Identification and Validation of Novel Endogenous Controls for qPCR Normalization in SK-BR-3 Breast Cancer Cell Line

doi:10.21203/rs.3.rs-689871/v1

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Identification and Validation of Novel Endogenous Controls for qPCR Normalization in SK-BR-3 Breast Cancer Cell Line

https://doi.org/10.21203/rs.3.rs-689871/v1

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published 11 Oct, 2021

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Background: Normalization of gene expression using internal controls or reference genes (RGs) has been the method of choice for overcoming and standardizing the technical variations in reverse transcription quantitative polymerase chain reactions (RT-qPCR). Conventionally, ACTB and GAPDH have been used as reference genes despite evidence from literature discouraging their use, especially as single internal controls. Hence, it becomes crucial to identify suitable controls. In the present study we identified and investigated novel reference genes in SK-BR-3, an HER2-enriched breast cancer cell line. To identify novel candidate reference genes, we compiled and analyzed transcriptomic data from 82 HER2-E breast cancer samples from TCGA (The Cancer Genome Atlas) database. Twelve genes with relatively stable expression were selected for further analysis. Additionally, 13 RGs from literature were selected for analysis. The expression variations of the candidate genes in SK-BR-3 were studied over five successive passages (p) in two parallel cultures S1 and S2 and in acute and chronic hypoxia using various algorithms. Finally, the most stable RGs were selected and validated for normalization of the expression of three genes of interest (GOIs) in normoxia and hypoxia using Pfaffl’s method.

Results: HSP90AB1, DAD1, PFN1, RPL13A and PUM1 were the top five most stable RGs in the SK-BR-3 cell line. However, upon normalization of the GOIs, RPL13A was not a suitable RG candidate. Although geNorm suggests use of two reference genes, as a good practice, we investigated the reference genes in triplets. Accordingly, after normalization of GOIs in hypoxia, we found that the remaining four genes when used in triplets (in any combination) successfully normalized the experimental variations in RT-qPCR experiments.

Conclusions: HSP90AB1, DAD1, PFN1 and PUM1 can be used in any combination of three (triplets) for optimizing intra- and inter-assay gene expression differences in the SK-BR-3 cell line. Additionally, we discourage use of conventional RGs (ACTB, GAPDH, RPL13A, RNA18S and RNA28S) as internal controls for RT-qPCR in SK-BR-3 cell line.

General Biochemistry

Molecular Biology

SK-BR-3

RT-qPCR

reference genes

hypoxia

gene expression

breast cancer cell line

HER2 enriched

Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) represents a modified variant of the popular conventional PCR with diverse applications, ranging from different areas in functional genomics to molecular medicine, virology, microbiology, and biotechnology [1]. Quantitative PCR-based assays can target both DNA (genome) and RNA (transcriptome), thereby making it an extremely powerful and important technique in molecular diagnostics [2]. While functional genomics deals with understanding the functions and interactions of genes and proteins at a genome-wide level including the role of ligands, receptors, and signaling networks that converge on transcriptional regulation, transcriptomic analysis, on the other hand, deals with ascertaining the functional significance to expression signature changes between tissues, disease states, or treatment [2]. Large-scale analysis of expression patterns is performed by RNA-Seq or high-throughput microarray analysis, however, the findings for individual genes usually are validated by RT-qPCR due to its high sensitivity, specificity, reproducibility, and broad dynamic range [2–4].

However, this enhanced sensitivity of RT-qPCR imposes special conditions. The protocol necessitates accurate and precise pipetting, high-quality RNA, accurate estimation of RNA concentration, and efficient reverse transcription [3]. Other considerations include standardization of RT-qPCR protocols [5], maintaining consistency of used reagents [6–7] and careful attention towards assay design, template preparation, and statistical analysis [8]. Any deviation from these requirements (by human error etc.) can introduce variations and influence the accuracy and precision of the results [9–12]. To overcome and account for such deviations, several strategies are recommended that can be incorporated in the protocol at different stages [12–14] ranging from ensuring that a similar sample size is chosen, to using controls like spike-in foreign RNA and reference genes (previously housekeeping genes).

Normalization using reference genes has been the method of choice by most researchers since variations in the experimental workflow will affect all genes similarly [14–16]. The expression of reference genes is expected to be sufficient and stable across different tissues and cell lines under varied experimental conditions [17]. However, variance in the expression of conventional reference genes like ACTB and GAPDH across different cell types has been noted [18–22] causing a continued search for suitable candidate genes. In addition, given the highly specific nature of RT-qPCR, the Minimum Information for Publication of Quantitative RT-PCR Experiments (MIQE) guidelines recommend to use more than one reference gene for normalization unless a clear evidence of uniform expression dynamics of the single reference gene is reported for specific experimental conditions [11]. Identification and validation of novel reference genes, hence, becomes imperative for accurate normalization. In the past reference genes have been identified using various large-scale gene expression profiling methods such as Expressed Sequence Tags (ESTs), Serial Analysis of Gene Expression (SAGE) and Microarray Analysis [23–25]. However, with the advent in technology, better techniques using RNA-seq data have been employed to identify stable reference genes. Several studies have previously identified novel reference genes and/or validated conventional reference genes for the study of breast cancer in general or of molecular subtypes [26–38].

Breast cancer represents the most common malignant disease worldwide among women, accounting for 24% of new cancer cases and 15% of cancer related deaths in 2018 [39] with the number predicted to almost double to 46% by 2040 [40]. With the immense burden of the disease, it becomes crucial to develop better protocols, prediction tools, diagnostics, and treatment modalities. The PAM50 (Prediction Analysis for Microarrays) represents a 50 gene classifier containing mostly hormone receptor, proliferation related, myoepithelial and basal feature related genes and is widely used to classify breast cancer into molecular subtypes [41–43]. The HER2-Enriched (HER2-E) subtype according to PAM50 is defined by higher expression of ERBB2 along with upregulated expression of tumor proliferation related genes at the RNA and protein levels in comparison to other cancer types [44–45]. SK-BR-3, established in 1970 from the pleural effusion of a Caucasian female with malignant breast adenocarcinoma, is a human breast cancer cell line overexpressing ERBB2 gene product [46]. There are contrasting views in the literature over the classification of SK-BR-3 with some authors including it in the luminal subtype [47–48], while others classifying it as HER2-E [49–51].

In the present study, we identified and validated novel reference genes that could be used to normalize qPCR data in SK-BR-3 breast cancer cell line. Additionally, we compared the gene expression stability of newly identified genes with previously reported reference genes [26–38] to select suitable candidate reference genes. Our study reports for the first time to our knowledge, a comprehensive analysis, combining previous and novel candidates studied over multiple successive passages (p), in replicate cultures S1 and S2, and validated in various hypoxic conditions for SK-BR-3 cell line.

2.1. TCGA transcriptomic analysis for selection of novel candidate reference genes

The transcriptome profiling datasets of 82 HER2-E (PAM50 classifier) breast cancer samples were downloaded from TCGA-BRCA legacy archive via R package TCGAbiolinks [52–54]. The scaled_estimate (Platform – Illumina HiSeq; file extension – rsem.genes.results) values, which represent the estimated frequency of gene/transcripts amongst the total number of transcripts that were sequenced, were obtained from the database. TPM (transcripts per million) values were generated by multiplying scaled_estimate values to a factor of 1 million (10⁶). For data analysis and visualization, the TPM values were converted to logarithmic scale using log2(TPM).

2.2. Gene Ontology (GO)

GO Annotations and enrichment analysis was done using a web based open-access tool - The Gene Ontology Resource, powered by Panther Classification (http://geneontology.org/) [55–58]. Gene ranking was based on the fold enrichment against the background frequency of total genes annotated to that term in the designated species (Homo Sapiens; whole genome, GO version Oct 2020, doi: 10.5281/zenodo.4081749) [59]. Fisher’s Exact test with False Discovery Rate (FDR) correction was used to estimate significance with the cut-off FDR value of < 0.05. The tool was further used to group genes based on functional classification.

2.3. Culture and seeding conditions

Samples were collected from the SK-BR-3 cell line (ATCC, HTB-30) that had been used in our laboratory for previous studies [60]. For consecutive passage analysis, two cultures S1 and S2 were established from different laboratory lineages of SK-BR-3 which were cultured over five consecutive passages (p7-p11). For hypoxic exposure analysis, cultures at the level of 80–90% confluence were transferred to Xvivo System Sx2 (BioSpherix Medical; 37^oC, 5% CO₂, 2% O₂). The length of hypoxic exposure for acute hypoxia samples was 24 h and 72 h. To obtain chronic hypoxia samples, the cultures (n = 3) were fully maintained in the hypoxic environment for four consecutive passages.

From each culture, 3 lysates (in triplicates) were collected per passage. Cells were cultured in RPMI-1640 (Lonza, BE12-115F), supplemented with 10% FBS (fetal bovine serum; Sigma Aldrich, F9665) at 37^oC, 5% CO₂ with the growth medium replaced every 2–3 days. Cell passaging was performed using 1x TrypLE solution (Thermo Fisher Scientific, A12177-02). Cells were grown to 80–100% confluence in T-25cm² flasks (Sarstedt). Cell count and viability was estimated using a cell counting chamber (Improved Neubauer Hemocytometer). For further consecutive passages, cells were seeded at a density of 5000 cells/cm². Three TRIzol lysates (1x10⁶ cells) were obtained from each passage for both cultures for RNA isolation.

2.4. RNA extraction and cDNA synthesis

Total RNA was extracted using Trizol reagent (Thermo Fisher Scientific, 15596026) according to the manufacturer's protocol. The concentration and quality of the RNA was assessed by Nanodrop 2000 with the mean absorption ratios A260/280 and A260/230 checked to ensure RNA purity. RNA integrity was evaluated using 1.8% agarose gel electrophoresis. The RNA was further examined for DNA contamination by PCR for ACTB and GAPDH. The PCR reaction was performed in the presence of both positive and negative controls. No amplified PCR product was found on the agarose gel after PCR and electrophoresis of the RNA samples (except for positive controls). The cDNA synthesis reaction was carried out using the High-Capacity cDNA Reverse transcription kit (Thermo Fisher Scientific, 4368814) in accordance with the manufacturer's protocol and guidelines and was stored at -20^oC until further analysis.

2.5. Selection of reference genes and primer design

In total, 13 reference genes were selected by searching for relevant literature related to various breast cancer cell lines [26–38]. Twelve more genes were selected from the TCGA dataset analysis and were referred to as Novel Candidate Reference genes. All selected genes are summarized in Table 1. Along with these 25 candidate reference genes, three genes of interest (target genes; GOIs) were also normalized to test the selected reference genes (Table 1). Primers for all 12 selected novel candidate reference genes, PPIA and SNAI1 were designed using Primer3Plus, in accordance with the MIQE guidelines [11, 61] while the rest were taken from literature or from our previous study in MCF-7 cell line [62]. The gene function, primer pairs and respective melting curves of all the selected genes are presented in Additional File 1.

Table 1

Summary table of candidate reference genes and genes of interest (GOI)
Cell Line	Source	Selected Candidate Reference Genes
Breast Cancer various cell lines	Literature	ACTB [26, 29, 32, 34], CCSER2 [35], GAPDH [34], HNRNPL [37], HSP90AB1 [31], PCBP1 [37], PGK1 [30], PPIA [36], PUM1 [28, 33, 35], RNA18S [26, 34], RNA28S [62], RPL13A [27, 33], SF3A1 [32, 38]
HER2-E	TCGA (Novel)	BSG, CFL1, DAD1, EIF5A, GABARAP, NACA, PFN1, PSMB4, RBX1, TPT1, TUBA1B, UBC
Genes of Interest (GOI)	Expression Atlas	AURKA, BUB1, SNAI1

Selection of the three genes of interest was based on the data from Expression atlas (https://www.ebi.ac.uk/gxa/home; European Bioinformatics Institute). The atlas was searched for SK-BR-3 cell line and GOIs were randomly selected based on high expression (AURKA; 241 TPM), medium expression (BUB1; 31 TPM), and low expression (SNAI1; 5 TPM).

2.6. Primer efficiency

Standard (calibration) curves were analyzed using different concentrations and dilutions as shown in Additional File 1. For each reaction, 7 µl was used in a 384 well plate. Each dilution was done in triplicate for each primer pair along with appropriate non-template controls (NTC). Real-time PCR was performed using the ViiA7 RT-PCR thermocycler (Thermo Fisher Scientific). The cycling parameters were 95^oC for 10 mins followed by 40 cycles of amplification at 95^oC for 15 seconds, 58^oC for 30 seconds and 72^oC for 30 seconds with signal acquisition. After that melting curve were obtained by signal acquisition from 58^oC to 95^oC in increments of 0.05^oC/s.

2.7. Reverse transcription quantitative PCR (RT-qPCR)

Reverse transcription quantitative PCR (RT-qPCR) was performed with 10 ng of cDNA per reaction using ViiA 7 RT-PCR thermocycler (Thermo Fisher Scientific). Triplicate reactions of each sample were done using HOT FIREPol EvaGreen qPCR Supermix (Solis Biodyne, 08-36-00020) on 384 well plates (Applied Biosystems™, LS4309849). The cycling parameters were 95^oC for 10 mins followed by 40 cycles of amplification at 95^oC for 15 seconds, 58^oC for 30 seconds and 72^oC for 30 seconds. This was followed by melting curve acquisition as described above. All assays were performed with non-template controls (NTC).

2.8. Determination of the least variable reference genes, validation in hypoxic conditions & statistical analysis

The Cq values obtained from RT-qPCR were used to determine the stability of the candidate reference genes in the sequential normoxic passage samples using different algorithms (coefficient of variation - CV%, NormFinder [63], geNorm [17], BestKeeper [64], Comparative ∆Ct [65] and RefFinder [66]). The least variable reference genes were then tested in hypoxic samples to verify their stability and expression in hypoxic conditions. The endpoints for each of these algorithms are listed in Appendix 1. Data management and storage along with descriptive statistics were done using MS Excel (Microsoft Office 365). The statistical analysis was done using SPSS v27.0 (Released 2020, IBM Corp., Armonk, NY, USA) and R v4.0.2 (via R studio v1.3.1056).

3.1. TCGA analysis for selection of novel reference genes

Novel candidate reference genes were selected on the basis of HER-E breast cancer sample transcriptomic data from TCGA legacy dataset by applying the following criteria: (I) medium to high expression levels - mean [log2 (TPM)] ≥ 5; (II) low expression variance - standard deviation [log2(TPM)] ≤ 1; (III) no exceptional expression - no log2 (TPM) differs from the mean log2 (TPM) by a factor of two or more (criteria based on study by Li Y. et al., doi: 10.1186/s12864-019-5661-x). Once the genes were filtered according to the criteria, CV% (Coefficient of Variation) was calculated. The lower the CV%, the more stable the expression of a candidate reference gene.

A complete list of 3363 ranked genes which fulfilled the selection criteria is available in Appendix 2. The candidates for validation (12 genes) were selected, based on further criteria including association with dissimilar cellular functions and lack of association/close relation to traditional reference genes considered. Upon consideration, from the top 10 genes, six genes were selected (GABARAP, PFN1, UBC, EIF5A, CFL1 and TPT1). Three more genes were selected from ranks 11–20 (NACA, DAD1 and PSMB4). Finally, three genes (TUBA1B, RBX1 and BSG) were selected from the top 400 ranks randomly.

Based on expression levels (measured as log2[TPM]), ACTB, TPT1 and GAPDH showed high expression levels while PUM1, SF3A1 and PPIA showed low expression levels in HER2-E samples (Fig. 1a). Based on CV%, however, GABARAP, ACTB and PFN1 were the genes with the least variable expression, while PUM1, SF3A1 and PGK1 were the genes with the most variable expression (Fig. 1b). Genes with higher levels of expression generally were associated with lower inter-sample variation.

3.2. Gene Ontology (GO) over-representation analysis

Based on fold enrichment (Table 2), the top ranked biological process was modulation by symbiont of the host process which included GAPDH, UBC and PGK1. However, the most significantly enriched biological process was cellular response to cytokine stimulus (FDR = 3.33E-03) which included GAPDH, RBX1, TUBA1B, HSP90AB1, RPL13A, UBC, CFL1, PPIA and PSMB4. A complete ontology for Molecular function and Cellular Component is presented in Additional File 2.

Table 2

Gene Ontology (Biological Process) of candidate reference genes ranked by fold enrichment
GO ID	GO Term	No. of genes*	Fold Enrichment	Raw P value	FDR
GO:0044003	Modulation by symbiont of host process	3	46.10	4.24E-05	3.05E-02
GO:0006090	Pyruvate metabolic process	3	38.31	7.20E-05	3.68E-02
GO:0061418	Regulation of transcription from RNA polymerase II promoter in response to hypoxia	3	34.87	9.43E-05	4.27E-02
GO:0048524	Positive regulation of viral process	3	32.67	7.14E-06	1.03E-02
GO:0019058	Viral life cycle	5	20.33	4.26E-06	7.52E-03
GO:0006417	Regulation of translation	5	12.12	4.90E-05	3.26E-02
GO:0071345	Cellular response to cytokine stimulus	9	7.86	8.38E-07	3.33E-03
GO:0043066	Negative regulation of apoptotic process	7	6.96	4.13E-05	3.12E-02
GO:0006139	Nucleobase containing compound metabolic process	11	3.64	6.05E-05	3.56E-02
GO:0010604	Positive regulation of macromolecule metabolic process	12	3.05	1.33E-04	5.02E-02
GO:0043170	Macromolecule metabolic process	17	2.43	2.16E-05	2.01E-02
*No. of genes indicates the number of genes from the input selected candidate reference genes that are represented by the respective GO term.

3.3. Grouping of genes based on functional classification (Panther)

Panther was used to group the candidate reference genes based on function. The grouping was done across five different ontologies (Fig. 2) – GO biological process, GO molecular function, GO cellular component, protein class and pathway. Most of the genes in GO cellular component analysis were associated with cell or cell part (17 genes each) (Fig. 2). In protein class classification six genes were identified as genes encoding cytoskeletal proteins (ACTB, TUBA1B, TPT1, PFN1, CFL1 and GABARAP). Finally, based on pathway, three genes were associated with cytoskeletal regulation by Rho GTPase pathway (ACTB, PFN1 and CFL1) while two genes were associated each with glycolysis (GAPDH and PGK1) and Huntington disease (GAPDH and ACTB).

3.4. Candidate reference gene stability in SK-BR-3 cell line

In the present study, we collected three technical replicates from five consecutive passages (p7-p11) in two replicate cultures S1 and S2. The biological replicates were analyzed for all 25 candidate reference genes, thereby producing a dataset with 250 Cq values. A similar dataset was analyzed for the three genes of interest (dataset of 30 Cq values). Different algorithms were then used to analyze the stability of the reference gene expression. NormFinder, geNorm, comparative ∆Ct and RefFinder all ranked HSP90AB1, PGK1, DAD1, PUM1 and RPL13A as the most stable genes in the consecutive passages of SK-BR-3 (Fig. 3). BestKeeper, however, ranked RBX1, CFL1 and UBC as the most stable genes. RNA18S, TUBA1B and RNA28S were consistently ranked as the least stable reference gene candidates by all algorithms.

3.5. Candidate reference gene stability in replicate cultures

To analyze the expression stability of candidate reference genes in replicate cultures S1 and S2, we applied all algorithms separately, as shown in Additional File 3. Descriptive analysis revealed that CCSER2 and GABARAP showed low expression levels (Cq > 28), while RNA28S and RNA18S showed high expression levels (Cq < 15). Coefficient of variation (CV%) analyses revealed that TUBA1B showed high variation (> 50%) and hence was not a suitable candidate. Further analysis with BestKeeper revealed ACTB, SF3A1, CFL1, UBC and NACA to have a low/moderate correlation with the BestKeeper Index (BI). After removal of these 10 candidate reference genes, the remaining 15 genes were re-analyzed using various algorithms, and none of the remaining genes violated any criteria. Finally, cumulative rankings from both cultures revealed HSP90AB1, DAD1, PGK1, RPL13A and PUM1 to be the top 5 most stable genes.

3.6. Selection of reference genes for further validation

Since geNorm indicated that use of two genes (Additional File 3) would be sufficient, we decided to select the top five least variable genes from our analysis and test different triplets (rather than in pairs, as a good practice) of the selected five genes. To select the genes, we analyzed the results obtained so far from both cultures whilst also factoring-in CV% rankings (Additional File 3). As a result, HSP90AB1, DAD1, PFN1, RPL13A and PUM1 were selected for further analysis and normalization of the genes of interest.

3.7. Normalization of genes of interest (GOIs)

The ∆∆Ct method is the method of choice for normalization of gene expression of genes of interest [67]. The modification of the ∆∆Ct method, accounting for primer efficiencies and multiple reference genes [68–69] was used in the present study. To account for primer efficiency in the equation, the mean efficiency from broad range and narrow range dilutions was used. Accordingly, we normalized three genes of interest (GOIs) – AURKA, BUB1 and SNAI1 - with triplet pairs of the five chosen reference genes (10 possible triplets as shown in Fig. 4). According to the guidelines, any arbitrary sample can be considered as an internal calibrator (control) for normalization without any effects on the relative quantification result. Hence, we considered passage p7 as the internal calibrator since it was the initial passage in the experiment.

For evaluation of successful normalization, the NRQs (normalized relative quantities) should be checked for patterns in expression, and the difference should be minimal between each sample after normalization [69, 70]. For AURKA, we noticed that the expression in comparison with p7 decreased in p8 followed by increase in p9. The NRQs were found to be close to p7 levels in p10 and p11 (Fig. 4a). However, this trend was not observed in the triplets with RPL13A (triplets 2, 5, 6, 7, 9 and 10). The expression of triplets with RPL13A in p9 and p10 was highly elevated in comparison to p7 (Fig. 4a). Similar observations were made for BUB1 and SNAI1 (Fig. 4b and 4c). This may be explained by the fact that among the five selected genes RPL13A had the lowest correlation r (r = 0.856) with the BestKeeper Index (Additional Table 10 in Additional File 3), which was also shown by NormFinder results. Hence, RPL13A was not an ideal candidate and we removed it from the further analysis.

3.8. Effects of hypoxia on the expression of the selected reference genes

To evaluate the effects of hypoxia on reference gene stabilities, we applied all algorithms combining data with hypoxic sample results for the four reference genes (HSP90AB1, PUM1, DAD1 and PFN1). In hypoxia, DAD1 and HSP90AB1 were the two most stable genes as revealed by NormFinder, geNorm, Comparative ∆Ct and RefFinder (Fig. 5). BestKeeper, however, ranked PUM1 as the most stable gene, and the DAD1 showed the highest correlation with the BestKeeper index.

Comparison of the expression stabilities of the genes in normoxia vs hypoxia showed that the expression stability of all four genes decreased after addition of hypoxic samples to the dataset (Fig. 5). Only DAD1 and PFN1 displayed improved expression stability in hypoxia according to RefFinder (Fig. 5f). Similarly, only these two genes improved their correlation with the BestKeeper index in hypoxia.

3.9. Validation of reference genes in hypoxic conditions

We found that after normalization AURKA was slightly upregulated (Fig. 6a) after 24 h of hypoxic exposure while after 72 h, different triplets indicated different results. The normalization factor (NF) was within the acceptable limits (NF < 2 to 3-fold relative to the average), thus eliminating potential causative issues, e.g., starting material quantity or quality, or a problem with one of the reference genes (either not stably expressed, or not adequately measured) [70]. As pointed out by the other authors [34, 70], the choice of calibrator sample (reference genes) does not influence the relative quantification result.

Although numbers may differ, the actual fold differences between the samples remain identical, therefore the results are fully equivalent and only rescaled. The expression of BUB1 was sequentially increasing, while SNAI1 after an initial (24 h) downregulation became upregulated, but still below the control levels (Fig. 6). Both AURKA, and BUB1 were downregulated (-0.78 and − 1.26 average log2 fold change, respectively) in chronic hypoxia, when normalized by all four reference gene triplets (Fig. 6). However, SNAI1 was upregulated in chronic hypoxia (+ 1.03 average log2 fold change). Based on our analysis, we can conclude that the selected reference gene triplets were able to successfully normalize the genes of interest irrespective of the expression levels of gene of interest in SK-BR-3 cell line (AURKA - high expression; BUB1 - medium expression; SNAI1 - low expression)

A pivotal aspect in any gene expression study is the selection of appropriate internal controls that are expressed uniformly irrespective of culturing conditions, experimental treatment, nutrient stress etc. These controls (or references) normalize any variations in starting quantities, calibration issues or poor pipetting, thereby providing accurate results. However, complex gene-gene interactions and environmental effects on gene expression complicate identification of such controls. Another layer of complexity in this pursuit is added by intrinsic heterogeneity of the cancer cells. Several tools and algorithms (NormFinder, geNorm, BestKeeper, Comparative ∆Ct and RefFinder) have been developed that can aid in sorting, selection, and validation of the reference genes. However, the considerations regarding applicability of these algorithms and the acceptable cut-off limits are often misinterpreted or misapplied. Finally, identification and extensive validation of the reference genes for each type of biological object in different conditions may not be feasible in every instance. Such studies are often time and resource (labor, financial) consuming. In the present study, we have identified and validated novel reference genes for normalization of RT-qPCR results in SK-BR-3 breast cancer cell line.

In the past the TCGA database has been commonly used to identify novel and reliable candidate genes in different cancer types [37–38], but it has never been investigated individually for HER2-E subtype of breast cancer. Our analysis of TCGA legacy data revealed that GABARAP, ACTB and PFN1 were the top three genes with the least variation amongst the samples (Fig. 1b). However, upon validation in the SK-BR-3 cell line, we found that the least variation amongst the samples was displayed by RBX1, UBC and CFL1 (Additional File 3). These differences between TCGA database and RT-qPCR results are normal and expected. Firstly, the database represents a heterogenous mixture of similar subtypes while the SK-BR-3 is a single cell line, therefore constitutes a more homogenous sample. Secondly, all samples of TCGA repository come from primary untreated tumors collected from different institutes. Thirdly, there is an inherent possibility of bias in the biorepository generation process, stemming from different institutional research interests, operative protocols, or patient populations [71]. Also, it must be taken into account that tissue specimens (TCGA data) in addition to cancer and normal mammary cells contain various types of stromal and immune cells, which are absent from the cell culture samples. This type of heterogeneity also may have effects on the overall transcription levels and stability. The database accommodates for most of the possible gene expression variations in tissues (in vivo) and, since cell lines like SK-BR-3 have been under constant cultivation over long-term, they tend to behave like outliers when compared to the TCGA database. Nonetheless, such extrapolation, bridging and application of in vivo data to in vitro conditions enable us to find common core genes which are uniformly expressed in both scenarios.

Gene Ontology (GO) analysis (Table 2) revealed that the candidate genes selected based on the TCGA samples and literature were most significantly enriched in cellular response to cytokine stimulus (GO:0071345; CFL1, GAPDH, HSP90AB1, PPIA, PSMB4, RBX1, RPL13A, TUBA1B and UBC). Indeed, it is well known that breast cancer cells respond to various cytokines in their microenvironment. The cells have been shown to evade cytokines like TGF-β in the early stages (due to its anti-proliferative effects), however, in the later stages TGF-β stimulates the progression of the disease by induction of epithelial to mesenchymal transition (EMT type 3) [72–74]. IL-1 (adipokine) is known to increase the aromatase activity in SK-BR-3 cells, resulting in generation of bioactive estrogens and increased cellular proliferation [75]. Other cytokines like IL-6, IL-19, IL-20, TGF-α, TNF-α, and IL-23 are also known to promote cancer progression [72]. Using data available from Mouse Genome Informatics (MGI; http://www.informatics. jax.org/), CFL1 was associated with cellular response to IL-1, IL-6 and TNF, GAPDH and RPL13A were associated with response to IFN-γ, while HSP90AB1 and TUBA1B were associated with the response to IL-4. Apart from cytokine response, the genes were found to be enriched in viral life cycle (GO:0019058; BSG, HSP90AB1, PCBP1, PPIA and UBC) and positive regulation of viral processes (GO:0048524; BSG, PFN1 and PPIA). Evidence of various virus-related DNAs like EBV (Epstein-Barr virus), HPV (Human Papillomavirus), BLV (bovine leukemia virus) and MMTV (Mouse mammary tumor virus) has been found in breast cancers [76–79]. In fact, Lawson et al., demonstrated presence of HPV associated pre-malignant koilocytes in normal and malignant breast tissues [80], indicating the oncogenic correlation between viruses and breast cancer and the possible explanation for the enrichment of virus associated pathways in SK-BR-3 cell line. Using data from MGI, we found that BSG was associated with positive regulation of viral entry into the host cell, while HSP90AB1 was associated with virion attachment to the host cell. On the other hand, PFN1 and PPIA were found to be associated with positive regulation of viral transcription and viral genome replication, respectively.

To understand the role of the four validated reference genes in metabolic processes, GO biological processes analysis in association with data from MGI were investigated. The genes were found to be associated with negative regulation of apoptosis (DAD1) and nucleobase containing compound metabolic process (Table 2). HSP90AB1 was found to be associated with positive regulation of activities of telomerase, phosphoprotein phosphatase and protein serine/threonine kinase. PFN1 was found to play a role in positive regulation of DNA metabolic processes and of transcription by RNA polymerase II, while PUM1 was associated with regulation of mRNA stability and production of miRNAs (microRNAs). Based on Molecular function, HSP90AB1 and PFN1 shared two common ontologies – RNA binding and Cytoskeletal protein binding (Additional File 2), both of which seemed to be downregulated in hypoxic conditions (for PFN1, fold change was 0.68 and 0.45 after 24 hours 72 hours of acute hypoxia, respectively, and 0.89 in chronic hypoxia). With respect to cellular component analysis the regulation of different genes (even from the same ontology) during hypoxia was divergent/unrelated. Whilst PUM1, HSP90AB1 and PFN1 shared two ontologies - nuclear and cytosol associated genes, the genes were differently regulated with PUM1 showing upregulation while other two showed downregulation in gene expression.

Our analysis revealed that the four reference genes (HSP90AB1, DAD1, PUM1 and PFN1), when used in any combination of three, successfully normalized the genes of interest (AURKA, BUB1 and SNAI1) in both hypoxic and normoxic conditions over multiple passages. HSP90AB1, previously known as HSPCB, was first described as a candidate reference gene by Jacob et al. [31] in a variety of 25 different cancer cell lines. However, the only breast cancer line in that study was MCF-7 (Luminal A subtype). The gene has been included among the most stable reference genes in other tissues/organs, e.g., ovary, muscle tissue, adipose tissue etc. [81–82]. Heat Shock Proteins like HSP90AB1 have been previously shown to be downregulated in response to hypoxia in pig adipose-derived stromal/stem cells [83] as well as in human hepatocytes [84]. Such downregulation (fold expression change of 0.71 after 24 hours and 0.88 after 72 hours in acute hypoxia and 0.52 in chronic hypoxia) appeared to have an impact on the stability of the gene by marginally lowering the expression stability in hypoxic conditions (Fig. 5).

DAD1, a novel reference gene identified in the present study, is a small integral membrane protein of the oligosaccharyltransferase (OST) enzyme complex involved in the highly conserved asparagine-linked glycosylation of proteins in all eukaryotic cells [85]. The gene has been shown to be involved in apoptosis. Loss of DAD1 gene led to apoptosis in hamster cell lines and yeast cells [86–87]. In fact, DAD1 was preferentially expressed in hepatocellular carcinoma (HCC) and prostate cancer cells [88–89]. Hence, it has been postulated that high expression of DAD1 in HCC cells can activate OST and block apoptosis, thereby enhancing tumor cell survival [88]. We speculate a similar role of the gene in the SK-BR-3 breast cancer cell line that could explain its consistent and stable expression. In A431 epithelial carcinoma cells, DAD1 was upregulated in hypoxic conditions (by a fold change of 1.2) after exposure of 72 hours [90]. In our results, however, DAD1 was downregulated by 0.64-fold change after exposure to acute hypoxia for 24 and 72 hours. The expression then increased and approached normoxia levels during chronic hypoxia (fold change 0.95). The differences in our results from those in A431 cells could be explained by the cardinal differences in background transcriptome and epigenome of A4321 and SK-BR-3 cells. Furthermore, there were differences in culturing conditions. While our cells were exposed to 2% O₂ in acute hypoxia, the A431 cells were exposed to < 0.1% O₂ [90]. Our results suggest that SK-BR-3 cells after an initial shock phase quickly adapted to chronic hypoxic conditions and continued to express core genes in levels similar to normoxia. This is also supported by the fact that the expression stability of DAD1 improved in hypoxia, i.e., it was expressed even more stably after exposure to hypoxia.

PFN1, another novel reference gene identified in the present study, is often regarded as the founding member of its family constituting four profilin genes (PFN1, PFN2, PFN3 and PFN4). These genes have been associated with almost every aspect of cellular functions including proliferation, survival, motility, endocytosis, membrane trafficking, mRNA splicing as well as gene transcription [91–92]. The overexpression of PFN1 could negatively regulate cancer cell motility in breast cancer cells [93]. Additionally, it has been demonstrated that in triple negative breast cancer cell lines, overexpression of PFN1 suppresses AKT (serine-threonine kinase) activation via upregulation of PTEN (phosphatase and tensin homolog) [94], indicating the tumor-suppressive character of PFN1 gene. Similar observations have also been reported in pancreatic cancer cells [95]. These findings are of interest since the expression of PFN1 in the SK-BR-3 cell line seems to be uniform and stable despite its anti-tumorigenic nature. In fact, depletion of PFN1 in breast cancer cells has enabled hyper-migratory phenotype in vitro and enhanced hematogenous dissemination from primary tumor in vivo [96]. We can hypothesize that some downstream regulation is at play, which leads to decreased PFN1 protein levels in breast cancer cells despite rather uniform expression of the gene.

Finally, the fourth reference gene identified for the SK-BR-3 cell line, PUM1 has been previously described as a candidate reference gene in various cancers including breast cancers [28, 33, 35]. The gene has been associated with cancer cell growth, migration, and invasion [97]. In fact, amongst the four selected reference genes in our study, only PUM1 showed an increase in expression fold change in hypoxia. The fold change was 0.80 and 1.13 after 24 hours and 72 hours of acute hypoxia, respectively, and reached 1.56 in chronic hypoxia. These findings indicate the crucial role played by the gene in the maintenance of cellular functions in hypoxic conditions. Another candidate gene, RPL13A, has been previously described as a stable reference gene in breast cancers [27, 33]. The expression of RPL13A was quite stable in our analysis, however, it did not yield successful normalization of the genes of interest.

Various other reference genes in the past have been described to be suitable reference genes in breast cancers including GAPDH, ACTB, PPIA and RNA28S/RNA18S [26, 29, 32, 34]. However, there is also contrary evidence suggesting against the use of these genes as references [18–22]. Use of GAPDH as a reference gene has long been a topic of debate. It is overexpressed in cervical, prostate, pancreatic and lung cancers, and it has been the least stable gene in multiple studies [29, 34, 98–99]. Similar concerns have been raised concerning use of ACTB as a single internal control [28]. RNA18S and RNA28S have been reported previously to be stable reference gene candidates [34], however, concerns regarding absence of purified mRNA samples and their relatively high abundance compared to the target mRNAs have been reported [17]. Both RNA28S and RNA18S were highly expressed (mean Cq = 7–8) in the present study and in our study of MCF-7 cell line [62]. Secondly, these two genes are not included in the TCGA database, which makes it difficult to compare their expression between in vivo and in vitro scenarios.

PPIA along with ACTB was found to be the most stable reference gene for basal type breast cancer cell lines in hypoxic and serum deprived conditions [36]. However, our analysis revealed that the expression of PPIA was moderately stable and ranked 9th in the combined analysis (Additional File 3). In our study of MCF-7 breast cancer cell line, we identified GAPDH-CCSER2-PCBP1 triplet as the most stable reference gene triplet which could be used to normalize the expression of genes of interest in various nutrient stress conditions [62]. However, the expression of CCSER2 was extremely variable in the present study. The gene did not reach the thresholds set for TCGA analysis, and the expression of the gene was low (mean Cq = 27.4). Hence, we eliminated it from our analysis in early stages. PCBP1 and GAPDH were among the top 15 most stable reference genes in our analysis (Additional File 3). Interestingly, GABARAP was identified to have the least variation (CV%) among our novel candidate reference genes, however, the gene was associated with low expression and poor primer efficiency when confirmed by RT-qPCR (Additional Files 1 and 3).

Nonetheless, the results of the present study are constrained by some limitations. First, the expression stability of the reference genes was validated in vitro only. Second, these reference genes were tested in normoxic and hypoxic conditions only. Their use for normalization of expression in other conditions such as nutrient stress, drug treatment etc. remains to be validated. Finally, given the heterogeneous behavior of cancer cells, there is a need for inter-laboratory validation to further confirm our results. The major question that arises is how many more reference genes we need to identify. Rather, how many reference genes can we identify. Although a precise number will be difficult to predict, the estimates in normal human tissues (using ESTs) predict the numbers to be in the range of 3100 to 6900 genes [92], thereby making a plethora of reference genes still waiting to be identified and validated that could be more promising than the ones previously reported. However, we agree with other authors that rather than testing thousands of genes, we need to validate a panel of reference genes whose expression under varying conditions can be proven to be as minimally variable and as robust as possible [35]. Accordingly, based on our analysis, we suggest the use of HSP90AB1, DAD1, PFN1 and PUM1 in any combination of three (triplet), thereby giving other researchers not one but four different combinations to choose from based on their individual experimental designs and needs.

Based on the results of the present study, we suggest the use of HSP90AB1, DAD1, PFN1 and PUM1 in any combination of threes (triplet) for normalization of the expression of genes of interest in SK-BR-3 breast cancer cell line. The conventional RT-qPCR reference genes like ACTB, GAPDH, RPL13A, RNA18S, RNA28S, as well as CCSER2 and GABARAP are not appropriate for use as reference genes in the SK-BR-3 cell line.

HER2-E MCF-7	HER2 enriched Michigan Cancer Foundation – 7
RGs	Reference genes
RT-PCR	Real time – polymerase chain reaction
p GOI CV% S.D. TPM BRCA TCGA HUGO FBS NRQ ACTB GAPDH RPL13A PGK1 HSP90AB1 RNA18S RNA28S PUM1 CCSER2 HNRNPL PCBP1 SF3A1 DAD1 PSMB4 BSG TUBA1B RBX1 CFL1 UBC PFN1 EIF5A TPT1 NACA PPIA GABARAP AURKA BUB1 SNAI1	Passage Gene of Interest Coefficient of Variation % Standard Deviation Transcripts per Million Breast Invasive Carcinoma The Cancer Genome Atlas Human Genome Organization Fetal Bovine Serum Normalized Relative Quantity Actin Beta Glyceraldehyde-3-phosphate Dehydrogenase Ribosomal Protein L13a Phosphoglycerate Kinase 1 Heat Shock Protein 90 Alpha family Class B member 1 RNA, 18S ribosomal RNA, 28S ribosomal Pumilio RNA Binding family member Coiled-Coil Serine Rich protein 2 Heterogeneous Nuclear Ribonucleoprotein L Poly(rC) Binding protein 1 Splicing factor 3a subunit 1 Defender against cell death 1 Proteasome 20S subunit beta 4 Basigin (OK blood group) Tubulin alpha 1b Ring-Box 1 Cofilin 1 Ubiquitin C Profilin 1 Eukaryotic translation initiation factor 5A Tumor protein, translationally controlled 1 Nascent polypeptide associated complex subunit alpha Peptidylprolyl isomerase A GABA type A receptor associated protein Aurora Kinase A BUB1 mitotic checkpoint serine/ threonine kinase Snail family transcriptional repressor 1

Funding: The present study was funded by Riga Stradiņš University (RSU) Project Nb.5-1/252/2020.

Competing Interests: The authors declare no competing interests in the present study. Further, neither funders nor the funding institution had a role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Ethical Approval: Not Applicable

Consent to Participate: Not Applicable

Consent for Publication: Not Applicable

Availability of Data and Materials: The full TCGA dataset (after processing and ranking for HER2-E subtype) is available as Appendix 2. The RT-qPCR raw datasets and metadata analyzed are available from corresponding author upon reasonable request.

Code Availability: The full code for R based TCGAbiolinks used to access TCGA data in the present study is available from the corresponding author upon reasonable request. Alternatively, refer to the following online help resources (to personalize the code):

1)https://bioconductor.riken.jp/packages/3.3/bioc/vignettes/TCGAbiolinks/inst/doc/tcgaBiolinks.html (Last accessed October 2020)

2)https://bioconductor.org/packages/release/bioc/vignettes/TCGAbiolinks/inst/doc/download_prepare.html (Last accessed October 2020)

Author’s Contributions: NJ and IC conceptualized the study while IC, IM, DN and NJ were responsible for methodology. Transcriptomic data and statistical analysis were done by NJ. Data curation was done by NJ and IM. Validation of the study protocol and results was done by VP, IC, DN, IM, and NJ. Visualization was done by NJ and VP while project supervision and funding acquisition was done by IC. Investigations, coding, and validation were done by NJ. Original draft was prepared by NJ and IC while revisions and final review was done by VP, DN, IC, IM, and NJ. All authors have read and approved the final manuscript for publication.

Acknowledgment: We are extremely grateful and thankful to our colleagues in the Latvian Biomedical Research and Study Centre (LV-BMC) for their unconditional support in providing the Viia7 platform for performing the RT-qPCR reactions.

Authors’ Information: All Authors (NJ, IM, DN, VP and IC) are affiliated with the Laboratory of Molecular Genetics, Institute of Oncology, Riga Stradiņš University, Dzirciema street 16, Riga, Latvia LV-1007.

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Identification and Validation of Novel Endogenous Controls for qPCR Normalization in SK-BR-3 Breast Cancer Cell Line

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Abstract

Figures

1. Background

2. Materials And Methods

2.1. TCGA transcriptomic analysis for selection of novel candidate reference genes

2.2. Gene Ontology (GO)

2.3. Culture and seeding conditions

2.4. RNA extraction and cDNA synthesis

2.5. Selection of reference genes and primer design

2.6. Primer efficiency

2.7. Reverse transcription quantitative PCR (RT-qPCR)

2.8. Determination of the least variable reference genes, validation in hypoxic conditions & statistical analysis

3. Results

3.1. TCGA analysis for selection of novel reference genes

3.2. Gene Ontology (GO) over-representation analysis

3.3. Grouping of genes based on functional classification (Panther)

3.4. Candidate reference gene stability in SK-BR-3 cell line

3.5. Candidate reference gene stability in replicate cultures

3.6. Selection of reference genes for further validation

3.7. Normalization of genes of interest (GOIs)

3.8. Effects of hypoxia on the expression of the selected reference genes

3.9. Validation of reference genes in hypoxic conditions

4. Discussion

5. Conclusions

6. List Of Abbreviations

7. Declarations

8. References

Supplementary Files

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