Additional file 3: Figure S2), CCSB (Additional file 4: Table S2) and cancer-systemsbiology databases (Additional file 5: Table S3). By contrast, according to the common knowledge available at present, CCSER2, TMEM11, SYMPK, UBXN4 and ANKRD17 among nHKGs do not show cross interactions. Thus, CCSER2, TMEM11, SYMPK, UBXN4 and ANKRD17 represented the best candidates for potential nHKGs offering internal control in comparative expression assays. By assessing the relationship between the expression patterns of nHKGs and tHKGs using the STRING database, we found that, with the exception of DHX9 that exhibits positive correlation with UBXN4, all remaining nHKGs are independently expressed (Additional file 6: Figure S3A). Among tHKGs, GAPDH shares expression with PGK1, ACTB and TUBA1A and the expression of TUBA1A is directly associated with ACTB (Additional file 6: Figure S3B). Based on all these results taken together, we selected CCSER2, TMEM11, SYMPK, UBXN4 and ANKRD17 as a set of nHKGs for validation. These nHKGs include genes with the largest expression stability among the transcriptome data of our cell line sample as well as the absence of obvious co-regulation and co-expression with other genes. Additionally, we selected TUBA1A, GAPDH, ACTB, B2M and 18S as a set of tHKGs for comparison.
Fig. 3
Fig. 3

Subnetworks of nHKGs, tHKGs and transcription factors in Gephi. Nodes are for genes and links for interactions among them. Node size indicates connectivity grade. Red is for transcription factors, green for tHKGs, and blue for nHKGs

Evaluation of selected nHKGs and tHKGs by qRT-PCR

To validate inferences about nHKGs and tHKGs from HTS data, we next performed qRT-PCR analyses with the five nHKG candidates (CCSER2, SYMPK, TMEM11, UBXN4 and ANKDR17) for comparison with tHKG expression (ACTB, GAPDH, TUBA1A, B2M and 18S). In order to compare our results across cell lines and genes, we used the simplest and most direct method, the comparative CT method, which uses only raw values of threshold cycles CT. Since CT is inversely proportional to the gene expression, we compared gene expression according to 1/CT as shown in Fig. 4a. The amplification of cDNA with gene-specific primers from an independent set of breast cell lines (MCF-10A, MCF-7, MDA-MB-231, MDA-MB-468 and T47D) demonstrated better performance in terms of stability for nHKGs than tHKGs (Fig. 4a and Table 2). nHKGs showed nearly the same gene expression levels for each sample of the whole set (Fig. 4a). Based on qRT-PCR data, the average expression of nHKGs was 28.18 when calculating CTCT = 0.94) and 35.66 10−31/CT = 1.2 10−3) when calculating 1/CT (CT ranging from 29.23 to 26.82 or 1/CT ranging from 34 10−3 to 37 10−3) and that of tHKGs was 19.86 (σCT = 2.31) and 51.66 10−31/CT = 6.4 10−3) when calculating 1/CT (CT ranging from 23.15 to 16.07 or 1/CT ranging from 43 10−3 to 63 10−3). Furthermore, for HTS data, the average expression of nHKGs was lower than that of tHKGs. Additionally, we confirmed that the CV of nHKGs (6.63 on average for CT and 52.72 10−3 for 1/CT) was lower than that of tHKGs (9.74 on average for CT and 73.62 10−3 for 1/CT) (Fig. 4a and Table 2). All these results support our previous hypothesis that nHKGs are generally expressed more stably and at lower levels than tHKGs. By comparing the logarithm of average normalized read counts of RNA-seq data to the logarithm of 1/CT values of qRT-PCR for each gene, we observed a strong linear correlation (r = 0.963), which allows the transposition of results obtained with one technique to the other and vice versa (Fig. 4b).
Fig. 4
Fig. 4

Scatter plot of gene expression measured by real-time PCR and read counting in RNA-seq data. a Expression pattern of nHKG candidates and tHKGs in breast cell lines obtained by qRT-PCR across eight cell lines. Boxes delimit lower and upper quartiles while the vertical lines within the boxes indicate median expression values. Lateral whiskers provide lower and higher values as left and right ticks, respectively. The x-axis gives the mean of 1/CT for each gene. Results are representative of three independent experiments. b Correlation between qRT-PCR and read count from RNA-seq data for each gene across eight breast cell lines (r = 0963). The y-axis represents the average of CVs for each gene over eight cell lines

Table 2

Threshold cycle (CT): Values of average, standard deviation and coefficient of variation for tHKGs and nHKGs

CT

GAPDH

B2M

ACTB

TUBA1A

18S

CCSER2

UBXN4

SYMPK

TMEM11

ANKRD17

MCF-7

18.51

23.97

16.24

21.92

20.49

29.72

26.61

29.46

27.04

28.82

MDA-MB-231

16.45

20.80

13.84

20.22

19.61

27.47

24.83

27.35

24.69

27.12

MDA-MB-468

20.48

25.49

18.09

24.94

23.47

30.96

30.49

30.84

29.44

31.57

T47D

15.66

22.14

14.62

20.32

19.56

28.01

25.33

27.34

24.94

26.80

MCF10A

18.29

23.32

17.56

22.69

17.86

29.96

28.61

29.74

27.98

29.25

Minimum

15.67

20.81

13.85

20.23

17.87

27.47

24.84

27.35

24.69

26.81

Maximum

20.49

25.50

18.10

24.94

23.47

30.96

30.50

30.84

29.45

31.57

Mean

17.88

23.15

16.07

22.02

20.20

29.23

27.18

28.95

26.82

28.72

Std. Deviation

1.892

1.782

1.831

1.942

2.062

1.444

2.359

1.550

2.021

1.912

Coefficient of variation (%)

10.58

7.70

11.39

8.82

10.21

4.94

8.68

5.35

7.53

6.66

Good laboratory practice would recommend the use of more than one internal control for comparative analyses of gene expression to minimize the risk associated with accidental errors and to increase statistical consistency. Thus, we assessed the potential combination of all five genes based on qRT-PCR data. Correlation coefficients (r) were calculated, representing the relationship between the expression of each individual housekeeping gene and the mean expression of the remaining genes (Table 3). Clearly, all nHKGs showed a very high correlation coefficient, which means that we could alternatively choose and combine each one of nHKGs. By extension, if three nHKGs are used as internal controls, at least two should give similar levels of gene expression in order to provide confidence in the experimental results obtained.
Table 3

Correlation coefficients for the expression of each individual gene and the mean expression of the remaining four genes

 

CCSER2

UBXN4

SYMPK

TMEM11

ANKD17

MCF-7

29.72351

26.61776

29.46543

27.04637

28.82998

MDA-MB-231

27.4733

24.83932

27.3512

24.69488

27.12572

MDA-MB-468

30.9613

30.49971

30.84297

29.44699

31.57093

T47D

28.0175

25.33814

27.34727

24.94886

26.80775

MCF10A

29.96375

28.61279

29.74568

27.98631

29.25333

Correlation

0.976768

0.969011

0.983436

0.99814

0.977064

Validation of nHKGs and tHKGs in large breast cancer tissue datasets from ICGC, TCGA and GEO

We obtained the transcriptome expression patterns of 433 tissue samples associated with breast cancer from the ICGC consortium, 95 paired tissue samples from TCGA, and three distinct microarray datasets from GEO and successively screened these data for nHKGs and tHKGs validation. This assay presented three main goals: (i) validation of nHKGs for use in clinical conditions, (ii) generalization of the nHKG and tHKG expression data obtained with malignant breast cell lines to human breast tumors, and (iii) assessment of tHKGs expression variability in malignant tissues of human breast.

The expression levels of DHX9, LARP1, TAF2, CCSER2, SYMPK, and ANKDR17 (nHKGs) were measurable in all 433 samples, while we did not observe expression of MZT2B, UBXN4, STX5 and TMEM11 in all samples. These results mean that among the 5 core nHKGs, we identified CCSER2, SYMPK and ANKDR17 as the best nHKG candidates. It is worth stressing that among tHKGs, DIMT1, TUBA1A and B2M were not expressed in all samples. However, PUM1 appeared to be the best tHKG candidate for use as internal controls in BC investigations. Comparison of the mean expression levels, standard deviations and CV of tissue sample sets revealed that nHKGs showed lower values than the commonly used housekeeping genes (Fig. 5). The average expression of nHKGs by RNA-seq read count was 24.97 and that of tHKGs was 247.66 (Table 4). With respect to expression variation, most of the tHKGs showed relatively higher variation levels than the majority of nHKGs and the mean CV values of nHKGs (55.94, on average) was ~3 times lower than those of tHKGs (161.39, on average) (Table 4). Similar results were also observed when we analyzed TCGA and microarray data (Additional file 7: Figure S4 and Additional file 8: Figure S5). All these data support the hypothesis that nHKGs are generally expressed more stably and at lower levels than tHKGs. The results of our data analysis identified CCSER2, SYMPK, ANKDR17 and PUM1 as suitable reference genes for bench experiments of gene expression since they showed low variation, but persistent expression across individual tissues and over large-scale sampling. Given that the expression stability of nHKGs was consistently better than that for the majority of tHKGs, one may conclude here that nHKGs are better internal controls than tHKGs to report on disease and/or tissue-specific effects on the basis of molecular investigations. Our results indicate CCSER2, SYMPK, ANKRD17 and PUM1 to be the best HKG candidates for clinical and in vitro investigations in BC.
Fig. 5
Fig. 5

Distribution of expression levels by read counts of 10 nHKGs and 9 tHKGs in breast cancer RNA-seq (n = 433) from ICGC. Boxes delimit lower and upper quartiles while the vertical lines within the boxes indicate median expression values. Lateral whiskers provide lower and higher values as left and right ticks, respectively

Table 4

Expression level, average, standard deviation, median and coefficient of variation values of nHKGs and tHKGs in a large data set of breast cancer tumors (n = 433) from ICGC

 

Minimum

25 % percentile

Median

75 % percentile

Maximum

Mean

Std. deviation

Coefficient of variation (%)

DHX9

16.00

27.00

36.00

47.50

97.00

39.22

14.56

37.14

MZT2B

0.0

2.000

7.000

12.00

39.00

7.896

6.834

86.55

UBXN4

0.0

15.00

21.00

30.50

95.00

23.94

12.93

54.04

ANKRD17

3.000

15.00

27.00

40.00

60.00

27.55

13.88

50.37

LARP1

1.000

12.00

26.00

35.00

61.00

25.53

13.92

54.54

TAF2

2.000

15.00

23.00

34.00

53.00

23.94

11.95

49.94

CCSER2

2.000

17.50

24.00

34.00

58.00

25.58

11.64

45.50

STX5

0.0

14.00

33.00

44.50

95.00

32.82

21.33

65.00

TMEM11

0.0

7.000

11.00

18.00

108.0

14.10

12.07

85.62

SYMPK

2.000

9.000

16.00

24.00

58.00

17.40

10.58

60.80

PUM1

4.000

15.00

20.00

29.50

76.00

22.63

11.32

50.03

DIMT1

0.0

16.00

27.00

35.00

85.00

27.93

15.46

55.38

ACTB

7.000

37.50

59.00

102.5

1232

87.07

108.7

124.8

GUSB

11.00

29.00

45.00

64.00

177.0

53.06

32.19

60.67

GAPDH

9.000

19.00

39.00

157.0

437.0

97.21

98.85

101.6

TUBA1A

0.0

7.000

13.00

44.00

158.0

26.07

24.98

95.80

B2M

0.0

13.00

23.00

37.00

26248

608.1

3264

536.73

RPL13A

12.00

34.00

73.00

185.0

26248

1094

4564

417.25

PGK1

4.000

42.00

52.00

71.50

146.0

59.78

29.30

49.02

Discussion

Despite the considerable progress in high-throughput technologies, a rational method design to identify HKGs has not been achieved yet. Until now, no fully effective reference HKGs have been proposed for comparative analyses of gene expression in the context of complex diseases, such as cancer, neurological, autoimmune, cardiovascular and metabolic diseases. Such lack of critical assessment can promote biases in the conclusions drawn from these investigations. Thus, we believe that the strategy that we outlined here is relevant for the identification of suitable HKGs as internal control for bench experiments on gene expression in BC, and should be explored for other neoplasias and diseases.

Our findings illustrate the importance of minimizing any sources of bias and suggest the importance of critically assessing the performance of the HKGs used as internal controls in each case studied. We used transcriptome data to select genes with low variability in expression levels across breast cell lines. Our large-scale dataset samples were filtered out to identify genes with the largest expression stability across breast cell lines. Further screening including the elimination of candidate genes with obvious co-regulation, co-expression and/or similar biological function was successfully added to the protocol. HKGs distributed within different functional classes significantly reduce the chance of genes co-regulation. All these criteria taken together increase the likelihood of independent expression of candidate HKGs and decrease the likelihood of expression alterations in the context of complex networks such as those found in cancer diseases.

Clearly, the use of nHKGs is expected to improve the robustness likelihood of bench experiments aimed to validate bioinformatic inferences in the context of BC for in vitro models. We demonstrated a very high correlation level (r = 0.963) between expression levels obtained from RNA-seq data (Illumina sequencing) and qRT-PCR using the same cell lines despite being cultured in a different place, at a different time, on different media and from independent sources; a set of modifications that represents a huge source of potential variability. The high correlation level and the almost perfect match with the linear regression of RNA-seq and qRT-PCR data gives a simple mean for direct result extrapolation from one result to another. As a consequence, a real possibility exists to translate the expression data of investigative RNA-seq into diagnosis at a clinical level by using qRT-PCR or AmpliSeq. Such a high level of robustness of gene expression on a multidimensional scale suggests that CCSER2, ANKRD17 and SYMPK are suitable nHKGs as well as the tHKG PUM1 for fine comparative analyses of gene expression by HTS and qRT-PCR.

Most of the tHKGs selected here have been indiscriminately used by a number of scientists worldwide and are available commercially as standard kits. Typically, these kits focus on a specific pathway and include a panel of genes relevant to that specific pathway or disease state. For example, the cancer-pathway kit from Qiagen array includes: B2M, HPRT1, RPL13A, GAPDH, ACTB while that of Life technologies array includes: CDKN1B, G6PD, POLR2A, IPO8, CASC3, YWHAZ, CDKN1A, UBE2D2, HMBS, UBC,TP5B, HPRT1, CUL1, 18S, RPLP0, ACTB, PPIA, GAPDH, PGK1, B2M, GUSB, HPRT1, TBP, TFRC. On the other hand, ACTB, GAPDH, RPLP0, GUSB and TFRC form a set of reference genes included in a commercial Oncotype DX test. This test was supported by the National Comprehensive Cancer Center Network (NCCN) and the American Society of Clinical Oncology (ASCO) in their treatment guidelines [20] in order to calculate a recurrence risk score for each patient. Here, we have shown that most of these genes are not stably express across breast cell lines. As a result, in a large subset of human tissues, the introduction of these genes as reference HKGs is expected to promote noise in the assessment of expression levels from other genes. As a matter of fact, this situation can be expected since tHKGs have a higher level of connection with other genes, such as TFs for example, than nHKGs.

Astounding discrepancies can be found in the data from the literature when considering the most frequently used tHKGs in qRT-PCR as internal controls. Révillion et al. [21] showed an association of GAPDH expression with BC cell proliferation and with the aggressiveness of tumors. Ahmad et al. [22] demonstrated phosphoglycerate kinase 1 (PGK1) as a promoter of metastasis in colon cancer. Hence, PGK1 is a promoting enzyme for peritoneal dissemination in gastric cancer [23]. McNeill et al. [24] showed alterations in GUSB expression in breast cancer. Stromal myofibroblasts in invasive breast cancer expression of alpha-smooth muscle actin (α-SMA) correlate with worse clinical outcomes [25] and the metastasis group showed significantly higher α-SMA expression compared with the non-metastasis group. Loss of α-tubulin was significantly correlated with distant metastases [26]. B2M expression demonstrated a significant difference in the breast cancer molecular subtypes, and may be related to apoptosis regulation in breast cancer [27].

The expression pattern of each nHKG selected here accurately reflected the mean expression pattern of the others. This demonstrates that the expression of each single nHKG is expected to be similar to the other four nHKGs, which is an important point in relation to the use of more than one HKG to normalize each assay and increase the assessment consistency. A universal internal control based on only one ideal HKG may not exist, thus we recommend to normalize bench experiments with a panel of HKGs whose expression has been proven to be as minimally variable as possible and the most robust as possible regarding variation under experimental conditions. In order to warrant robustness, the average of nHKG expression in one experiment should serve as internal control for comparison among experiments.

Conclusions

In summary, we have modeled the performance of candidate HKGs to test their goodness-of-fit in serving as internal controls for comparative analysis of gene expression through HTS and qRT-PCR. A major advantage of a model approach is that the genes are placed within a robust bioinformatics and bench framework, which allows the strategy to be generalized to a variety of different diseases and cancer types.

Declarations

Acknowledgements

This research was supported by a fellowship from CAPES-Fiocruz (cooperation term 001/2012 CAPES-Fiocruz) to T. M. Tilli, the National Institute for Science and Technology on Innovation on Neglected Diseases (INCT/IDN, CNPq, 573642/2008-7), the Canadian Breast Cancer Foundation, the Allard Foundation and the Alberta Cancer Foundation. We thank Dr Robson Monteiro of the Institute of Medical Biochemistry, Federal University of Rio de Janeiro, RJ, Brazil for help and space allocation in his laboratory. We thank Dr. Maria Isabel Doria Rossi of University Hospital of Clementino Fraga Filho, Federal University of Rio de Janeiro, RJ, Brazil for supplying the cell lines used in this report.

Authors’ contributions

TT and NC conceived the study. CSC offered all computational support. NC did the scripting and data formatting. TT did the bench assays. NC and TT analyzed the data and wrote the manuscript. JT performed critical reading and improved the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Laboratório de Modelagem de Sistemas Biológicos, National Institute of Science and Technology for Innovation in Neglected Diseases (INCT/IDN, CNPq), Centro de Desenvolvimento Tecnológico em Saúde, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
(2)
Tecnologia da Informação, Centro de Desenvolvimento Tecnológico em Saúde, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
(3)
Department of Oncology, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, T6G 1Z2, Canada
(4)
Department of Physics, University of Alberta, Edmonton, AB, T6G 2E1, Canada

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Copyright

© The Author(s). 2016

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