Next Article in Journal
Characteristics and Comparative Analysis of the Special-Structure (Non-Single-Circle) Mitochondrial Genome of Capsicum pubescens Ruiz & Pav
Next Article in Special Issue
New Genetic Markers of Skin T-Cell Lymphoma Treatment
Previous Article in Journal
Utilizing Massively Parallel Sequencing (MPS) of Human Leukocyte Antigen (HLA) Gene Polymorphism to Assess Relatedness in Deficiency Parentage Testing
Previous Article in Special Issue
A Comprehensive Metabolism-Related Gene Signature Predicts the Survival of Patients with Acute Myeloid Leukemia
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 15
Issue 2
10.3390/genes15020151
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Validation of Endogenous Control Genes by Real-Time Quantitative Reverse Transcriptase Polymerase Chain Reaction for Acute Leukemia Gene Expression Studies

by
Flávia Melo Cunha de Pinho Pessoa
1,†,
Vitória Beatriz de Jesus Viana
2,†,
Marcelo Braga de Oliveira
2,
Beatriz Maria Dias Nogueira
1,
Rodrigo Monteiro Ribeiro
3,
Deivide de Sousa Oliveira
1,3,
Germison Silva Lopes
4,
Ricardo Parente Garcia Vieira
5,
Manoel Odorico de Moraes Filho
1,
Maria Elisabete Amaral de Moraes
1,
André Salim Khayat
2,
Fabiano Cordeiro Moreira
2,‡ and
Caroline Aquino Moreira-Nunes
1,2,6,*,‡
1
Department of Medicine, Pharmacogenetics Laboratory, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza 60430-275, CE, Brazil
2
Department of Biological Sciences, Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil
3
Department of Hematology, Fortaleza General Hospital (HGF), Fortaleza 60150-160, CE, Brazil
4
Department of Hematology, César Cals General Hospital, Fortaleza 60015-152, CE, Brazil
5
Department of Hematology, São Vicente de Paulo Maternity Hospital, Barbalha 63180-000, CE, Brazil
6
Central Unity, Molecular Biology Laboratory, Clementino Fraga Group, Fortaleza 60115-170, CE, Brazil
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
‡
These authors also contributed equally to this work.
Genes 2024, 15(2), 151; https://doi.org/10.3390/genes15020151
Submission received: 7 December 2023 / Revised: 9 January 2024 / Accepted: 22 January 2024 / Published: 24 January 2024
(This article belongs to the Special Issue Research Progress in Hematological Malignancies: A Molecular Genetics Perspective)
Browse Figures
Versions Notes

Abstract

:
Reference genes are used as internal reaction controls for gene expression analysis, and for this reason, they are considered reliable and must meet several important criteria. In view of the absence of studies regarding the best reference gene for the analysis of acute leukemia patients, a panel of genes commonly used as endogenous controls was selected from the literature for stability analysis: Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), Abelson murine leukemia viral oncogene human homolog 1 (ABL), Hypoxanthine phosphoribosyl-transferase 1 (HPRT1), Ribosomal protein lateral stalk subunit P0 (RPLP0), β-actin (ACTB) and TATA box binding protein (TBP). The stability of candidate reference genes was analyzed according to three statistical methods of assessment, namely, NormFinder, GeNorm and R software (version 4.0.3). From this study’s analysis, it was possible to identify that the endogenous set composed of ACTB, ABL, TBP and RPLP0 demonstrated good performances and stable expressions between the analyzed groups. In addition to that, the GAPDH and HPRT genes could not be classified as good reference genes, considering that they presented a high standard deviation and great variability between groups, indicating low stability. Given these findings, this study suggests the main endogenous gene set for use as a control/reference for the gene expression in peripheral blood and bone marrow samples from patients with acute leukemias is composed of the ACTB, ABL, TBP and RPLP0 genes. Researchers may choose two to three of these housekeeping genes to perform data normalization.

1. Introduction

The standard polymerase chain reaction (PCR) was developed in the 1980s by Kary Mullis and resembles an in vitro elementary form of DNA replication, mimicking the physiological process that occurs in living organisms. Up to the present time, many variations of this technique have been developed, such as Nested-PCR, reverse transcriptase PCR (RT-PCR), real-time quantitative reverse transcriptase PCR (RT-qPCR) and others [1,2,3].
Real-time quantitative reverse transcriptase PCR (RT-qPCR) technology has revolutionized the detection landscape in every area of molecular biology. This technique is based on the conversion of an RNA template to complementary DNA (cDNA), followed by fluorescent reporter dye detection to measure the amplification at each PCR cycle with higher sensitivity and specificity [1,3,4,5,6,7,8].
The RT-qPCR technique is considered as the most reliable and most accurate method of diagnosis [9,10,11,12,13,14,15,16,17]. However, numerous failures can occur during the RT-qPCR process, which can lead to the misinterpretation of results and incorrect conclusions. That is why the application of an appropriate normalization method is an absolute necessity to achieve reliable results [18,19,20,21]. There are many ways to normalize RT-qPCR data, such as normalization with genomic DNA, total RNA, artificial molecule (spike) and reference genes [4,13,20,22,23,24].
The use of reference genes is considered one of the most effective methods for the normalization step of the RT-qPCR technique [13]. Reference genes may be used as an internal reaction control for gene expression analysis, and for this reason, they are considered reliable and must meet several important criteria. A good reference gene is unaffected by experimental factors and shows minimal variability between tissues and physiological states of the organism. Therefore, it is beneficial to choose a reference gene that shows a similar threshold cycle in studies within different genes of interest [17,18,25,26,27,28,29,30,31,32,33].
Basic metabolism genes often present the perfect fulfillment of these conditions since they are expressed at a stable and constant level and are involved in essential cell processes. Because of that, they can be called housekeeping genes (HKGs). In the past, the validation process was often avoided and HKGs were used due to a common belief that they are characterized by constant expression levels regardless of their conditions and origins. As awareness of the complex expression regulation networks in the cell function grew, this statement began to be undermined and experimental confirmation of the stability of candidate genes is now a standard requirement [14,15,34,35,36,37,38,39,40,41].
A relevant number of traits can have different impacts on gene expression, such as tissue type, developmental stage, related species, abiotic stress, diseases, infections, alternative splicing, and tumors. Therefore, it is observed that the need for reference gene validation has been underlined several times in different articles involving different types of diseases, especially in the oncological area, where types of cancer have different physiologies and the involvement of several genetic and external factors. Since cancer encompasses so many variables, molecular studies are required to determine comparative genes for expression analyses, which is imperative for adequate normalization, whose task is to compensate for PCR variations resulting from basic difficulties inherent to the method. Therefore, the ideal is to find genes with undoubtedly stable expression for each disease/condition model [42,43,44,45,46,47,48,49,50].
Several studies have already been carried out aiming to validate the reference genes for different types of cancer, allowing the normalization of gene expression analyses in future studies. McNeil et al. [47] validated the MRPL19 and PPIA genes as endogenous controls for analysis of breast cancer patients. For the analysis of endometrial cancer, the most stable reference genes were RPL30, MT-ATP6 and ACTB according to research conducted by Ayakannu et al. [51].
There are still no concrete published studies on the validation of reference genes using the RT-qPCR technique for gene expression analysis involving acute leukemia patients. Our group chose to perform validation on both peripheral blood and bone marrow samples, as these are the two sample types used in our laboratory for the detection of genetic alterations in acute leukemia patients through the RT-qPCR technique. This validation’s importance is due to the fact that this methodology is widely used at these patients’ time of diagnosis but also for the detection of minimal residual disease (MRD), with the ability to identify if they are having a good treatment response and/or if they are disease free at the end of the chemotherapy protocol. Therefore, it is extremely necessary to perform a reference gene normalization in this technique, so it is as accurate as possible.
Considering the importance of using the reference gene as a control for gene expression analysis and the lack of studies in the literature that validate the best reference genes to be used for the analysis of patients with acute leukemias, this work aimed to perform this validation and normalize expression assays with peripheral blood and bone marrow samples from these patients.

2. Materials and Methods

2.1. Biological Samples

All adult patients with AML (24) and ALL (25) participating in this study were treated at the Fortaleza General Hospital (Fortaleza, CE, Brazil) and sought care due to suspicion of the disease, i.e., samples from all patients were collected during the diagnostic phase. Samples were collected prospectively from July 2021 to July 2022. Control group samples (15) were also collected during the same period. Pediatric ALL samples (25) were collected from 2012 to 2023 at Octávio Lobo Children’s Hospital (Belém, PA, Brazil), also in the diagnostic phase (Table 1). The diagnosis of these patients was made in the participating hospitals and local blood centers through tests such as myelogram, immunophenotyping and karyotyping.
Patients’ samples were collected in ethylenediaminetetraacetic acid (EDTA) collection tubes at the time of diagnosis and were packed in a thermal case at 2–4 °C for transport to the laboratory for later processing. After collection, the samples were immediately processed, going through the buffy coat separation for RNA extraction and conversion into cDNA. The cDNA samples were stored until the end of the collections, in July 2022, in a freezer (−2 °C to −8 °C) for RT-qPCR assays.
This study was approved by both the Ethics Committee of the Ophir Loyola Hospital (approval number: 2,798,615) and the Ethics Committee of the General Hospital of Fortaleza (approval number 4,798,575). Informed written consent was obtained from the patients or the patients’ legal guardians, and all methods were carried out in accordance with Helsinki guidelines and regulations.

2.2. RNA Extraction and Reverse Transcription of RNA to cDNA

RNA from samples was extracted with TRIzol Reagent® (Invitrogen, Waltham, MA, USA) according to the manufacturer’s instructions. From 20 μL of RNA, the cDNA was synthesized using a High-Capacity cDNA Reverse Transcriptase kit (Life Technologies, Carlsbad, CA, USA) to convert the extracted and purified RNA to cDNA. The conversion step was performed on a Veriti® Thermal Cycler (Applied Biosystems, Foster City, CA, USA). After this step, the samples were stored in a freezer at −20 °C until use for analysis.

2.3. Identification of Gene Expression by Quantitative Real-Time Polymerase Chain Reaction (qPCR)

Quantitative real-time polymerase chain reaction was performed to evaluate the endogenous expressions that are commonly used in studies of general gene expression. Information about the genes and probes chosen for the study is shown in Table 2. Regarding the protocol, for each sample, the following were used: 1 μL of cDNA, 0.5 μL of each primer/probe, 5 μL of TaqMan® Gene Expression Master Mix (Life Technologies, Carlsbad, CA, USA) and 3.5 μL of ultra-pure water. RT-qPCR was performed for the following genes shown in Table 2, and each sample was analyzed in triplicate for experimental and technique validation, according to the international standards for evaluation of gene expression by real-time PCR [46,52].
The gene-expression levels were based on absolute and relative analyses and calculated using the 2−∆∆CQ (delta delta cycle quantification) method, using the healthy samples as the calibrator/control [46,52]. Fold change data are represented as mean ± standard deviation of three independent experiments.

2.4. Data Analysis Based on the Gene Expression Omnibus (GEO) Database

We used the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/gds (accessed on 12 July 2023)), a public repository of high-throughput gene expression data, as a reference model to profile ACTB, ABL, GAPDH, HPRT, TBP and RPLP0 expression in acute leukemias. The spreadsheet created with all the Ct (cycle threshold) data of all endogenous genes and all the analyzed samples is available as a Supplementary file. The file contains 4 spreadsheets: metadata template, matrix non-normalized template, matrix normalized template and fold change template. The metadata template spreadsheet contains the description of all samples used in the study, such as the numbering of each patient, leukemia type and sample type information. The matrix non-normalized template spreadsheet reports all the raw Ct averages of each analyzed sample, where the rows correspond to the reference genes used in the research and the columns correspond to the samples tested. The matrix normalized template spreadsheet contains target gene signals normalized to housekeeping genes, e.g., 2−ΔCt, where −ΔCt = −(Ct_Target − Ct_HKG), where the rows correspond to the reference genes used in the research and the columns correspond to the samples tested. And finally, the last spreadsheet, fold change template, reports the fold change data, e.g., SAMPLEtest target gene signal normalized to housekeeping gene (2−ΔCttest)/SAMPLEcontrol target gene signal normalized to HKG (2−ΔCtcontrol), where the rows correspond to the reference genes used in the research and the columns correspond to the samples tested.

2.5. Statistical Analysis

The stability of candidate reference genes was analyzed according to three statistical methods of assessment, namely, the Delta Ct method, the estimation of the intra- and intergroup variation (NormFinder) [53] and pairwise comparison (GeNorm) [43]. In addition, a deeper and more individual analysis was carried out using a Kruskal–Wallis (KKW) test in the R software [54], and comprehensive ranking orders of these candidate genes were available from the four methods.
GeNorm uses a pairwise comparison-based model to select, from a panel of candidate HKGs, the gene pair showing the least variation in expression ratio across the samples. It calculates a measure of gene stability (M) of each gene based on the average pairwise variation between all tested genes. Genes with the lowest M values are those demonstrating the most stable expression. This calculation is based on the principle that it stepwise excludes the gene with the highest M value. In addition, GeNorm involves a cut-off value of 0.15, below which the inclusion of an additional reference gene is not required. This cut-off value of 0.15 is suggested by Vandesompele et al. (2002) when multiple control genes are used as a normalization factor [43].
NormFinder uses a model-based approach to estimate not only the overall expression variation of the candidate normalization genes but also the variation between the sample subgroups of the sample set. The candidates with the lowest intergroup and intragroup variations give the lowest S stability value and are therefore ranked higher as more stable [53].
The R software (https://www.r-project.org, accessed on 14 September 2023) allows a deep analysis. To evaluate the potential endogenous genes, three analyses were performed: (i) using all samples, (ii) using only peripheral blood (PB) samples and (iii) using only bone marrow (BM) samples.
The Kruskal–Wallis (KKW) test was performed for each gene to identify significant differences among the four groups (control, AML, ALL and ALL_Ped). Additionally, the sum of the square differences between each condition mean (Ct) and standard deviation and the gene mean (Ct) and standard deviation were calculated. These values indicate the mean and standard deviation variation among different groups. Greater values indicate more variance among groups [55,56].
To select the best endogenous gene set, we utilized an interactive methodology involving multiple steps: (i) we calculated the endogenous mean for each sample; (ii) we excluded outlier samples (|MeanCt| > 2); (iii) the Kruskal–Wallis test was applied to assess variations in the endogenous means across different conditions; (iv) in cases where significant differences were identified among conditions or groups, the least stable endogenous gene was systematically removed from the analysis and the excluded samples removed in step (ii) were then reintegrated, and the process recommenced from step (i); (v) if there are no significant differences among groups, the survival endogenous gene set is selected (Figure 1).
To identify the less stable endogenous gene, the following criteria were applied: (i) gene significant differences among conditions; (ii) standard deviation values; (iii) sum of the square differences of the mean and standard deviation between conditions/groups and the gene; (iv) NormFinder and GeNorm stability values.
To evaluate the variation of potential endogenous genes in ALL and AML groups in different origin samples (PB and BM), a two-way ANOVA ranking was used. The statistical significance threshold was set at p-value < 0.05 for all analyses [57,58,59].

3. Results

In order to identify the best reference genes for gene expression studies in peripheral blood and/or bone marrow samples of acute leukemia patients, a qPCR assay based on TaqMan detection for the expression analysis of the six selected genes (ACTB, ABL, GAPDH, HPRT, TBP and RPLP0) was used.
The gene ranking according to GeNorm indicates that TBP is the gene with the most stable expression for adult ALL and AML patients, with stability values of 0.098 and 0.073, respectively, and HPRT is the gene with the most stable expression for both pediatric ALL patients and normal samples, with a stability value of 0.059 and 0.040, respectively (Table 3).
NormFinder identified a different result from GeNorm, probably because it was possible to identify the different sample groups during the analysis. The ACTB gene was the most stable gene followed by the RPLP0 gene, which had stability values of 0.47 and 0.52, respectively. The least stable reference gene candidate was GAPDH with a stability value of 1.78 (Table 4).
The R software allowed a deep analysis, as this tool is capable of performing intergroup and intragroup analyses, ensuring a more adequate evaluation of the endogenous genes. Table 5 summarizes the results obtained through the analyses performed in the R software, including Kruskal–Wallis results, gene standard deviation, sum of mean square difference and sum of standard deviation square difference. Among all the results, we highlight that it was possible to observe that the ACTB gene presented a very good performance with low mean and standard deviation values (1.52 and 1.69, respectively), as well as little difference in the standard deviation between all groups (Table 5).
The analysis made in this study showed that the GAPDH and HPRT genes could not be classified as good reference genes, considering that they presented a high standard deviation and great variability between groups, indicating low stability. From these results, it can be considered that GAPDH and HPRT do not behave as good endogenous genes for the expression analysis of acute leukemia samples; therefore, they should not be used.
In addition, it was possible to identify that the endogenous gene set composed of ACTB, ABL, TBP and RPLP0 demonstrated good performances and stable expressions between the analyzed groups. The expression levels for many of these genes fluctuate dramatically both within and across datasets (Figure 2). The origin of the sample (peripheral blood or bone marrow) may also have an influence on the reference gene expression (Figure 3).
As most endogenous gene validation studies report, we have observed, in our study, an expression variation of endogenous candidates in the different types of samples tested (bone marrow and peripheral blood). However, in general, the expression variation observed was not as high. In Figure 3, it is possible to identify that the endogenous genes ACTB and ABL are the two most suitable for studies that rely on both types of samples since there is a lower variation in expression between bone marrow and peripheral blood. If the study relies only on peripheral blood samples, researchers can choose two or three endogenous genes from the ACTB, ABL, TBP and RPLP0 gene set.

4. Discussion

The normalization of gene expression in a group of samples is necessary to validate the stability of the expression of a reference gene under experimental conditions, such as sample type and disease type, for example, before its use in studies. In the literature, it is possible to observe that most works use the standardization programs GeNorm and NormFinder to search for the most appropriate reference genes for their studies. However, although these tools are capable of analyzing endogenous genes in different groups to try to identify specific behaviors for each of them, they do not conduct it in a completely satisfactory way [51,60,61,62,63].
In studies of normalization and validation of endogenous genes to disease models, the statistical test must be performed between endogenous Ct-case and endogenous Ct-control. If there is a large difference between both Cts, the use of the endogenous gene in question is not feasible, since it is being influenced by the conditions. Furthermore, it was observed during our statistical analysis that it is necessary to remove assays whose endogenous gene presents outlier behavior (endogenous Ct > 2 × SD of the average of endogenous Cts). It is of great importance to verify whether there is a correlation between endogenous Ct and target Ct, considering that it may characterize experimental bias.
Since this work had four distinct groups, pediatric ALL, adult ALL, adult AML, and control, checking the data through these tools, it was possible to perceive that when they were analyzed all together, there was a loss of intragroup variability; on the other hand, when the analyzes were performed separately by group, there was a loss of intergroup variability. Thus, it was not possible to actually elect the most appropriate endogenous gene using these tools for the proposed study. For this reason, additional analyses were performed using the R software [43,53,54,64].
Currently, the most frequently used reference genes for general expression studies are B-actin (ACTB), glyceraldehyde-3- phosphate dehydrogenase (GAPDH) and hypoxanthine-guanine phosphoribosyl transferase 1 (HPRT1) [13,65,66,67,68,69].
The GAPDH gene is involved in many cell processes such as membrane transport and membrane fusion, microtubule assembly, nuclear RNA export, protein phosphotransferase/kinase reactions, DNA replication and DNA repair. With this in mind, GAPDH expression would be expected to vary as it has a diverse range of functions unrelated to its glycolytic activity [70].
This study’s analysis determined that the GAPDH gene presented the most unstable behavior between the analyzed endogenous genes. Our findings agree with several other studies that have scrutinized the stability of the commonly known reference gene GAPDH and have demonstrated that it should be used with caution as its expression varied considerably, and it was consequently unsuitable as a reference gene in some cases [12,70,71,72,73]. However, some studies have shown different results regarding the expression stability of GAPDH, as it was identified as one of the best housekeeping genes in the analysis of a great variety of tissue type [74,75,76].
The HPRT gene is also widely used as an endogenous control in many studies of gene expression in different types of cancer. This gene is found in all cells as a soluble cytoplasmic enzyme. Although HPRT is found in all types of somatic cells, significantly higher levels are found in the central nervous system [77,78]. Many studies have shown that the HPRT gene presents the behavior of a good reference gene, both used alone and associated with other genes such as TBP and GAPDH, among others [11,71,79,80,81,82].
A study by Jacques B. de Kok and colleagues chose HPRT as the reference gene with the highest accuracy when used as a single normalization gene in several types of solid tumors, such as colorectal, breast, prostate, skin, and bladder tissues, with tumors ranging from noninvasive to metastatic carcinomas. In this study, the authors analyzed 13 housekeeping genes (LRP, ACTB, CYC, GAPDH, PGK, B2M, BGUS, HPRT, TBP, TfR, PBGD, ATP6 and rRNA) in order to elect which one was most stable [83].
However, our study also identified that the HPRT gene presents low stability in samples of patients with acute leukemias and is not indicated as a suitable endogenous gene. Some other studies have also reported that the HPRT gene exhibits high expression variability and have classified it as an inadequate reference gene, corroborating the data found in this work [84,85].
By using the previously described method, the endogenous genes GAPDH and HPRT were removed from the analysis due to poor performance. Both genes presented high standard deviation and high variability between the analyzed groups, characterizing bad behavior for a reference gene. Therefore, our study proposes the set of endogenous genes ACTB, ABL, TBP and RPLP0 as the most appropriate for the analysis of expression assays of acute leukemia samples.
The ACTB gene is an abundant and highly conserved cytoskeleton structural protein that is widely distributed in all eukaryotic cells and that plays critical roles in multiple cell processes. It is usually regarded as a constitutive housekeeping gene, assuming that its expression is normally unaffected by most experimental or physiological conditions. Therefore, ACTB has been widely used as a reference gene for expression analysis in many types of tissues [10,35,86,87].
In this study, the ACTB gene was reported as one of the most stable endogenous genes analyzed, presenting low mean and standard deviation values intragroup and between all four groups. This gene has also been reported as a good reference gene in other studies of different types of cancers, but especially in breast cancer expression analysis [47,88,89,90].
However, the ACTB gene was found to be differentially expressed in many different types of cancer such as liver, melanoma, renal, colorectal, gastric, pancreatic, esophageal, lung, breast, prostate, ovarian cancers, leukemia, and lymphoma under certain conditions. This suggests that it might be an unsuitable endogenous gene for expression analysis [9,87,91,92]. Some studies reported ACTB as an unsuitable reference gene [67,71,73,93,94].
In a recent study, Gupta and colleagues performed an identification and validation of the optimal reference genes for standardizing the gene expression profiling diagnostic panel of Ph-like B-lineage acute lymphoblastic leukemia. They reported that EEF2, GAPDH and PGK1 are optimal and stable endogenous genes for specific gene quantification in Ph-like ALL cases as compared to ABL1, ACTB, B2M, RNA18S, GUSB and TBP [95].
Two other genes that showed stable gene expression were two protein-coding genes, ABL and TBP, respectively, according to mean values and standard deviation. The ABL gene is an oncogene likely associated with many roles of cell cycle regulation, stress responses, integrin signaling and neural development [96,97,98]. The TATA-binding protein (TBP), in turn, has been considered a universal transcription factor that is required for initiation by all three nuclear RNA polymerases, and it is also a component of the DNA-binding protein complex of transcription factor II D (TFIID). This gene is associated with a variety of factors that play important roles in the regulation of gene expression [10,99].
The ABL gene was constantly expressed in the peripheral blood of healthy individuals at levels comparable to other analyzed reference genes in different studies, including studies with chronic myelogenous leukemia (CML) expression analysis [100,101,102]. Furthermore, a study published by Weisser et al. in 2004 reported that ABL was a suitable endogenous gene for monitoring minimal residual disease in acute myeloid leukemia patients [103].
Altogether, the data from many different studies show the relevance of TBP gene expression stability, indicating that it is a suitable reference gene to be used as a control in studies of various kinds of diseases, including some types of cancer such as bladder cancer and glioblastoma. However, the majority of these studies also showed that the use of TBP associated with other reference genes presented an even better performance [92,93,104,105,106,107,108].
RPLP0 is a ribosomal protein that is responsible for recruiting both translation factors and other ribosomal proteins to the ribosomal complexes, facilitating protein synthesis. Usually, it is strongly expressed in normal lymph nodes, skin, spleen and fetal brain tissue, expressed at lower levels in normal lung, bladder and placenta and not expressed in normal colon, kidney and bone marrow [109,110,111].
What was observed in this study in relation to the RPLP0 gene is what is usually demonstrated in the other endogenous gene validation studies for gene expression techniques. The RPLP0 gene presents a relatively good expression stability in several studies, but it is not the most suitable reference gene [44,112,113,114,115].
Table 6 summarizes all the endogenous gene normalization and validation studies that were mentioned in the discussion of this work, allowing for better data visualization, such as model (tissue type), number of patients, analyzed samples, endogenous genes tested, endogenous gene with best behavior and year of each study. Through this table, it is possible to observe that most reference genes’ stability varies among different disease models, demonstrating that performing this kind of validation study before any gene expression analysis is very important.

5. Conclusions

Given these findings, this study suggests the main endogenous gene set for use as a control/reference for the analysis of gene expression in peripheral blood and bone marrow samples from patients with acute leukemias is composed of the ACTB, ABL, TBP and RPLP0 genes. Researchers may choose two to three of these endogenous controls to perform data normalization.
In addition, the statistical analysis in this type of study is indispensable. It is important to verify the variation of endogenous gene expression between groups. Once the calculation of delta Ct is made, the variability of endogenous gene expression is transferred to the target. Then, if there is a high endogenous gene variability between groups, potential differences found on the target may not really indicate target variation but actually the influence of the endogenous variation.
Therefore, it is extremely necessary to perform the reference gene validation for any gene expression study, considering that the endogenous gene used influences the reliability and accuracy of these studies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes15020151/s1, Spreadsheet created with all the CT data of all endogenous controls and all analyzed samples in GEO database format. The file contains 4 spreadsheets: metadata template, matrix non-normalized template, matrix normalized template and fold change template.

Author Contributions

Invitation received, C.A.M.-N.; conceptualization, F.M.C.d.P.P., F.C.M. and C.A.M.-N.; provision of data and subsequent analysis and interpretation, F.M.C.d.P.P., V.B.d.J.V., M.B.d.O., R.M.R., D.d.S.O., G.S.L., R.P.G.V., M.O.d.M.F., M.E.A.d.M., F.C.M., A.S.K. and C.A.M.-N.; bioinformatics analysis, F.C.M. and C.A.M.-N.; writing—original draft preparation, F.M.C.d.P.P. and C.A.M.-N.; writing—review and editing, F.M.C.d.P.P., B.M.D.N., F.C.M. and C.A.M.-N.; funding acquisition, A.S.K. and C.A.M.-N. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Council of Technological and Scientific Development (CNPq to M.O.d.M.F., M.E.A.d.M., A.S.K. and C.A.M.-N.) and Cearense Foundation of Scientific and Technological Support (FUNCAP grant number P20-0171-00078.01.00/20 to F.M.C.d.P.P., M.O.d.M.F. and to C.A.M.-N.), and we thank PROPESP/UFPA for publication payment.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Ophir Loyola Hospital (approval number: 2,798,615) and the Ethics Committee of the General Hospital of Fortaleza (approval number 4,798,575).

Informed Consent Statement

Written informed consent has been obtained from the patient(s) and/or their legal representants to publish this paper.

Data Availability Statement

Data are contained within the article and supplementary materials.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or data interpretation; in the writing of the manuscript or in the decision to publish the results.

References

  1. Arya, M.; Shergill, I.S.; Williamson, M.; Gommersall, L.; Arya, N.; Patel, H.R.H. Basic principles of real-time quantitative PCR. Expert. Rev. Mol. Diagn. 2005, 5, 209–219. [Google Scholar] [CrossRef] [PubMed]
  2. Jalali, M.; Zaborowska, J.; Jalali, M. The Polymerase Chain Reaction: PCR, qPCR, and RT-PCR; Elsevier Inc.: Amsterdam, The Netherlands, 2017; pp. 1–18. [Google Scholar] [CrossRef]
  3. Singh, J.; Birbian, N.; Sinha, S.; Goswami, A. A critical review on PCR, its types and applications. Int. J. Adv. Res. Biol. Sci. 2014, 1, 65–80. [Google Scholar]
  4. Seifi, M.; Ghasemi, A.; Heidarzadeh, S.; Khosravi, M.; Namipashaki, A.; Soofiany, V.M.; Khosroshahi, A.A.; Danaei, N. Overview of Real-Time PCR Principles. In Polymerase Chain Reaction; IntechOpen: London, UK, 2012. [Google Scholar]
  5. Loftis, A.D.; Reeves, W.K. Principles of Real-Time PCR. In Veterinary PCR Diagnosis; Bentham Science Publishers: Bussum, The Netherlands, 2012; pp. 3–17. [Google Scholar]
  6. Heid, C.A.; Stevens, J.; Livak, K.J.; Williams, P.M. Real Time Quantitative PCR. Genome Res. 1996, 6, 986–994. [Google Scholar] [CrossRef] [PubMed]
  7. Harshitha, R.; Arunraj, D.R. Real-time quantitative PCR: A tool for absolute and relative quantification. Biochem. Mol. Biol. Educ. 2021, 49, 800–812. [Google Scholar] [CrossRef] [PubMed]
  8. Higuchi, R.; Dollinger, G.; Walsh, P.S.; Griffith, R. Simultaneous amplification and detection of specific DNA sequences. Biotechnology 1992, 10, 413–417. [Google Scholar] [CrossRef]
  9. Vilà, M.R.; Nicolás, A.; Morote, J.; De, I.; Meseguer, A. Increased glyceraldehyde-3-phosphate dehydrogenase expression in renal cell carcinoma identified by RNA-based, arbitrarily primed polymerase chain reaction. Cancer 2000, 89, 154–164. [Google Scholar] [CrossRef]
  10. Le, P.U.; Nguyen, T.N.; Drolet-Savoie, P.; Leclerc, N.; Nabi, I.R. Increased β-actin expression in an invasive Moloney sarcoma virus- transformed MDCK cell variant concentrates to the tips of multiple pseudopodia. Cancer Res. 1998, 58, 1631–1635. [Google Scholar]
  11. Wierschke, S.; Gigout, S.; Horn, P.; Lehmann, T.-N.; Dehnicke, C.; Bräuer, A.U.; Deisz, R.A. Evaluating reference genes to normalize gene expression in human epileptogenic brain tissues. Biochem. Biophys. Res. Commun. 2010, 403, 385–390. [Google Scholar] [CrossRef]
  12. Deindl, E.; Boengler, K.; van Royen, N.; Schaper, W. Differential expression of GAPDH and β-actin in growing collateral arteries. Mol. Cell Biochem. 2002, 236, 139–146. [Google Scholar] [CrossRef]
  13. Huggett, J.; Dheda, K.; Bustin, S.; Zumla, A. Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 2005, 6, 279–284. [Google Scholar] [CrossRef]
  14. Valasek, M.A.; Repa, J.J. The power of real-time PCR. Adv. Physiol. Educ. 2005, 29, 151–159. [Google Scholar] [CrossRef] [PubMed]
  15. Fraga, D.; Meulia, T.; Fenster, S. Real-Time PCR. Curr. Protoc. Essent. Lab. Tech. 2014, 8, 10.3.1–10.3.40. [Google Scholar] [CrossRef]
  16. Jia, Y. Real-Time PCR; Elsevier: Amsterdam, The Netherlands, 2012; pp. 55–68. [Google Scholar] [CrossRef]
  17. Wilhelm, J.; Pingoud, A. Real-Time Polymerase Chain Reaction. ChemBioChem 2003, 4, 1120–1128. [Google Scholar] [CrossRef] [PubMed]
  18. Kozera, B.; Rapacz, M. Reference genes in real-time PCR. J. Appl. Genet. 2013, 54, 391–406. [Google Scholar] [CrossRef] [PubMed]
  19. Jacob, F.; Guertler, R.; Naim, S.; Nixdorf, S.; Fedier, A.; Hacker, N.F.; Heinzelmann-Schwarz, V. Careful Selection of Reference Genes Is Required for Reliable Performance of RT-qPCR in Human Normal and Cancer Cell Lines. PLoS ONE 2013, 8, e59180. [Google Scholar] [CrossRef]
  20. Bustin, S.A. Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): Trends and problems. J. Mol. Endocrinol. 2002, 29, 23–39. [Google Scholar] [CrossRef]
  21. Bustin, S.A.; Benes, V.; Nolan, T.; Pfaffl, M.W. Quantitative real-time RT-PCR—A perspective. J. Mol. Endocrinol. 2005, 34, 597–601. [Google Scholar] [CrossRef]
  22. Balogh, A.; Paragh, G., Jr.; Juhász, A.; Köbling, T.; Törőcsik, D.; Mikó, E.; Varga, V.; Emri, G.; Horkay, I.; Scholtz, B.; et al. Reference genes for quantitative real time PCR in UVB irradiated keratinocytes. J. Photochem. Photobiol. B Biol. 2008, 93, 133–139. [Google Scholar] [CrossRef]
  23. Hendriks-Balk, M.C.; Michel, M.C.; Alewijnse, A.E. Pitfalls in the normalization of real-time polymerase chain reaction data. Basic Res. Cardiol. 2007, 102, 195–197. [Google Scholar] [CrossRef]
  24. Schmittgen, T.D.; Zakrajsek, B.A. Effect of experimental treatment on housekeeping gene expression: Validation by real-time, quantitative RT-PCR. J. Biochem. Biophys. Methods 2000, 46, 69–81. [Google Scholar] [CrossRef]
  25. Galiveti, C.R.; Rozhdestvensky, T.S.; Brosius, J.; Lehrach, H.; Konthur, Z. Application of housekeeping npcRNAs for quantitative expression analysis of human transcriptome by real-time PCR. Rna 2010, 16, 450–461. [Google Scholar] [CrossRef] [PubMed]
  26. Wu, T.; Zhang, R.; Gu, C.; Wu, J.; Wan, H.; Zhang, S.; Zhang, S. Evaluation of candidate reference genes for real time quantitative PCR normalization in pear fruit. Afr. J. Agric. Res. 2012, 7, 3701–3704. [Google Scholar]
  27. Chapman, J.R.; Waldenström, J. With reference to reference genes: A systematic review of endogenous controls in gene expression studies. PLoS ONE 2015, 10, e0141853. [Google Scholar] [CrossRef] [PubMed]
  28. Hellemans, J.; Mortier, G.; De Paepe, A.; Speleman, F.; Vandesompele, J. qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol. 2008, 8, R19. [Google Scholar] [CrossRef]
  29. Chervoneva, I.; Li, Y.; Schulz, S.; Croker, S.; Wilson, C.; Waldman, S.A.; Hyslop, T. Selection of optimal reference genes for normalization in quantitative RT-PCR. BMC Bioinform. 2010, 11, 253. [Google Scholar] [CrossRef] [PubMed]
  30. Harris, M.H.; Czuchlewski, D.R.; Arber, D.A.; Czader, M. Genetic Testing in the Diagnosis and Biology of Acute Leukemia. Am. J. Clin. Pathol. 2019, 152, 322–346. [Google Scholar] [CrossRef] [PubMed]
  31. Shakeel, M.; Rodriguez, A.; Tahir, U.B.; Jin, F. Gene expression studies of reference genes for quantitative real-time PCR: An overview in insects. Biotechnol. Lett. 2018, 40, 227–236. [Google Scholar] [CrossRef]
  32. Guénin, S.; Mauriat, M.; Pelloux, J.; Van Wuytswinkel, O.; Bellini, C.; Gutierrez, L. Normalization of qRT-PCR data: The necessity of adopting a systematic, experimental conditions-specific, validation of references. J. Exp. Bot. 2009, 60, 487–493. [Google Scholar] [CrossRef]
  33. Feuer, R.; Vlaic, S.; Arlt, J.; Sawodny, O.; Dahmen, U.; Zanger, U.M.; Thomas, M.; Kaderali, L. LEMming: A linear error model to normalize parallel quantitative real-time PCR (qPCR) data as an alternative to reference gene based methods. PLoS ONE 2015, 10, e0135852. [Google Scholar] [CrossRef]
  34. Richly, E.; Chinnery, P.F.; Leister, D. Evolutionary diversification of mitochondrial proteomes: Implications for human disease. Trends Genet. 2003, 19, 356–362. [Google Scholar] [CrossRef]
  35. Micheva, K.D.; Vallée, A.; Beaulieu, C.; Herman, I.M.; Leclerc, N. Β-Actin Is Confined To Structures Having High Capacity of Remodelling in Developing and Adult Rat Cerebellum. Eur. J. Neurosci. 1998, 10, 3785–3798. [Google Scholar] [CrossRef] [PubMed]
  36. McLysaght, A. Evolutionary steps of sex chromosomes are reflected in retrogenes. Trends Genet. 2008, 24, 478–481. [Google Scholar] [CrossRef] [PubMed]
  37. Eisenberg, E.; Levanon, E.Y. Human housekeeping genes, revisited. Trends Genet. 2013, 29, 569–574. [Google Scholar] [CrossRef] [PubMed]
  38. Chan, B.K.C. Data Analysis Using R Programming. In Biostatistics for Human Genetic Epidemiology; Advances in Experimental Medicine and Biology: New York, NY, USA, 2018; pp. 47–122. [Google Scholar] [CrossRef]
  39. Dheda, K.; Huggett, J.F.; Bustin, S.A.; Johnso, M.A.; Rook, G.; Zumla, A. Validation of housekeeping genes for normalizing RNA expression in real-time PCR. Biotechniques 2004, 37, 112–119. [Google Scholar] [CrossRef] [PubMed]
  40. Eisenberg, E.; Levanon, E.Y. Human housekeeping genes are compact. Trends Genet. 2003, 19, 362–365. [Google Scholar] [CrossRef]
  41. Zhu, J.; He, F.; Hu, S.; Yu, J. On the nature of human housekeeping genes. Genome Anal. 2008, 24, 481–484. [Google Scholar] [CrossRef] [PubMed]
  42. Iyer, G.; Wang, A.R.; Brennan, S.R.; Bourgeois, S.; Armstrong, E.; Shah, P.; Harari, P.M. Identification of stable housekeeping genes in response to ionizing radiation in cancer research. Sci. Rep. 2017, 7, 43763. [Google Scholar] [CrossRef]
  43. Vandesompele, J.; De Preter, K.; Pattyn, F.; Poppe, B.; Van Roy, N.; De Paepe, A.; Speleman, F. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3, research0034.1. [Google Scholar] [CrossRef]
  44. Shen, Y.; Li, Y.; Ye, F.; Wang, F.; Lu, W.; Xie, X. Identification of suitable reference genes for measurement of gene expression in human cervical tissues. Anal. Biochem. 2010, 405, 224–229. [Google Scholar] [CrossRef]
  45. Taube, M.; Andersson-Assarsson, J.C.; Lindberg, K.; Pereira, M.J.; Gäbel, M.; Svensson, M.K.; Eriksson, J.W.; Svensson, P.A. Evaluation of reference genes for gene expression studies in human brown adipose tissue. Adipocyte 2015, 4, 280–285. [Google Scholar] [CrossRef]
  46. Livak, K.J.; Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef] [PubMed]
  47. McNeill, R.E.; Miller, N.; Kerin, M.J. Evaluation and validation of candidate endogenous control genes for real-time quantitative PCR studies of breast cancer. BMC Mol. Biol. 2007, 8, 107. [Google Scholar] [CrossRef] [PubMed]
  48. Weaver, S.; Dube, S.; Mir, A.; Qin, J.; Sun, G.; Ramakrishnan, R.; Jones, R.C.; Livak, K.J. Taking qPCR to a higher level: Analysis of CNV reveals the power of high throughput qPCR to enhance quantitative resolution. Methods 2010, 50, 271–276. [Google Scholar] [CrossRef] [PubMed]
  49. Janssens, N.; Janicot, M.; Perera, T.; Bakker, A. Housekeeping genes as internal standards in cancer research. Mol. Diagnosis 2004, 8, 107–113. [Google Scholar] [CrossRef] [PubMed]
  50. Khimani, A.H.; Mhashilkar, A.M.; Mikulskis, A.; O’Malley, M.; Liao, J.; Golenko, E.E.; Mayer, P.; Chada, S.; Killian, J.B.; Lott, S.T. Housekeeping genes in cancer: Normalization of array data. Biotechniques 2005, 38, 739–745. [Google Scholar] [CrossRef] [PubMed]
  51. Ayakannu, T.; Taylor, A.H.; Konje, J.C. Selection of Endogenous Control Reference Genes for Studies on Type 1 or Type 2 Endometrial Cancer. Sci. Rep. 2020, 10, 2–11. [Google Scholar] [CrossRef] [PubMed]
  52. Bustin, S.A.; Benes, V.; Garson, J.A.; Hellemans, J.; Huggett, J.; Kubista, M.; Mueller, R.; Nolan, T.; Pfaffl, M.W.; Shipley, G.L.; et al. The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 2009, 55, 611–622. [Google Scholar] [CrossRef]
  53. Andersen, C.L.; Jensen, J.L.; Ørntoft, T.F. Normalization of real-time quantitative reverse transcription-PCR data: A model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004, 64, 5245–5250. [Google Scholar] [CrossRef]
  54. R Core Team. R: A Language and Environment for Statistical Computing. Available online: https://www.r-project.org/ (accessed on 14 September 2023).
  55. Ostertagová, E.; Ostertag, O.; Kováč, J. Methodology and application of the Kruskal-Wallis test. Appl. Mech. Mater. 2014, 611, 115–120. [Google Scholar] [CrossRef]
  56. Frey, B.B. Kruskal-Wallis Test. In The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation; SAGE Publications, Inc.: Thousand Oaks, CA, USA, 2018; Voluem 10, p. n377. [Google Scholar]
  57. Almonroeder, T.G. Introduction to analysis of variance. In Advanced Statistics for Physical and Occupational Therapy; Routledge: London, UK, 2022; pp. 77–97. [Google Scholar] [CrossRef]
  58. Sthle, L.; Wold, S. Analysis of variance (ANOVA). Chemom. Intell. Lab. Syst. 1989, 6, 259–272. [Google Scholar] [CrossRef]
  59. Fujikoshi, Y. Two-way ANOVA models with unbalanced data. Discrete Math. 1993, 116, 315–334. [Google Scholar] [CrossRef]
  60. Song, Q.; Dou, L.; Zhang, W.; Peng, Y.; Huang, M.; Wang, M. Public transcriptome database-based selection and validation of reliable reference genes for breast cancer research. Biomed. Eng. Online 2021, 20, 124. [Google Scholar] [CrossRef]
  61. Martin, J.L. Validation of reference genes for oral cancer detection panels in a prospective blinded cohort. PLoS ONE 2016, 11, e0158462. [Google Scholar] [CrossRef] [PubMed]
  62. de Campos, R.P.; Schultz, I.C.; de Andrade Mello, P.; Davies, S.; Gasparin, M.S.; Bertoni, A.P.S.; Buffon, A.; Wink, M.R. Cervical cancer stem-like cells: Systematic review and identification of reference genes for gene expression. Cell Biol. Int. 2018, 42, 139–152. [Google Scholar] [CrossRef] [PubMed]
  63. Pfaffl, M.W.; Horgan, G.W.; Dempfle, L. Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res. 2002, 30, e36. [Google Scholar] [CrossRef]
  64. Pfaffl, M.W.; Tichopad, A.; Prgomet, C.; Neuvians, T.P. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper—Excel-based tool using pair-wise correlations. Biotechnol. Lett. 2004, 26, 509–515. [Google Scholar] [CrossRef]
  65. Jelinek, D.F.; Tschumper, R.C.; Stolovitzky, G.A.; Iturria, S.J.; Tu, Y.; Lepre, J.; Shah, N.; Kay, N. Identification of a global geneexpression signature of B-chronic lymphocytic leukemia. Mol. Cancer Res. 2003, 1, 346–361. [Google Scholar]
  66. Joshi, A.D.; Hegde, G.V.; Dickinson, J.D.; Mittal, A.K.; Lynch, J.C.; Eudy, J.D.; Armitage, J.O.; Bierman, P.J.; Bociek, R.G.; Devetten, M.P.; et al. ATM, CTLA4, MNDA, andHEM1 in high versus low CD38 expressing B-cell chronic lym-phocytic leukemia. Clin. Cancer Res. 2007, 13, 5295–5304. [Google Scholar] [CrossRef]
  67. Ghani, M.; Sato, C.; Rogaeva, E. Segmental duplications in genome-wide significant loci and housekeeping genes; warning for GAPDH and ACTB. Neurobiol. Aging 2013, 34, e1–e1710. [Google Scholar] [CrossRef]
  68. Kienle, D.; Benner, A.; Laufle, C.; Winkler, D.; Schneider, C.; Buhler, A.; Zenz, T.; Habermann, A.; Jager, U.; Lichter, P.; et al. Gene expression factors as predictors of genetic risk and survival in chronic lymphocytic leukemia. Haematologica 2010, 95, 102–109. [Google Scholar] [CrossRef]
  69. Nuckel, H.; Collins, C.H.; Frey, U.H.; Sellmann, L.; Durig, J.; Siffert, W.; Duhrsen, U. FCRL2 mRNA expression is inverselyassociated with clinical progression in chronic lymphocytic leukemia. Eur. J. Haematol. 2009, 83, 541–549. [Google Scholar] [CrossRef] [PubMed]
  70. Glare, E.M.; Divjak, M.; Bailey, M.J.; Walters, E.H. β-actin and GAPDH housekeeping gene expression in asthmatic airways is variable and not suitable for normalising mRNA levels. Thorax 2002, 57, 765–770. [Google Scholar] [CrossRef] [PubMed]
  71. Valceckiene, V.; Kontenyte, R.; Jakubauskas, A.; Griskevicius, L. Selection of reference genes for quantitative polymerase chain reaction studies in purified B cells from B cell chronic lymphocytic leukaemia patients. Br. J. Haematol. 2010, 151, 232–238. [Google Scholar] [CrossRef] [PubMed]
  72. Tong, Z.; Gao, Z.; Wang, F.; Zhou, J.; Zhang, Z. Selection of reliable reference genes for gene expression studies in peach using real-time PCR. BMC Mol. Biol. 2009, 10, 71. [Google Scholar] [CrossRef]
  73. Bednarz-Misa, I.; Neubauer, K.; Zacharska, E.; Kapturkiewicz, B.; Krzystek-Korpacka, M. Whole blood ACTB, B2M and GAPDH expression reflects activity of inflammatory bowel disease, advancement of colorectal cancer, and correlates with circulating inflammatory and angiogenic factors: Relevance for real-time quantitative PCR. Adv. Clin. Exp. Med. 2020, 29, 547–556. [Google Scholar] [CrossRef]
  74. Ullmannová, V.; Haškovec, C. The use of housekeeping genes (HKG) as an internal control for the detection of gene expression by quantitative real-time RT-PCR. Folia Biol. 2003, 49, 211–216. [Google Scholar]
  75. Tan, S.C.; Carr, C.A.; Yeoh, K.K.; Schofield, C.J.; Davies, K.E.; Clarke, K. Identification of valid housekeeping genes for quantitative RT-PCR analysis of cardiosphere-derived cells preconditioned under hypoxia or with prolyl-4-hydroxylase inhibitors. Mol. Biol. Rep. 2012, 39, 4857–4867. [Google Scholar] [CrossRef]
  76. Barber, R.D.; Harmer, D.W.; Coleman, R.A.; Clark, B.J. GAPDH as a housekeeping gene: Analysis of GAPDH mRNA expression in a panel of 72 human tissues. Physiol. Genomics 2005, 21, 389–395. [Google Scholar] [CrossRef]
  77. Albertini, R.J. HPRT mutations in humans: Biomarkers for mechanistic studies. Mutat. Res./Rev. Mutat. Res. 2001, 489, 1–16. [Google Scholar] [CrossRef]
  78. Stout, J.T.; Caskey, C.T. Hprt: Gene structure, expression, and mutation. Annu. Rev. Genet. 1985, 19, 127–148. [Google Scholar] [CrossRef]
  79. Everaert, B.R.; Boulet, G.A.; Timmermans, J.P.; Vrints, C.J. Importance of suitable reference gene selection for quantitative real-time PCR: Special reference to mouse myocardial infarction studies. PLoS ONE 2011, 6, e23793. [Google Scholar] [CrossRef] [PubMed]
  80. Gao, Q.; Wang, X.Y.; Fan, J.; Qiu, S.J.; Zhou, J.; Shi, Y.H.; Xiao, Y.S.; Xu, Y.; Huang, X.W.; Sun, J. Selection of reference genes for real-time PCR in human hepatocellular carcinoma tissues. J. Cancer Res. Clin. Oncol. 2008, 134, 979–986. [Google Scholar] [CrossRef] [PubMed]
  81. Maurer-Morelli, C.V.; de Vasconcellos, J.F.; Reis-Pinto, F.C.; Rocha, C.d.S.; Domingues, R.R.; Yasuda, C.L.; Tedeschi, H.; Oliveira, E.D.; Cendes, F.; Lopes-Cendes, I. A comparison between different reference genes for expression studies in human hippocampal tissue. J. Neurosci. Methods 2012, 208, 44–47. [Google Scholar] [CrossRef] [PubMed]
  82. Ropenga, A.; Chapel, A.; Vandamme, M.; Griffiths, N.M. Use of reference gene expression in rat distal colon after radiation exposure: A caveat. Radiat. Res. 2004, 161, 597–602. [Google Scholar] [CrossRef] [PubMed]
  83. de Kok, J.B.; Roelofs, R.W.; Giesendorf, B.A.; Pennings, J.L.; Waas, E.T.; Feuth, T.; Swinkels, D.W.; Span, P.N. Normalization of gene expression measurements in tumor tissues: Comparison of 13 endogenous control genes. Lab. Investig. 2005, 85, 154–159. [Google Scholar] [CrossRef]
  84. Townsend, M.H.; Felsted, A.M.; Ence, Z.E.; Piccolo, S.R.; Robison, R.A.; O’Neill, K.L. Falling from grace: HPRT is not suitable as an endogenous control for cancer-related studies. Mol. Cell Oncol. 2019, 6, 1–10. [Google Scholar] [CrossRef]
  85. Radonić, A.; Thulke, S.; Mackay, I.M.; Landt, O.; Siegert, W.; Nitsche, A. Guideline to reference gene selection for quantitative real-time PCR. Biochem. Biophys. Res. Commun. 2004, 313, 856–862. [Google Scholar] [CrossRef]
  86. Popov, A.; Nowak, D.; Malicka-Błaszkiewicz, M. Actin-cytoskeleton and b-actin expression in correlation with higher invasiveness of selected hepatoma Morris 5123 cells. J. Physiol. Pharmacol. 2006, 57, 111–123. [Google Scholar]
  87. Guo, C.; Liu, S.; Wang, J.; Sun, M.Z.; Greenaway, F.T. ACTB in cancer. Clin. Chim. Acta 2013, 417, 39–44. [Google Scholar] [CrossRef]
  88. Morse, D.L.; Carroll, D.; Weberg, L.; Borgstrom, M.C.; Ranger-Moore, J.; Gillies, R.J. Determining suitable internal standards for mRNA quantification of increasing cancer progression in human breast cells by real-time reverse transcriptase polymerase chain reaction. Anal. Biochem. 2005, 342, 69–77. [Google Scholar] [CrossRef]
  89. Majidzadeh, A.K.; Esmaeili, R.; Abdoli, N. TFRC and ACTB as the best reference genes to quantify Urokinase Plasminogen Activator in breast cancer. BMC Res. Notes 2011, 4, 215. [Google Scholar] [CrossRef]
  90. Gur-Dedeoglu, B.; Konu, O.; Bozkurt, B.; Ergul, G.; Seckin, S.; Yulug, I.G. Identification of endogenous reference genes for qRT-PCR analysis in normal matched breast tumor tissues. Oncol. Res. 2009, 17, 353–365. [Google Scholar] [CrossRef] [PubMed]
  91. Goidin, D.; Mamessier, A.; Staquet, M.; Schmitt, D.; Berthier-Vergnes, O. Ribosomal 18S RNA prevails over glyceraldehyde-3-phosphate dehydrogenase and beta-actin genes as internal standard for quantitative comparison of mRNA levels in invasive and noninvasive human melanoma cell subpopulations. Anal. Biochem. 2001, 295, 17–21. [Google Scholar] [CrossRef] [PubMed]
  92. Jung, M.; Ramankulov, A.; Roigas, J.; Johannsen, M.; Ringsdorf, M.; Kristiansen, G.; Jung, K. In search of suitable reference genes for gene expression studies of human renal cell carcinoma by real-time PCR. BMC Mol. Biol. 2007, 8, 47. [Google Scholar] [CrossRef] [PubMed]
  93. Lemma, S.; Avnet, S.; Salerno, M.; Chano, T.; Baldini, N. Identification and Validation of Housekeeping Genes for Gene Expression Analysis of Cancer Stem Cells. PLoS ONE 2016, 11, e0149481. [Google Scholar] [CrossRef]
  94. Gu, Y.; Tang, S.; Wang, Z.; Cai, L.; Lian, H.; Shen, Y.; Zhou, Y. A pan-cancer analysis of the prognostic and immunological role of β-actin (ACTB) in human cancers. Bioengineered 2021, 12, 6166–6185. [Google Scholar] [CrossRef]
  95. Gupta, D.G.; Varma, N.; Abdulkadir, S.A.; Singh, P.; Sachdeva, M.U.S.; Naseem, S.; Siddiqui, M.R.; Bose, P.; Binota, J.; Malhotra, P.; et al. Identifcation and validation of the optimal reference genes for standardizing the gene expression profling diagnostic panel of Ph-like B-lineage acute lymphoblastic leukemia. Clin. Exp. Med. 2023, 23, 4539–4551. [Google Scholar] [CrossRef]
  96. Wang, J.Y.J. The Capable ABL: What Is Its Biological Function? Mol. Cell Biol. 2014, 34, 1188–1197. [Google Scholar] [CrossRef]
  97. Van Etten, R.A. Cycling, stressed-out and nervous: Cellular functions of c-Abl. Trends Cell Biol. 1999, 9, 179–186. [Google Scholar] [CrossRef]
  98. Ramakrishnan, L.; Rosenberg, N. Abl Genes. Biochim. Biophys. Acta (BBA)-Rev. Cancer 1989, 989, 209–224. [Google Scholar] [CrossRef]
  99. Davidson, I. The genetics of TBP and TBP-related factors. Trends Biochem. Sci. 2003, 28, 391–398. [Google Scholar] [CrossRef] [PubMed]
  100. Emig, M.; Saußele, S.; Wittor, H.; Weißer, A.; Reiter, A.; Willer, A.; Berger, U.; Hehlmann, R.; Cross, N.C.P.; Hochhaus, A. Accurate and rapid analysis of residual disease in patients with CML using specific fluorescent hybridization probes for real time quantitative RT-PCR. Leukemia 1999, 13, 1825–1832. [Google Scholar] [CrossRef] [PubMed]
  101. Müller, M.C.; Erben, P.; Saglio, G.; Gottardi, E.; Nyvold, C.G.; Schenk, T.; Ernst, T.; Lauber, S.; Kruth, J.; Hehlmann, R.; et al. Harmonization of BCR-ABL mRNA quantification using a uniform multifunctional control plasmid in 37 international laboratories. Leukemia 2008, 22, 96–102. [Google Scholar] [CrossRef] [PubMed]
  102. Schoch, C.; Schnittger, S.; Bursch, S.; Gerstner, D.; Hochhaus, A.; Berger, U.; Hehlmann, R.; Hiddemann, W.; Haferlach, T. Comparison of chromosome banding analysis, interphase- and hypermetaphase-FISH, qualitative and quantitative PCR for diagnosis and for follow-up in chronic myeloid leukemia: A study on 350 cases. Leukemia 2002, 16, 53–59. [Google Scholar] [CrossRef] [PubMed]
  103. Weisser, M.; Haferlach, T.; Schoch, C.; Hiddeman, W.; Schnittger, S. The use of housekeeping genes for real-time PCR-based quantification of fusion gene transcripts in acute myeloid leukemia. Leukemia 2004, 18, 1551–1553. [Google Scholar] [CrossRef] [PubMed]
  104. Lossos, I.S.; Czerwinski, D.K.; Wechser, M.A.; Levy, R. Optimization of quantitative real-time RT-PCR parameters for the study of lymphoid malignancies. Leukemia 2003, 17, 789–795. [Google Scholar] [CrossRef] [PubMed]
  105. Meller, M.; Vadachkoira, S.; Luthy, D.A.; Williams, M.A. Evaluation of housekeeping genes in placental comparative expression studies. Placenta 2005, 26, 601–607. [Google Scholar] [CrossRef]
  106. Ohl, F.; Jung, M.; Radonić, A.; Sachs, M.; Loening, S.A.; Jung, K. Identification and Validation of Suitable Endogenous Reference Genes for Gene Expression Studies of Human Bladder Cancer. J. Urol. 2006, 175, 1915–1920. [Google Scholar] [CrossRef]
  107. Turabelidze, A.; Guo, S.; Dipietro, L.A. Importance of housekeeping gene selection for accurate reverse transcription-quantitative polymerase chain reaction in a wound healing model. Wound Repair. Regen. 2010, 18, 460–466. [Google Scholar] [CrossRef]
  108. Valente, V.; Teixeira, S.A.; Neder, L.; Okamoto, O.K.; Oba-Shinjo, S.M.; Marie, S.K.N.; Scrideli, C.A.; Paçó-Larson, M.L.; Carlotti, C.G. Selection of suitable housekeeping genes for expression analysis in glioblastoma using quantitative RT-PCR. BMC Mol. Biol. 2009, 10, 17. [Google Scholar] [CrossRef]
  109. Wang, C.H.; Wang, L.K.; Wu, C.C.; Chen, M.L.; Lee, M.C.; Lin, Y.Y.; Tsai, F.M. The Ribosomal Protein RPLP0 Mediates PLAAT4-induced Cell Cycle Arrest and Cell Apoptosis. Cell Biochem. Biophys. 2019, 77, 253–260. [Google Scholar] [CrossRef] [PubMed]
  110. Wang, Y.L.; Zhao, W.W.; Bai, S.M.; Ma, Y.; Yin, X.K.; Feng, L.L.; Zeng, G.D.; Wang, F.; Feng, W.X.; Zheng, J.; et al. DNA damage-induced paraspeckle formation enhances DNA repair and tumor radioresistance by recruiting ribosomal protein P0. Cell Death Dis. 2022, 13, 709. [Google Scholar] [CrossRef] [PubMed]
  111. Artero-Castro, A.; Castellvi, J.; García, A.; Hernández, J.; Ramón y Cajal, S.; LLeonart, M.E. Expression of the ribosomal proteins Rplp0, Rplp1, and Rplp2 in gynecologic tumors. Hum. Pathol. 2011, 42, 194–203. [Google Scholar] [CrossRef] [PubMed]
  112. Stern-Straeter, J.; Bonaterra, G.A.; Hörmann, K.; Kinscherf, R.; Goessler, U.R. Identification of valid reference genes during the differentiation of human myoblasts. BMC Mol. Biol. 2009, 10, 66. [Google Scholar] [CrossRef]
  113. Ali, H.; Du, Z.; Li, X.; Yang, Q.; Zhang, Y.C.; Wu, M.; Li, Y.; Zhang, G. Identification of suitable reference genes for gene expression studies using quantitative polymerase chain reaction in lung cancer in vitro. Mol. Med. Rep. 2015, 11, 3767–3773. [Google Scholar] [CrossRef]
  114. Bakhashab, S.; Lary, S.; Ahmed, F.; Schulten, H.J.; Bashir, A.; Ahmed, F.W.; Al-Malki, A.L.; Jamal, H.S.; Gari, M.A.; Weaver, J.U. Reference genes for expression studies in hypoxia and hyperglycemia models in human umbilical vein endothelial cells. G3 Genes Genomes Genet. 2014, 4, 2159–2165. [Google Scholar] [CrossRef]
  115. Wang, H.; Yang, B.; Geng, T.; Li, B.; Dai, P.; Chen, C. Tissue-specific selection of optimal reference genes for expression analysis of anti-cancer drug-related genes in tumor samples using quantitative real-time RT-PCR. Exp. Mol. Pathol. 2015, 98, 375–381. [Google Scholar] [CrossRef]
Genes 15 00151 g001
Figure 1. Flowchart of the steps used to select the best endogenous gene set. This figure illustrates the methodology utilized during this study’s statistical analysis in the R software. KKW: Kruskal–Wallis; WMW: Wilcoxon–Mann–Whitney.
Figure 1. Flowchart of the steps used to select the best endogenous gene set. This figure illustrates the methodology utilized during this study’s statistical analysis in the R software. KKW: Kruskal–Wallis; WMW: Wilcoxon–Mann–Whitney.
Genes 15 00151 g001
Genes 15 00151 g002
Figure 2. Box-plot graph indicating the range of Ct values of the reference genes by group. ALL: adult acute lymphoblastic leukemia; ALL_Ped: pediatric acute lymphoblastic leukemia; AML: acute myeloid leukemia. This figure, obtained through R software analysis, illustrates the mean Ct of each endogenous candidate in the different groups analyzed, allowing the visualization of which endogenous genes showed greater expression instability and which were more stable. The smaller the mean Ct variation and the smaller the variation between the groups, the more stable the endogenous gene evaluated.
Figure 2. Box-plot graph indicating the range of Ct values of the reference genes by group. ALL: adult acute lymphoblastic leukemia; ALL_Ped: pediatric acute lymphoblastic leukemia; AML: acute myeloid leukemia. This figure, obtained through R software analysis, illustrates the mean Ct of each endogenous candidate in the different groups analyzed, allowing the visualization of which endogenous genes showed greater expression instability and which were more stable. The smaller the mean Ct variation and the smaller the variation between the groups, the more stable the endogenous gene evaluated.
Genes 15 00151 g002
Genes 15 00151 g003
Figure 3. Box-plot graph indicating the range of Ct values of reference genes by sample origin. BM: bone marrow; PB: peripheral blood. This figure, obtained through R software analysis, illustrates the mean Ct of each endogenous candidate according to the type of sample analyzed (bone marrow and peripheral blood). Only the AML and adult ALL groups participated in this analysis since they were the only two groups that had both sample types.
Figure 3. Box-plot graph indicating the range of Ct values of reference genes by sample origin. BM: bone marrow; PB: peripheral blood. This figure, obtained through R software analysis, illustrates the mean Ct of each endogenous candidate according to the type of sample analyzed (bone marrow and peripheral blood). Only the AML and adult ALL groups participated in this analysis since they were the only two groups that had both sample types.
Genes 15 00151 g003
Table 1. Samples examined in this study and patients’ characteristics.
Table 1. Samples examined in this study and patients’ characteristics.
Number of PatientsBone MarrowPeripheral BloodMean Age (Range) Median AgeGender
AML24142451.1 (19–96)47.5F: 9
M: 15
ALL25142542.5 (19–96)36F: 8
M: 17
ALL_Ped25-258 (0–18) *7 *F: 8
M: 14
NA: 3
Control15-1533.7 (20–47)33F: 10
M: 5
Total892889
AML: acute myeloid leukemia; ALL: adult acute lymphoblastic leukemia; ALL_Ped: pediatric acute lymphoblastic leukemia; F: female; M: male; NA: not available. * These numbers were calculated with only 22 patients’ age information since the remaining 3 were not available.
Table 2. Selected endogenous genes for quantitative real-time PCR evaluation.
Table 2. Selected endogenous genes for quantitative real-time PCR evaluation.
Gene SymbolGene NameChromosome LocationFunctionAmplicon SizeAssay Number
ABL1Abelson murine leukemia viral oncogene human homolog 1Chr.9: 130713881–130887675Protein tyrosine kinase involved in a variety of cellular processes60Hs01104728_m1
ACTBβ-actinChr.7: 5527148–5530601 Cytoskeletal structural protein63Hs01060665_g1
GAPDHGlyceraldehyde-3-phosphate dehydrogenaseChr.12: 6534405–6538375Oxidoreductase in glycolysis and gluconeogenesis157Hs02786624_g1
HPRT1Hypoxanthine phosphoribosyl-transferase 1Chr.X: 134460165–134500668Purine synthesis in salvage pathway82Hs2800695_m1
RPLP0Ribosomal protein lateral stalk subunit P0Chr.12: 120196700–120201211Ribosomal protein translation76Hs00420895_gH
TBPTATA box binding proteinChr.6: 170554333–170572870Regulation of transcription DNA and component of the DNA-binding protein complex TFIID91Hs00427620_m1
Table 3. GeNorm endogenous gene stability value (M).
Table 3. GeNorm endogenous gene stability value (M).
ABLACTBGAPDHHPRTTBPRPLP0
ALL0.1130.1040.1280.1000.0980.121
AML0.0870.0830.0900.0980.0730.091
ALL_Ped0.0590.0660.0820.0590.0600.071
Control0.0450.0520.0670.0400.0440.044
GeNorm program did not allow the identification of the different groups in its analyses; therefore, each group was analyzed separately.
Table 4. NormFinder endogenous gene stability value ranking.
Table 4. NormFinder endogenous gene stability value ranking.
GeneStability Value
Overall *
1ACTB0.47
2RPLP00.52
3HPRT0.72
4ABL0.86
5TBP0.91
6GAPDH1.78
* The NormFinder normalizer program allowed a single analysis of all study samples subdivided by groups (AML, ALL, ALL_Ped and control), identifying the reference genes with the best overall performance.
Table 5. Software R endogenous candidates’ analysis.
Table 5. Software R endogenous candidates’ analysis.
ABLGAPDHACTBHPRTTBPRPLP0
Kruskal–Wallis p-value0.1760.0000.0460.0000.9140.368
Gene Standard Deviation2.4213.3521.6982.1502.4722.362
Sum of Mean Square Difference0.89322.5000.4873.2560.2860.385
Sum of Standard Deviation Square Difference2.4487.3960.6260.7151.2233.170
Table 6. Analysis of the previous studies of normalization and validation of endogenous genes compared to this current study.
Table 6. Analysis of the previous studies of normalization and validation of endogenous genes compared to this current study.
Model (Tissue Type)Number of PatientsAnalyzed SampleEndogenous Genes TestedEndogenous Genes with
Best Behavior		YearReference
Acute leukemias89Peripheral blood and bone marrowACTB, ABL, GAPDH, HPRT1, TBP and RPLP0ACTB, ABL, TBP and RPLP02024Present study
Acute myeloid Leukemia29Peripheral bloodABL1, G6PDH, B2M and PBGDG6PDH and ABL2004[103]
ALL, Ph-like B-lineage23Peripheral bloodABL1, GUSB, EEF2, 18S, ACTB, GAPDH, TBP, PGK1, B2M, JCHAIN, SPATS2L, CA6, NRXN3, MUC4, CRLF2, ADGRF1 and BMPR1BEEF2, GAPDH and PGK12023[95]
B-cell chronic lymphocytic leukemia30Peripheral bloodACTB, B2M, GAPDH, GUSB, HMBS, HPRT1, MRPL19, TBP and UBCB2M, HPRT1 and GUSB2010[71]
Bladder and colon cancer58Tumor biopsiesFLOT2, ATP5B, HSPCB, S100A6, TEGT, CFL1, FLJ20030, TPT1, UBB, TBC, RPS23, GAPD, ACTB, CLTC, NACA, SU11 and TUBA6UBC, GAPD and TPT1 for colon and HSPCB, TEGT and ATP5B for bladder2004[53]
Bladder cancer14Tissue biopsiesACTB, ALAS1, G6PD, GAPD, HMBS, HPRT1, K-α-1, SDHA and TBPSDH and TBP2006[106]
Breast cancer87Tumor biopsies and cell linesSF1, TARDBP, THRAP3, QRICH1, TRA2B, SRSF3, YY1, DNAJC8, RNF10 and RHOASF1, TRA2B, THRAP3, RHOA and QRICH12021[60]
Breast cancer-Cell line (MCF-10A)18S, 28S, ACTB, PPIA, GAP and RPL3218S and ACTB2005[88]
Breast cancer40Tumor biopsiesGAPDH, TFRC, RPLP0, GUSB, HPRT1, UPA and ACTBACTB and TFRC2011[89]
Breast cancer23Tumor biopsiesACTB, GAPD, TBP, SDHA, HPRT, HMBS, B2M, PPIA, GUSB, YWHAZ, PGK1, RPL41, PUM1, RPLP0, MRPL19, TTC22, IL22RA1 and ZNF224ACTB and SDHA2009[90]
Breast, gastric, esophageal, colon, rectum, and lung carcinomas327Tissue biopsiesACTB, GAPDH, GUSB, RPLPO and TFRCThe optimal reference genes were tissue-specific2014[115]
Brown adipose tissue-Tissue biopsies18S, B2M, GAPDH, LRP10, PPIA, RPLP0, UBC and YWHAZPSMB2, GNB2 and GNB12015[45]
Cancer stem cells-Cell lines (RD, MG63, HOS, Saos-2, A673, MDA-MB-231 and ACHN)18S, ACTB, B2M, G6PD, GAPDH, GUSB, HMBS, HPRT1, PGK1, PPIA, RPL13a, SDHA, TBP, TUBB and YWHAZGAPDH, TBP and PPIA2016[93]
Cervical cancer-Cell lines (SiHa, HeLa and ME180)ACTB, B2M, GAPDH, HPRT1 and TBPB2M, GAPDH, HPRT1 and TBP2018[62]
Colon-Rat tissue biopsiesGAPD, ACTB, Cyclophilin A, HPRT, AcRP0, L32, 18S and 28SAcRP02004[82]
Colon, breast, prostate, skin, and bladder16Tissue biopsiesLRP, BACT, CYC, GAPDH, PGK, B2M, BGUS, HPRT, TBO, TfR, PBGD and ATP6HPRT2005[83]
Colon, liver, pancreas, rectum, lung, cervix, ovary, prostate, umbilical, breast, spleen, etc.72Tissue biopsiesGAPDHGAPDH varies a lot between tissues2005[76]
Colorectal cancer64Peripheral bloodACTB, B2M, GAPDH, HPRT1, SDHA TBP, IL-1B and CCL4HPRT1, SDHA and TBP2020[73]
Endometrial cancer (type 1 or type 2)15Endometrial biopsiesRPL30, MT-ATP6, 18S, ACTB, TBP, RPLP0, PES1, POLR2A, TFRC, HPRT1, ABL1, GADD45A, HMBS, CDKN1A, RPL37A, UBC, GAPDH, CDKN1B, CASC3, POP4, PGK1, GUSB, YWHAZ, PPIA, RPS17, MRPL19, B2M, EIF2B1, ELF1, PSMC4, PUM1 and IPO8PSMC4, PUM1 and IPO8 for type 1 and UBC, MRPL19, PGK1 and PPIA for type 22020[51]
Glioblastoma30Tumor biopsiesACTB, GAPDH, GUSB, HMBS, HPRT1, TBP, 18S, TG1, TG2, TG3, TG4, TG5, TG6, TG7, TG8, GT9, TG10, TG11 and TG12TBP and HPRT12009[108]
Hepatocellular carcinoma65Tumor biopsiesACTB, GAPDH, B2M, HPRT1 and TBPTBP and HPRT2008[80]
Hippocampal tissue25Tissue biopsiesACTB, GAPDH, HPRT, NSE, SDHA and SYPHPRT, NSE, SDHA and SYP2012[81]
Hypoxia and hyperglycemia model-Umbilical cordsRPLP0, GAPDH, GUSB, TFRC and ACTBTFRC and RPLP02014[114]
Lung cancer-Cell lines (A549, NCI-H446 and NCI-H460)18S, GAPDH, RPLP0, ACTB, PPIA, PGK1, B2M, RPL13A, HPRT1 and TBPACTB, PPIA and PGK12015[113]
Lung, breast, colon, prostate, and pancreas326Tissue biopsiesHPRTHPRT should no longer be used as an endogenous standard2019[84]
Lymphoid malignancies92Cell lines, tumor biopsies and peripheral blood18S, RPLP0, GAPD, PPIA, PRKG1, TBP, ACTB, B2M and GUSBPRKG1 and TBP2003[104]
Melanoma-Cell lines (IC8 and T1C3)GAPDH, 18S and ACTB18S2001[91]
Myoblasts15Tissue biopsiesACTA1, MYOG, MYH3, ACTB, B2M, GAPDH, PPIA, RPLP0 and TBPRPLP0 and TBP2009[112]
Myocardial infarction-Mouse myocardial infarction tissue sampleACTB, B2M, EEF1A1, GAPDH, HPRT, POLR2A, PPIA, RPL13a, TBP and TPT1HPRT, RPL13A and TPT12011[79]
Oral cancer68SalivaB2M, MT-ATP6, RPL30, RPL37A, RPL0, RPS17 and UBCMT-ATP6, RPL30, RPL37A, RPLP0 and RPS172016[61]
Placenta20Placenta tissueB2M, GAPDH, HMBS, HPRT, SDHA, TBP, YWHAZ and LEPSDHA, TBP and YWHAZ2004[105]
Renal cell carcinoma25Tissue biopsiesACTB, ALASI, GAPDH, HMBS, HPRT1, PPIA, RPLP0, SDHA, TBP, TUBB and ADAM9PPIA and TBP2007[92]
Wound healing model-Skin and wound samples from miceB2M, TBP, GAPDH, GUSB, RPLP2, ACTB and 18SGAPDH, TBP and B2M2010[107]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Pessoa, F.M.C.d.P.; Viana, V.B.d.J.; de Oliveira, M.B.; Nogueira, B.M.D.; Ribeiro, R.M.; Oliveira, D.d.S.; Lopes, G.S.; Vieira, R.P.G.; de Moraes Filho, M.O.; de Moraes, M.E.A.; et al. Validation of Endogenous Control Genes by Real-Time Quantitative Reverse Transcriptase Polymerase Chain Reaction for Acute Leukemia Gene Expression Studies. Genes 2024, 15, 151. https://doi.org/10.3390/genes15020151

AMA Style

Pessoa FMCdP, Viana VBdJ, de Oliveira MB, Nogueira BMD, Ribeiro RM, Oliveira DdS, Lopes GS, Vieira RPG, de Moraes Filho MO, de Moraes MEA, et al. Validation of Endogenous Control Genes by Real-Time Quantitative Reverse Transcriptase Polymerase Chain Reaction for Acute Leukemia Gene Expression Studies. Genes. 2024; 15(2):151. https://doi.org/10.3390/genes15020151

Chicago/Turabian Style

Pessoa, Flávia Melo Cunha de Pinho, Vitória Beatriz de Jesus Viana, Marcelo Braga de Oliveira, Beatriz Maria Dias Nogueira, Rodrigo Monteiro Ribeiro, Deivide de Sousa Oliveira, Germison Silva Lopes, Ricardo Parente Garcia Vieira, Manoel Odorico de Moraes Filho, Maria Elisabete Amaral de Moraes, and et al. 2024. "Validation of Endogenous Control Genes by Real-Time Quantitative Reverse Transcriptase Polymerase Chain Reaction for Acute Leukemia Gene Expression Studies" Genes 15, no. 2: 151. https://doi.org/10.3390/genes15020151

APA Style

Pessoa, F. M. C. d. P., Viana, V. B. d. J., de Oliveira, M. B., Nogueira, B. M. D., Ribeiro, R. M., Oliveira, D. d. S., Lopes, G. S., Vieira, R. P. G., de Moraes Filho, M. O., de Moraes, M. E. A., Khayat, A. S., Moreira, F. C., & Moreira-Nunes, C. A. (2024). Validation of Endogenous Control Genes by Real-Time Quantitative Reverse Transcriptase Polymerase Chain Reaction for Acute Leukemia Gene Expression Studies. Genes, 15(2), 151. https://doi.org/10.3390/genes15020151

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop