HMBS is the most suitable reference gene for RT‑qPCR in human HCC tissues and blood samples
- Authors:
- Published online on: September 17, 2021 https://doi.org/10.3892/ol.2021.13052
- Article Number: 791
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Copyright: © Ahn et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Liver cancer has been predicted to be the sixth most commonly diagnosed cancer and the fourth leading cause of cancer-related deaths worldwide. Among different types of primary liver cancer, hepatocellular carcinoma (HCC) is the most common, comprising 75–85% of cases in adults (1). Ultrasonography and α-fetoprotein (AFP) detection are the most widely employed techniques for the screening and early diagnosis of HCC. However, the sensitivity of ultrasonography for detecting early HCC is only 63%. The clinical diagnostic accuracy of AFP is also inadequate due to its low sensitivity and specificity, since 30–40% of patients with HCC are serum-AFP-negative (2,3). Moreover, biomarkers for the accurate diagnosis of HCC have not yet been reported. Therefore, it is crucial to establish effective biomarkers expressed in both the tissue and blood samples of patients with HCC. Furthermore, it is important to understand the characteristics of HCC through gene expression profiling in biomarker studies. Schulze et al (4) identified 161 putative genetic alterations in HCC using exome sequencing analysis. Using a series of bioinformatics methods, Zhang et al (5) and Gao et al (6) investigated key genes and pathways known to be closely associated with HCC. Moreover, the number of studies evaluating the global gene expression profiles of HCC has markedly increased in recent years. Therefore, identifying stably expressed optimal internal controls is necessary for the accurate gene expression profiling of HCC.
Recent studies have suggested that the measurement of exosome markers is emerging as a novel and efficient method of biomarker quantification as the various molecular constituents of exosomes are closely connected with the original cells from which the exosomes are derived (7–9). Exosomes are membrane-bound nanometer-sized vesicles widely derived from cancer cells, and have been highlighted as notable constituents of intercellular communication (10,11). Therefore, exosomes can be considered as a type of predictive biomarker. The study of gene expression profiles, including those of exosomes, is commonly performed using modalities such as cDNA microarrays, though it is difficult to detect a small number of mRNA copies. As such, due lower economic burden and increased accuracy, reverse transcription-quantitative (RT-q) PCR is often used as an alternative, especially since it is the only technology that can detect mRNA copies at low expression levels (12).
RT-qPCR is a rapid, sensitive and accurate method used to detect gene expression. The technique is based on the normalization of target gene expression within a biological material with any stably-expressed internal reference gene in the same material. Therefore, selection of appropriate reference genes is one of the most important factors for ensuring the accuracy of RT-qPCR analysis. GAPDH, ACTB, TBP, 18S rRNA, HPRT1 and TUBB are commonly used as reference genes in RT-qPCR (13,14). However, previous studies have reported numerous putative reference genes for a wide variety of human tissues and human cell lines under different experimental conditions or environmental factors (15–19). For example, mRNA levels of GAPDH in liver cancer are not always constant, and may vary based on changes in pathology, treatment, or environmental conditions among different tissues or cell lines (20–25). Furthermore, liver cancer is heterogeneous, and therefore, an accurate and precise protocol is required for biomarker validation. When performing RT-qPCR analysis, the selection of the internal reference gene is arguably the most important step. To date, studies determining suitable reference genes for gene expression analysis in serum samples from patients with HCC have been insufficient (20,26,27). Therefore, the aim of the present study was to identify valid internal control genes for the normalization of RT-qPCR studies in both human HCC tissues and blood samples.
Materials and methods
Data processing and expression analysis for reference genes in HCC
The gene expression profiles of the GSE114564 dataset were obtained from the Gene Expression Omnibus database (www.ncbi.nlm.nih.gov.libproxy.ajou.ac.kr/geo/); gene expression profiles were analyzed with the GEO2R tool, using high-throughput sequencing to investigate the expression of 14 candidate reference genes in patients with different liver disease statuses. A heatmap of the reference genes was generated using the heatmap visualization tool Morpheus (https://software.broadinstitute.org/morpheus/). Suitable reference gene candidates for analyzing gene expression in HCC were identified using the list of housekeeping genes at genomics-online (https://www.genomics-online.com/resources/16/5049/housekeeping-genes); the gene accession numbers were obtained through the NCBI BLAST database (Table I). Kruskal-Wallis (non-parametric) followed by Dunn's post hoc test was used to determine statistical significance between non-tumor (normal, chronic hepatitis and liver cirrhosis) and HCC groups (early and advanced HCC). P<0.05 was considered to indicate a statistically significant difference.
The Exocarta database (http://www.exocarta.org) is a manually curated web-based overview of exosomal proteins, RNA and lipids. Exocarta, which is used to evaluate corresponding data, such as exosome characterization and molecular properties, was used to identify reference genes expressed in exosomes (28).
Samples
Sera and tissue samples were collected from the Biobank of Ajou University Hospital, a member of the Korea Biobank Network, between April 2015 and July 2019. Written informed consent was obtained from all study participants. Serum samples were collected from 20 healthy controls and 20 patients with HCC; 20 pairs of HCC tissues with 20 corresponding non-tumor tissue samples were also obtained from patients undergoing tumor resection surgery. These samples were immediately frozen in liquid nitrogen until use. Healthy controls were subjects 18 years of age or older without a history of viral hepatitis or alcoholic liver disease who visited the Ajou University Hospital for the purpose of regular health checkups. HCC was diagnosed based on the American Association for the Study of Liver Diseases practice guideline (29) or histopathologic findings. Subjects were excluded if they exhibited any evidence of other malignancy except HCC or viral coinfections with the human immunodeficiency virus. The patient clinical characteristics are presented in Table SI. All experiments were performed according to the Declaration of Helsinki and the study protocol was approved by the Institutional Review Board of Ajou University Hospital, Suwon, South Korea (approval no. AJRIB-BMR-KSP-16-365 and AJIRB-BMR-SMP-17-189).
Cell culture
To evaluate exosomes, Huh7 cells from the Korean Cell Line Bank were cultured in Dulbecco's modified Eagle's medium (GenDEPOT, LLC) supplemented with 10% fetal bovine serum (Invitrogen; Thermo Fisher Scientific, Inc.) and 1% penicillin-streptomycin (GenDEPOT, LLC). The cells were incubated at 37°C in a humidified atmosphere containing 5% CO2.
Separation of blood sera
Blood samples (5 ml each) were collected from 20 patients directly into serum collection tubes. The whole blood samples were centrifuged at 1,800 × g at room temperature for 10 min, and the resultant sera were aliquoted into 1.5 ml tubes. The samples were then centrifuged at 3,000 × g at 4°C for 15 min to remove cell debris prior to use.
Exosome isolation
Exosomes were isolated from human serum samples using ExoQuick (System Biosciences, LLC) according to the manufacturer's instructions (2).
Transmission electron microscopy (TEM)
Exosome presence and size were confirmed using TEM. Serum exosome samples were fixed with 2% glutaraldehyde and 4% paraformaldehyde for 2 h at room temperature, and then treated with 0.4% uranyl acetate at 4°C for 10 min. Thereafter, the exosomes were observed using a Sigma 500 electron microscope (Zeiss GmbH), and further examined using a NanoSight NS300 instrument (Malvern Panalytical Ltd.) equipped with a 405-nm laser, to determine the size and quantity of the isolated particles. A 60-sec video was generated at a frame rate of 30 frames/s, and particle movement was analyzed using NTA software (version 3.0, Malvern Panalytical, Ltd.). Each sample was analyzed three times and the average number of counts were used.
Western blotting
To validate the expression of exosomal protein markers, serum exosomes and Huh7 cell lysates were lysed in RIPA lysis buffer (Thermo Fisher Scientific, Inc.) containing the Halt protease inhibitor cocktail (Thermo Fisher Scientific, Inc.). Total protein concentration was quantified using the bicinchoninic acid assay method (Thermo Fisher Scientific, Inc.); equal amounts (10 µg) of protein sample were separated with 10% gel, and then transferred onto polyvinylidene difluoride membranes (MilliporeSigma). The membranes were blocked with 5% non-fat milk (in Tris-buffered saline and 0.1% Tween-20) for 1 h at room temperature, and then incubated with the following primary antibodies: Mouse anti-Alix (1:1,000; cat. no. sc53538; Santa Cruz Biotechnology, Inc.), mouse anti-CD81 (1:250; cat. no. 10630D; Invitrogen; Thermo Fisher Scientific, Inc.), rabbit anti-CD9 (1:2,000; cat. no. ab92726; Abcam) and mouse anti-BiP/GRP78 (1:1,000; cat. no. 610979; BD Biosciences). The resulting immune complexes were then probed using secondary horseradish peroxidase-conjugated anti-rabbit (cat. no. BR170-6515; Bio-Rad Laboratories, Inc.) or anti-mouse (cat. no. BR170-6516; Bio-Rad Laboratories, Inc.) antibodies. Luminescence was observed using the ChemiDoc™ Imaging System (Bio-Rad Laboratories, Inc.).
Primer design
The NCBI BLAST database (https://blast.ncbi.nlm.nih.gov/Blast.cgi) was used for primer design. All primers were designed with target amplicons <200 bp in length. The primer sequences are listed in Table II. The specificity of these primer sets was confirmed using melting curve analysis (Fig. S1A).
RNA extraction and cDNA synthesis
Total RNA from the selected tissue samples was isolated using QIAzol reagent (Qiagen GmbH), and serum RNA was extracted from the selected blood samples using the TRIzol® LS reagent (Invitrogen; Thermo Fisher Scientific, Inc.). Exosomal RNA was isolated from serum using the SeraMir™ Exosome RNA Amplification kit (System Bioscience, LLC) according to the manufacturer's instructions. RNA concentration was quantified using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Inc.). Following the manufacturer's instructions, serum RNA (500 ng) was reverse transcribed into cDNA using the PrimeScript™ RT Master Mix (Takara Bio, Inc.), and exosomal RNA (50 ng) was reverse transcribed using the miScript II RT kit (Qiagen GmbH).
qPCR
qPCR was performed using the amfiSure qGreen Q-PCR Master Mix (GenDEPOT, LLC) according to the manufacturer's instructions, on the CFX Connect Real-Time PCR Detection System (Bio-Rad Laboratories, Inc.). Each sample was prepared in a total volume of 10 µl, containing 4 µl diluted cDNA template, 5 µl amfiSure qGreen Q-PCR Master Mix (GenDEPOT, LLC), and 500 nM of each primer. The PCR conditions were as follows: 95°C for 2 min, 40 cycles of 95°C for 15 sec, 58°C or 60°C for 34 sec, and 72°C for 30 sec, followed by a dissociation stage of 95°C for 10 sec, 65°C for 5 sec, and 95°C for 5 sec. Relative gene expression levels were calculated using the 2−ΔΔCq method (30). All PCR reactions were performed in triplicate.
Analysis of reference gene expression stability
The stability of candidate reference gene expression was evaluated using the Excel-based software BestKeeper (https://www.gene-quantification.de/bestkeeper.html). All data processing was based on crossing point (CP). The stability rankings of the individual genes were determined according to the lowest standard deviations.
Statistical analysis
All experiments were performed independently in triplicate. Results are presented as the mean ± standard deviation or standard error of the mean. Statistical differences between groups were analyzed using paired Student's t-test for the tissue samples or Welch's t-test for the serum and serum exosome samples. All statistical analyses were performed using GraphPad Prism 5.0 (GraphPad Software Inc.) and P<0.05 was considered to indicate a statistically significant difference.
Results
Selection of candidate reference genes for HCC marker studies
Expression levels of the 14 selected reference genes, analyzed using the next-generation sequencing multistage HCC RNA seq dataset GSE114564, are represented as a heat map based on liver disease status (Fig. 1A and Table SII). Differences in expression levels between the control group and the HCC group were identified in patients with different liver disease statuses. From the 14 genes, ACTB, GAPDH, HMBS, PPIA, RPLP0 and TBP were selected, as they did not show a statistically significant difference between the control and HCC groups (Fig. 1B and Table SII). For the exosome samples, the Exocarta database (http://www.exocarta.org/) was used to identify suitable reference genes from the five selected genes. TBP, which is not registered in the Exocarta database, was excluded from the final selected candidates.
The ACTB gene performs key functions of the cytoskeleton, such as cell motility and contraction (31). The GAPDH gene has glyceraldehyde-3-phosphate dehydrogenase and nitrosylase activities, and is involved in glycolysis and nuclear function. It also regulates the organization and assembly of the cytoskeleton (32,33). The HMBS gene supports the generation of hydroxymethylbilane synthase, and is indirectly involved in the production of heme (34). The PPIA gene catalyzes the cis-trans isomerization of proline imidic peptide bonds in oligopeptides, and is involved in apoptosis signaling through NF-κΒ, AKT1 and BCL2 upregulation (35,36). The RPLP0 gene encodes a ribosome, an organelle that catalyzes protein synthesis, is composed of a small 40S and a large 60S subunit, and is associated with pathologies including Chagas disease (37). Based on these results, the expression levels of 6 genes exhibited no statistical significance between the control and HCC groups. Among them, 5 genes were expressed in exosomes using the Exocarta database. The present study subsequently identified the molecular characteristics of those 5 candidate reference genes.
Primer specificity of candidate reference genes
Following primer design using NCBI BLAST, and confirmation of specificity using melting curve analysis, all primers were observed as a single peak (Fig. S1A). The most suitable annealing temperature and mean Cq values were then selected (Fig. S1B).
RT-qPCR Cq values of candidate reference genes
Pure exosomes were identified by isolation from serum samples and characterization using TEM analysis (Fig. S2A). Furthermore, positive and negative protein markers of extracellular vesicles were confirmed through western blotting (Fig. S2B). Next, RT-qPCR analysis was used to evaluate the expression levels of the selected genes in the control and HCC groups. All samples were analyzed in triplicate, and Welch's t-test was performed with the average Cq values for each group. First, Cq values of the five selected reference genes were calculated in 20 healthy and 20 HCC tissues. The expression levels of PPIA (P=0.0076) showed the lowest significant difference between the control and HCC tissue groups, and the expression levels of ACTB (P=0.0011), GAPDH (P=8.92E-05), HMBS (P=0.0003), and RPLP0 (P=0.0003) indicated a more significant difference. (Fig. 2A). Next, Cq values of the selected reference genes in serum and serum exosome samples were estimated. Unlike the tissue samples, the expression levels of ACTB (P=0.0837), HMBS (P=0.0904), PPIA (P=0.2238) and RPLP0 (P=0.8058) showed no significant difference. However, similar to the tissue the samples, GAPDH (P=0.0233) indicated a significant difference in expression level between the control and HCC groups (Fig. 2B). Finally, the expression levels of the five reference genes were confirmed in exosomal RNA isolated from patient serum. Of these five genes, HMBS (P=0.0404) exhibited the least significantly different expression between the control and HCC serum exosome groups; however, the expression of ACTB (P=0.0001), GAPDH (P=0.0001), PPIA (P=0.0001) and RPLP0 (P=0.0001) indicated a substantially significant difference (Fig. 2C). Therefore, among the five reference genes identified, HMBS exhibited the least significant difference in expression between the control and HCC groups for blood samples (both serum and serum exosome).
Identification of the most suitable reference genes in HCC studies
BestKeeper analyses of the tissue, blood and serum samples were performed to investigate the stability of the five reference genes. Descriptive statistics of the derived CPs were calculated for each reference gene. CPs are direct results obtained from the threshold line crosses fluorescence plots for each of the samples. All CP data for all groups were compared throughout the study (38). Stability rankings for each sample were evaluated according to the coefficient of variance values of the BestKeeper analyses. As such, the most stable reference gene was identified to be HMBS. GAPDH, which is a commonly used reference gene, was found to be the least stable (Fig. 3A). In all 40 tissues and blood samples, HMBS had the most consistent CP values among five reference genes (Fig. 3B). The stability values obtained from the BestKeeper analyses are represented in Fig. 3C. Also, when performing NormFinder analysis (another tool for calculation of stability), HMBS exhibited the highest stability in tissue samples among the five candidate reference genes (data not shown). In conclusion, HMBS was selected as the most stable reference gene in tissue, serum and serum exosomes based on Bestkeeper, a software that identifies the suitable reference gene (39). Additionally, NormFinder analysis revealed that HMBS is the most stable reference gene for tissue samples.
In the present study, we found that HMBS is the most suitable reference gene for blood and tissue samples in HCC. This study will be helpful for future studies by finding suitable reference genes for RT-qPCR, used to detect gene expression widely. In this respect, we suggest that the expression stability of reference genes should be validated to obtain accurate and reliable results.
Discussion
Various studies have suggested biomarkers for liver cancer, and the effort to identify additional markers is ongoing (40). Numerous methods, including immunohistochemistry, ELISA and western blotting, have previously been used in such studies (41,42). However, these methods are relatively time-consuming and expensive. Similarly, droplet digital PCR technology (which can be used for extremely low target quantitation) and microarrays (that can measure the absolute expression of genes in cells or tissues so that can perform precise analyses) are widely-used newer technologies, but the cost of the associated instruments and reagents is higher (43,44). In this respect, RT-qPCR has been the most cost-effective and widely-used technique for biomarker analysis studies, and can be used for analyzing both tissue and blood samples (45). However, despite the uncertainty surrounding gene normalization, RT-qPCR is still used as one of the most accurate methods for transcript quantification, and since liver cancer is heterogenous, a suitable reference gene is required for this method (25,46). Therefore, an accurate protocol for the validation of biomarker studies needs to be developed.
The selection of an internal control gene for normalizing target gene expression is an important consideration for RT-qPCR. In particular, since exosomes are presently and commonly used to identify biomarkers in cancer, the identification of a suitable reference gene for exosome detection is also required (47). Gorji-Bahri et al (48) validated reference genes in pooled cancer exosomes, and Dai et al (49) revealed that GAPDH, YWHAZ and UBC were the most stably expressed reference genes in exosomal RNA isolated from liver and breast cancer cell lines. However, reference genes in HCC tissues and blood were not evaluated by in vitro experiments and another software analysis to determine stable housekeeping genes. The aim of the present study was to identify the most reliable reference genes in HCC tissue and blood samples using RT-qPCR. Therefore, 14 candidate, commonly used reference genes, were selected through a systematic literature search.
Previous studies have reported that ACTB is upregulated in liver cancer tissues and is therefore unsuitable for the normalization of RT-qPCR (50). Furthermore B2M was expressed at different levels depending on hepatitis infection status (25). Barber et al (51) indicated that normalization is unstable for a single gene, as the between-tissue variation for GAPDH can be substantial (51). GUSB was not suitable as a reference gene in RT-qPCR study for lung squamous-cell carcinoma (52). Furthermore, HMBS has been verified as suitable for the normalization of gene expression data among tumor tissues in HCC (23). In addition, and as reported by Ceelen et al (53), gene expression stability level was analyzed in the human HepaRG cell line using three algorithms (geNorm, BestKeeper, NormFinder). The results revealed that TBP and HMBS exhibited the highest stability (53). Also, in tumor tissues from male HCC patients with hepatitis B infection and cirrhosis, CTBP1 was the most stable reference gene, and HMBS ranked third (24). HPRT1 has been validated as the most suitable reference gene for heart, liver and thymus samples (54), and PGK1 is known to be suitable in small bowel studies, while PPIA is more optimal in large bowel studies (55). RPLP0 expression in breast, normal and adjacent tissues was examined using geNorm and NormFinder software, and RPLP0 was consequently found to be the least stable gene (56). Through geNorm and BestKeeper analyses, RPL13A was selected as the most stable gene in the granulosa cells of healthy women, as well as those of patients with polycystic ovarian syndrome (57), and was suitable for both healthy breast and breast tumor tissues (58). In addition, Ohl et al (59) identified SDHA and TBP as reference genes for relative gene quantification in bladder cancer, and TFRC was reported to be one of the optimal set of reference genes for RT-qPCR analysis in HUVECs under oxidative stress (60). Finally, Bruce et al (61) found that YWHAZ was stably expressed as a reference gene in studies of non-alcoholic fatty liver disease.
Next, the expression of the 14 reference genes was confirmed using human multistage HCC transcriptome data. Among them, five candidate reference genes that did not show any statistically significant difference between the control and HCC groups, regardless of liver disease status, were selected. Primers were designed for the five candidate reference genes using the NCBI BLAST database, and primer efficiency was evaluated using RT-qPCR analysis. The five reference genes were then evaluated in tissue, serum and serum exosome samples; the characteristics of serum exosomes were observed using TEM, and exosome markers were confirmed using western blotting. RT-qPCR analysis was used to measure the Cq values of the five candidate reference genes in 40 tissue samples (20 paired healthy tissues and 20 tissues from patients with HCC) and 40 blood samples (20 healthy controls and 20 patients with HCC). HMBS showed the least significant difference in Cq value in each group. Moreover, BestKeeper analysis was used to evaluate the stability of the reference genes by calculating the standard deviation of the Cq values. The results indicated that HMBS was the most stable reference gene in both tissue and blood samples. Thus, an in vitro study using RT-qPCR confirmed that HMBS maintained a constant expression level among the five candidate reference genes in HCC blood samples. Furthermore, for the serum exosome group, BestKeeper analysis revealed HMBS to be the most suitable reference gene. Based on these results, HMBS is suggested as a suitable normalization gene for RT-qPCR in HCC studies. However, further validation via other techniques (i.e. droplet digital PCR or NanoString) may be required in the future, although experiments in the present study were repeated in the same sample and validated within a constant range. Also, the current study was limited by the small number of samples, thus in future studies, it will be necessary to reduce error by increasing the sample population size.
Supplementary Material
Supporting Data
Acknowledgements
The biospecimens and data used for the present study were provided by the Biobank of Ajou University Hospital, a member of the Korea Biobank Network.
Funding
The present study was supported by grants from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea (grant no. HR21C1003), as well as the Bio and Medical Technology Development Program of the National Research Foundation (grant nos. NRF-2017M3A9B6061509 and NRF-2019R1C1C1007366), funded by the Korean government (MSIT).
Availability of data and materials
The datasets analyzed during the current study are available in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, accession no. GSE114564.
Authors' contributions
JWE, JYC and HRA made substantial contributions to the conception and design of the present study. HRA and HJC performed the in vitro experiments. JAS, MGY and GOB acquired and analyzed the data. SSK, DY and JHY interpreted all the datasets in the present study. HJC and SSK drafted the initial manuscript and critically revised it for important intellectual content. JWE and JYC confirmed the authenticity of all the raw data. All authors have read and approved the final manuscript.
Ethics approval and consent to participate
The present study was approved by the Institutional Review Board of Ajou University Hospital, Suwon, South Korea (approval nos. AJRIB-BMR-KSP-16-365 and AJIRB-BMR-SMP-17-189). Written informed consent was obtained from each patient.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Glossary
Abbreviations
Abbreviations:
|
AFP |
α-fetoprotein |
|
CP |
crossing point |
|
Cq |
quantification cycle |
|
EV |
extracellular vesicle |
|
HCC |
hepatocellular carcinoma |
|
LC |
liver cirrhosis |
|
RT-qPCR |
reverse transcription-quantitative PCR |
|
TEM |
transmission electron microscopy |
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