Identification of appropriate housekeeping genes for gene expression studies in human renal cell carcinoma under hypoxic conditions

Hypoxia pathways are deregulated in clear renal cell carcinoma (ccRCC) because of the loss of the von Hippel-Lindau tumor suppressor function. Quantitative PCR is a powerful tool for quantifying differential expression between normal and cancer cells. Reliable gene expression analysis requires the use of genes encoding housekeeping genes. Therefore, in this study, eight reference candidate genes were evaluated to determine their stability in 786-0 cells under normoxic and hypoxic conditions. Four different tools were used to rank the most stable genes—geNorm, NormFinder, BestKeeper, and Comparative Ct (ΔCt), and a general ranking was performed using RankAggreg. According to the four algorithms, the TFRC reference gene was identified as the most stable. There was no agreement among the results from the algorithms for the 2nd and 3rd positions. A general classification was then established using the RankAggreg tool. Finally, the three most suitable reference genes for use in 786-0 cells under normoxic and hypoxic conditions were TFRC, RPLP0, and SDHA. To the best of our knowledge, this is the first study to identify reliable genes that can be used for gene expression analysis in ccRCC in a hypoxic environment.


Introduction
Renal cell carcinoma (RCC) is a group of malignant histological subtypes that arise from epithelial cells, accounting for 2-3% of all malignancies in adults [1,2]. The three major RCC histological subtypes are clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (ccRCC). Each subtype is associated with unique genetic mutations, clinical characteristics, and sensitivity to treatment [3].
Maria Helena Bellini mbmarumo@ipen.br at 20.000xg for 15 min, the supernatant was collected and added to protease inhibitor cocktail powder (Sigma-Aldrich, St Louis, MO, USA). The protein concentration was detected by using the bicinchoninic acid assay (BCA) method and stored at -80 °C until use. The protein samples were loaded at a concentration of 50 µg per lane, separated using 12% sodium dodecyl sulphate polyacrylamide gel electrophoresis, and transferred onto a GE Hybond-P polyvinylidene difluoride membrane. The membrane was then blocked in 5% skim milk in Tris-buffered saline at room temperature for 1 h. The membranes were incubated overnight at 4 °C with the primary antibody anti-HIF-2α (rabbit polyclonal anti-mouse; Abcam, Cambridge, UK), diluted 1:300, followed by incubation with HRP-conjugated goat anti-rabbit secondary antibody (Santa Cruz Biotechnology, Dallas,TX, USA), diluted 1:1000, for 2 h at room temperature. Signals were detected using the SuperSignal® West Pico Chemiluminescent Substrate Kit (Thermo Scientific, Waltham, MA, USA). Photographs were taken using the Uvitec Cambridge Alliance 4.7 equipment. Protein bands were quantified using ImageJ software.

RNA extraction
RNA was extracted from the cells grown under normoxic and hypoxic conditions. The cells were washed with PBS, and RNA was extracted using the RNeasy® Mini Kit (Qiagen, Valencia, CA, USA), following the manufacturer's instructions. The extracted RNA was diluted with RNasefree water. Subsequently, RNA concentration (ng/µL) and purity (A260/280) were determined using a Nanodrop® ND-100 (Thermo Scientific). The RNA was considered pure if the A260/280 ratio was within the range of 1.8-2.1. The integrity of the samples was confirmed using agarose gel electrophoresis. The RNA samples were stored at -80 °C.

Complementary DNA (cDNA) synthesis
The QuantiTec reverse transcription kit (Qiagen) was used to synthesize cDNA. The cDNA was synthesized using 2 µg of total RNA, followed by elimination of genomic DNA using the buffer from the kit. The resulting mixture was then incubated in a thermocycler at 42 °C for 2 min and then immediately transferred on ice. A second mix was prepared, complementing the previous mix, containing the RT primer, and amplified at 42 °C for 15 min. The reaction was stopped with a cycle of 95 °C for 3 min. Then, the samples were incubated in an ice bath for 2 min and stored at -20 °C until qRT-PCR analysis.
HIF1α protein for degradation. Thus, free HIF1α promotes the transcription of various target genes. Molecular studies of hypoxia-responsive pathways are challenging because they require genes with stable expression to be used as reference genes [8].
Determination of gene expression profiles is an important tool in the field of molecular oncology. The analysis of differential gene expression between tumors and normal tissues is essential for identifying possible therapeutic targets [9]. Real-time quantitative polymerase chain reaction (qRT-PCR) is used to measure mRNA in a given cell type; owing to its high sensitivity and accuracy, this technique is the gold standard for gene expression measurements [10]. In qRT-PCR analysis, the target gene expression is determined by normalizing it with the expression of housekeeping genes (HKG). HKGs are a set of genes that are constitutively expressed and play a fundamental role in maintaining the existence of cells, and their expression is not modulated by experimental conditions [11].
In this study, we investigated the performance of a panel of eight HKGs in a ccRCC cell line under normoxic and hypoxic conditions, with the aim of identifying suitable reference genes for normalization in RCC gene expression studies.
A day before the hypoxia assay was performed, approximately 2 × 10 5 cells were seeded in 60 mm petri dishes and incubated for 6 h in a hypoxia-inducing humid chamber (StemCellTM Technologies, USA) with an atmosphere of 1% O 2 , 5% CO 2 , and 94% N 2 , and placed in an incubator at 37 °C. The Altair PRO Single-Gas Detector (Code: 217,597, MSA, Cranberry Township, Pennsylvania, USA) was used to measure the O 2 concentration inside the chamber.

Protein extraction and western blot analysis
Proteins were extracted from the cells grown under normoxic and hypoxic conditions using CelLytic™ M reagent (Sigma-Aldrich, St Louis, MO, USA). After centrifugation

Hypoxic response
The effectiveness of the hypoxic microenvironment was confirmed using western blot analysis. The 786-0 cells cultured under hypoxia had significantly increased HIF-2 α protein levels compared to those observed in 786-0 cells grown under normoxia (normoxia vs. hypoxia, P < 0.05) ( Fig. 1 A and A1).

RNA quality
The RNA extracted from all samples showed high yield, quality, and integrity. The mean RNA concentration in the cells from the normoxia and hypoxia group was 3603.38 ± 176.50 ng/µL and 3111.76 ± 54.90 ng/µL, respectively. The mean A260/280 ratio in the cells from the normoxia and hypoxia group was 2.05 ± 0.02 and 2.08 ± 0.01, respectively. Integrity was assessed using agarose gel, and two sharp bands (28 S and 18 S rRNA) were observed.

Primer specificity and efficiency
The specificity of the primers designed for the amplification of HKGs was determined using melt-curve analysis. A single fluorescence peak was detected for each primer, indicating that only one fragment was amplified during qPCR amplification (Fig. 2). The efficiency of the primers (E) ranged from 1.98 to 2.02, and the correlation coefficient (R 2 ) ranged from 0.99 to 1.00.

Expression stability of reference genes under normoxic and hypoxic conditions
The cycle threshold (Ct) values of eight reference genes in 786-0 cells under normoxic and hypoxic conditions were used to compare gene expression patterns. A wide range of Ct expression variances were observed. ATCB had the highest Ct variation, while TFRC, RPLPO, SDHA, and HPRT1 showed the lowest variation (Fig. 3).

Determination of expression stability of candidate reference genes
The expression stability of the eight candidate genes was assessed under hypoxic conditions and evaluated using the statistical algorithms BestKeeper, geNorm, NormFinder, and Delta-C T (ΔCт).
The stability of the HKGs was determined using Best-Keeper based on the extent of standard deviation (SD ± CP), with a higher SD value corresponding to the low stability

Real-time quantitative polymerase chain reaction (qRT-PCR)
The amplification qRT-PCR was performed using SYBR Green® (Applied Biosystems, NY, USA). StepOnePlus® (Applied Biosystems), and the protocol was as follows: 2 min at 50 °C and 10 min at 95 °C; two cycles of 15 s at 95 °C and 1 h at 60 °C (40 cycles); followed by a final cycle of 15 s at 95 °C.

Analysis of the stability of reference genes
Four algorithms were used to determine the stability of the candidate HKG: NormFinder [12], geNorm [13], Best-Keeper [14], and Delta-Ct (ΔCт) method [15]. NormFinder calculates the stability of reference genes based on intra-and inter-group variability. The weighted measure of these two parameters is expressed as the S value, and the most stable reference gene has the lowest S value [12]. GeNorm calculates the average expression stability (M). The algorithm first identifies two genes with the highest expression agreement and, therefore, high stability for each gene. Lower M values indicate greater stability [13]. The BestKeeper program calculates a Pearson's correlation coefficient for each gene, where p values closer to 1.0 indicate greater stability [14]. Comparative ΔCt method uses a basic ΔCt approach to compare the relative expression of pairs of genes, creating a stability rank based on ΔCt and average standard deviations. The genes with the lowest average standard deviation (SD) and constant ΔCt values are considered the most stable [15]. In addition, once all the stability values for all tools were obtained, the BruteAggreg function, a weighted rank aggregation tool from the RankAggreg package was used [16]. This is an R package that uses a Monte Carlo algorithm to calculate the Spearman distance to obtain the overall ranking among the evaluated genes and tools (NormFinder, geNorm, BestKeeper, and ΔCт). of the HKGs. According to the BestKeeper ranking, TFRC (0.38) was the best candidate, followed by SDHA (0.60) and HPRT1 (0.63) ( Table 2).
GeNorm analysis ranked the target reference genes according to their M values using the Ct values of all of the samples. Samples with the lowest M values were considered to be the most stable, and vice versa. The M value of HKGs ranged from 0.65 to 1.55. TFRC and RPLP0 showed the highest stability (0.65), followed by SDHA (0.75) and PGK1 (0.89) ( Table 2).
NormFinder analysis was employed for intra-and intergroup variations to estimate stability values. Following this approach, TFRC (S-value = 0.20) was identified as the most stable gene, followed by SDHA (S = 0.51) and RPRP0 (S = 0.51) ( Table 2).
Finally, the stability of the HKGs was determined using the comparative ΔCт methods based on SD. A lower SD value correlated with higher stability of the HKGs. The TFRC with an SD value of 1.15 was perceived to be the most stable HKG, followed by SDHA (1.20) and RPLP0 (1.20) ( Table 2).
The rank-ordered genes calculated using the four algorithms presented in Table 2 were further analyzed by using RankAggreg [16] to obtain a consensus rank list of genes. The stability of the candidate reference genes was in the following order: TRFC > RPLPO > SDHA > PGK1 > HPRT1 > GAPDH > ACTB > X18S (Fig. 4).

Discussion
Accurate relative quantification in gene expression analysis requires the use of normalized reference genes, since the stability of target genes could vary according to the experimental design, making it essential for the reliability of the results [10,13,14]. The importance of selecting suitable reference genes for gene expression analyses has recently been highlighted in several studies [17][18][19][20][21].
RCC cell line-based research has a major impact on understanding signaling pathways and discovering new therapeutic targets [22]. In vitro assays mimic the tumor microenvironment conditions as closely as possible. Hypoxia is present in nearly 80% of RCCs and modulates the gene expression profile, resulting in an aggressive phenotype of this tumor [23]. Despite the great importance of hypoxia in the pathophysiology of RCC, studies on more adequate HKGs are scarce. To the best of our knowledge, this is the first study on elucidating the appropriate HKG under hypoxic conditions in ccRCC cells. For this purpose, eight putative reference genes (RRN18S, ACTB, GAPDH, HPRT1, PGK1, RPLP0, SDHA, and TFRC) in ccRCC cell lines under normoxic and hypoxic conditions were evaluated   hypoxic conditions [25]. RPLP0 was found to be an optimal reference gene for expression analysis using formalin-fixed paraffin-embedded renal tumors [26]. RPLP0 was also a suitable reference gene to normalize gene expression levels in qRT-PCR experiments in hypoxic and/or hyperglycemic HUVEC cultures [27]. Finally, the SDHA reference gene ranked 3rd in the RankAggreg analysis. This gene was used as a reference for renal tissue sample gene expression evaluation by Hansson et al. [28].

Conclusions
TFRC, RPLP0, and SDHA were considered the most stable genes among the eight evaluated genes using the analysis tools, and they might be recommended for normalization of gene expression data in qPCR in studies of the impact of hypoxia on renal tumor cells.
according to their expression stability and consistency with four different specific tools: geNorm, NormFinder, Best-Keeper, and the ΔCt method. In addition, Rankagreg was used to generate a consensus ranking.
A partial accordance among the four methodologies was observed for the three best normalizer genes (TFRC, SDHA, and RPLP0) selected (Table 2). X18S and ACTB were considered to be the least stable genes. According to the four algorithms, the TFRC reference gene was identified as the most stable gene, followed by SDHA and RPLP0. RPLP0 was the gene that showed the greatest discrepancy among the four algorithms, ranking 1st by geNorm and 4th by BestKeeper (Table 2; Fig. 3). This disagreement is possibly due to the different principles used by the algorithms [21]. To provide comprehensive rankings integrating the four different programs, the RankAggreg, a Monte Carlo cross-entropy algorithm, was employed to reach a consensus among data obtained by the other four algorithms [11,12]. High stability of TFRC has been demonstrated in breast [23] and pancreatic cancers [24]. Furthermore, TFRC was found to be the most suitable reference gene for human umbilical vein endothelial cells (HUVECs) subjected to  (6) 1.56 (7) 2.00 (7) 7 X18S 1.43 (7) 1.55 (7) 1.94 (8) 2.29 (8) 8