enoriega/keyword_pubmed · Datasets at Hugging Face

sentence stringlengths 1 5.95k	pmcid stringlengths 9 10	keyword_rank int32 -1 169k	keyword_indices sequence
The expression of RTDR1 , HCRT , APOC4 and KRTAP12-3 could not be determined in all 64 tissue samples , resultantly these candidates were excluded from further analysis.Mean C values for the remaining genes ranged from 19.48 ( ± 0.14 s.e.m ) for B2M to 32.30 ( ± 0.19 s.e.m ) for CHRNB4 .	PMC2825202	1,498	[ 7 ]
B2M displayed the narrowest range of C values between 17.5 and 21.5 ( mean 19.5 ± 0.14 s.e.m , range of 4.04 ) followed by PPIA and MRPL19 , while ACTB had the widest range of C values between 33.8 and 21.1 .	PMC2825202	14,380	[ 27 ]
The genes broadly fell into three categories , those least abundant genes with mean C values of 27-32 ( SLC25A23 , MRPL19 , DTX3 and CHRNB4 ) , moderately abundant genes with mean C values of 22-26 ( HPRT and ACTB ) and the most abundant highly expressed genes with mean C values of 19-21 ( B2M , PPIA and GAPDH ) Table 2 .	PMC2825202	14,380	[ 21 ]
Identification of Optimal EC genes .	PMC2825202	-1	[]
Scaled expression levels across the remaining nine candidate ECs analysed ( figure 1 ) indicated within-gene differences in expression between tumour and normal tissue groups in both SLC25A23 ( p = 0.040 ) and CHRNB4 ( P = 0.002 ) but not in the remaining genes ( p > 0.05 ) , ( figure 2A ) .	PMC2825202	-1	[]
Therefore , SLC25A23 and CHRNB4 genes were excluded from further analysis .	PMC2825202	-1	[]
Significant differences in variance of EC expression were identified using Levene 's test ( p < 0.001 , figure 2B ) .	PMC2825202	-1	[]
These findings necessitated further evaluation of each candidate EC gene prior to their possible use to accurately quantitate gene expression levels of the target genes CXCL12 , FABP1 , MUC2 and PDCD4.The stability of candidate EC genes was analysed using geNorm [ 8] and NormFinder [ 35 ] programmes .	PMC2825202	-1	[]
Stability was further evaluated using qBasePlus [ 8,36 ] , a commercially available RQ-PCR data mining package .	PMC2825202	16,786	[ 5 ]
These programmes were used to calculate amplification efficiency-corrected relative quantities from raw fluorescence data .	PMC2825202	-1	[]
The ranking of candidate EC genes as determined by each of these programmes is illustrated in Table 3 .	PMC2825202	-1	[]
In the case of GeNorm the variable V indicating the pairwise variation ( Vn / Vn +1 ) between two sequential normalisation factors ( NFn / NFn +1 ) indicated that three EC genes was the optimal number of genes for accurate normalisation ( figure 2 ) , however , target genes expression did not differ significantly if two rather than three EC genes were used ( figure 3 ) .	PMC2825202	-1	[]
Use of all three programmes confirmed that B2M and PPIA was the best combination of genes for normalising RQ-PCR data in CRC tissues ( table 3 ) .	PMC2825202	-1	[]
The Equivalence test [ 37 ] was used to examine the expression of candidate ECs .	PMC2825202	-1	[]
All genes were equivalently expressed between the normal and tumour colorectal tissues using a fold cut-off of 2 ( figure 4 ) .	PMC2825202	-1	[]
Association between EC genes and target genes .	PMC2825202	-1	[]
There was a significant effect of the expression of the candidate EC genes on relative expression of CXCL12 ( p < 0.001 ) , FABP1 ( p < 0.001 ) , MUC2 ( p < 0.001 ) and PDCD4 ( p < 0.001 ) ( figure 5A and 5B ) .	PMC2825202	-1	[]
Moreover , a significant effect of the choice of EC with regard to the estimation of error ( figure 5C ) was also detected .	PMC2825202	-1	[]
These findings were further confirmed for each EC gene compared to each other by ANOVA Tukey post hoc tests ( Additional files 1 ) .	PMC2825202	-1	[]
The combined use of B2M and PPIA significantly reduced the magnitude of error in comparison to the use of either gene individually for both CXCL12 and PDCD4 expression .	PMC2825202	-1	[]
The addition of a third EC gene ( MRPL19 ) to the B2M / PPIA combination did not result in any further improvement of the estimation of error ( figure 3C ) .	PMC2825202	14,380	[ 8 ]
Non-normalised expression levels of target genes .	PMC2825202	-1	[]
To assess whether normalisation was necessary in a large cohort such as this in which the biological effect of the target genes is already established , we compared the expression of the four target genes in tumour vs. normal tissues using non normalised cycle threshold ( C ) values in the entire sample set ( n = 64 ) and in a sub-set of randomly selected 10 normal and 10 tumour tissues ( n = 20 ) .	PMC2825202	-1	[]
This analysis showed down-regulated target gene ( CXCL12 , FABP1 , MUC2 and PDCD4 ) expression in tumour compared to normal tissues ( figure 6 ) , in keeping with their documented tumour suppressor functions , when using the larger set of samples .	PMC2825202	-1	[]
The unchanged target gene expression levels in the large cohort could be explained by the fact that in larger sample sizes the biological milieu may diminish subtle variations in individual samples .	PMC2825202	-1	[]
In contrast , when the smaller sample size was used , no significant differences in target gene expression were observed .	PMC2825202	-1	[]
Furthermore the expression levels of PDCD4 appeared slightly higher in tumours than in normal tissues .	PMC2825202	-1	[]
When the same subset of 20 samples were normalised with PPIA / B2M , significant differences in target gene expression were observed .	PMC2825202	-1	[]
Discussion .	PMC2825202	-1	[]
Since its introduction in 1996 [ 38 ] many methods have been developed for the analysis real-time quantitative PCR data .	PMC2825202	-1	[]
Relative quantification has come to the fore as the method of choice due to its superior flexibility and reduction in inherent variation associated with sample preparation .	PMC2825202	-1	[]
Prior to the availability of high-throughput realtime PCR instrumentation , a handful of genes were commonly used to normalise real-time data .	PMC2825202	-1	[]
Major technological advances enabling high throughput analysis of both samples and target genes have enabled investigation and validation of putative EC genes prior to their use to normalise target gene expression .	PMC2825202	-1	[]
It is now accepted that the use of more than one gene to normalise RQ data improves experimental accuracy compared to the use of a single EC gene [ 8,35,38 ] In their study of EC gene expression in breast and colon cancer tissues Tricarico et al [ 39 ] illustrated significant variation in the expression levels of 10 commonly used housekeeping genes including 18S rRNA , both between individuals and between biopsies taken from the same patient .	PMC2825202	-1	[]
They concluded that normalisation to a single EC gene was inappropriate for human tissue samples .	PMC2825202	-1	[]
Moreover , Vandesompele et al identified errors of up to 6.5 fold when a single EC gene was used in comparison to the use of multiple genes for data normalisation [ 8] thereby clearly indicating the potential for superior accuracy when due consideration is paid to the choice of EC genes.Many analytical programmes for relative quantification have been developed , certain of which enable the identification of EC genes from a study population [ 37,40,41 ] .	PMC2825202	-1	[]
In the present study the stability of expression of candidate EC genes was determined using a pair-wise comparison model : geNorm [ 8] and an MS Excel ANOVA based model , NormFinder [ 35 ] .	PMC2825202	-1	[]
No effect of disease status EC gene expression was identified in colorectal tissue .	PMC2825202	-1	[]
Since both geNorm and NormFinder are based on the assumption that candidate genes are not differentially expressed between samples , this was an important first step prior to their continued use [ 9,10 ] .	PMC2825202	-1	[]
In this study GeNorm was used to identify the most stably expressed EC genes from our panel of candidates and also provided a measure of the optimal number of EC genes .	PMC2825202	-1	[]
B2M and PPIA were identified as the most stable pairing .	PMC2825202	-1	[]
In order to achieve a pair-wise variation value ( V ) below the cut-off of 0.15 additional genes should theoretically be used ; however this cut-off point is not absolute [ 14 ] and may not always be achievable [ 42 ] .	PMC2825202	-1	[]
No significant difference in target gene expression was observed when the top three most stable EC genes identified by geNorm were used confirming that using of a pair of genes may be more practicable given cost , work load and sample availability considerations.NormFinder was designed to identify EC genes with the lowest stability values ; these values are calculated based on intra - and inter-group variation .	PMC2825202	-1	[]
In this study NormFinder was used to define the best combination of genes using tumour and normal as group identifiers in the calculations .	PMC2825202	-1	[]
MRPL19 was selected as the most stable gene using these criteria ; however B2M and PPIA were highlighted as the best combination of genes with even lower stability value compared to MRPL19 alone .	PMC2825202	14,380	[ 0, 31 ]
QBasePlus real-time PCR data manager programme was developed based on geNorm and qBase [ 36 ] algorithms .	PMC2825202	16,786	[ 0 ]
QBasePlus was used to confirm our selection of the B2M and PPIA pairing as the best combination of ECs in colorectal tissue.Equivalence testing was developed in biostatistics to address the situation where the aim is not to show the difference between groups , but rather to establish that two methods are equal to one another .	PMC2825202	16,786	[ 0 ]
In equivalence testing , the null hypothesis is that the two groups are not equivalent to one another , and hence rejection of the null indicates that the two groups are equivalent .	PMC2825202	-1	[]
Therefore , as stated by Haller et al , there is a risk of accepting non differentially expressed genes as suitable controls although they are not equivalently expressed [ 43 ] .	PMC2825202	-1	[]
Equivalence of expression between tumour and normal colorectal tissue was confirmed for all candidate EC genes using the equivalence test and a fold cut-off of 2 .	PMC2825202	-1	[]
DTX3 , B2M , MRPL19 and PPIA showed the minimum of variability in the confidence interval hence can be used for normalisation.In their study to identify EC genes to monitor enterocyte differentiation and to compare normal and adenocarcinoma of the colon from microarray data , Dydensborg et al [ 5 ] recommended RPLP0 for normalising gene quantification in human intestinal epithelial cells and B2M for studying gene expression in human colon cancer .	PMC2825202	14,380	[ 4 ]
In addition , Blanquicett et al [ 44 ] analysed the extent of variability in gene expression between tumour and normal colorectal and liver tissues using two-tailed T tests .	PMC2825202	-1	[]
They showed that 18S , S9 and GUS were the least variable genes in normal and metastatic liver specimens and were also appropriate for normal and tumour colorectal tissues .	PMC2825202	-1	[]
In the present study , we confirmed that more than one EC gene is required for optimal normalisation in colorectal tissue .	PMC2825202	-1	[]
We used clinico-pathologically diverse tissues to systematically evaluate normalisation of gene expression data in colorectal tissues .	PMC2825202	-1	[]
We also conducted equivalence testing to confirm the equality of expression of each EC gene .	PMC2825202	-1	[]
Thereby , the risks of incorrect rejection ( type 1 error ) and of false negativity ( type 2 error ) were minimised.As stated above significant differences in target gene expression were noticed when using each of the EC genes and the combination of PPIA and B2M .	PMC2825202	-1	[]
Moreover , significant effect of EC on the magnitude of error associated with estimation of target gene expression was also determined in this study ( figure 6 ) .	PMC2825202	-1	[]
Our results were further confirmed by post hoc testing of individual levels of EC gene expression ( Additional files 1 ) .	PMC2825202	-1	[]
Reduction in the magnitude of error achieved using the combination of PPIA and B2M in comparison to using individual EC genes alone , further indicates that using two EC genes to normalise real-time data achieves greater accuracy in the determination of gene expression levels .	PMC2825202	-1	[]
Conclusions .	PMC2825202	-1	[]
The findings reported in this study confirm that use of two EC genes to normalise RQ-PCR data resulted in superior accuracy in the quantification of gene expression in colorectal tissue .	PMC2825202	-1	[]
The combined use of B2M and PPIA was validated as the optimal pair of EC genes with which to estimate the expression of all four target genes in colorectal cancer tissue .	PMC2825202	-1	[]
Although these ECs may not be ideal in other tissue types , the approach described herein could serve as a template to identify valid ECs in other tissue types .	PMC2825202	-1	[]
Methods .	PMC2825202	-1	[]
Tissue Samples .	PMC2825202	-1	[]
A study group of 64 biopsies of human colon tissue samples was gathered from consenting patients at the time of primary curative surgical resection at Galway University Hospital , Ireland .	PMC2825202	-1	[]
The cohort comprised of 30 colorectal tumour specimens and 34 and tumour-associated normal ( TAN ) tissues .	PMC2825202	-1	[]
Following excision , all samples were subject to histopathological review prior immediate snap-freezing in liquid nitrogen and archival at -80 ° C until further use .	PMC2825202	-1	[]
Concomitant clinicopathological data on patients and specimens was obtained from the Department of Surgery Biobank , NUI Galway as detailed in Table 4 .	PMC2825202	-1	[]
Ethical approval for this study was granted by the Clinical Research Ethics Committee , Galway University Hospitals .	PMC2825202	-1	[]
RNA Extraction and Analysis .	PMC2825202	-1	[]
Tissue samples ( 50-100 mg ) were homogenised using a hand-held homogenizer ( Polytron PT1600E , Kinematica AG , Littau-Luzem , Switzerland ) in 1-2 ml of QIAzol reagent ( Qiagen , Crawley , UK ) .	PMC2825202	-1	[]
To minimise variation in sample processing , tumour and TAN samples were homogenised separately , but on the same day .	PMC2825202	-1	[]
RNA was extracted as previously described ( Davoren et al ) using the RNeasy Plus Mini Kit and RNeasy MinElute cleanup kit ( Qiagen , Crawley , UK ) according to the manufacturer 's instructions .	PMC2825202	-1	[]
Briefly , large ( > 200 nt ) and small RNA ( < 200 nt ) fractions were isolated separately .	PMC2825202	-1	[]
For this study , only large RNA was utilised for further analysis .	PMC2825202	-1	[]
RNA was eluted in 60 μ l volumes and stored at -80 ° C.RNA concentration and purity was assessed in duplicate samples using a using a NanoDrop ™ ND-1000 Spectrophotometer ( Thermo Fisher Scientific , USA ) .	PMC2825202	-1	[]
RNA integrity was evaluated using the RNA 6000 Nano Chip Kit ( Series II ) and the Agilent 2100 Bioanalyzer System ( Agilent technologies , Palo Alto , CA , USA ) .	PMC2825202	-1	[]
An RNA integrity number ( RIN ) was generated for each sample using the Agilent 2100 Expert Software ( Version B.02.03 ) based on the ratio of ribosomal bands and also the presence or absence of degradation products on the electrophoretic and gel-like images .	PMC2825202	-1	[]
A threshold value of RIN ≥ 7 was applied and RNA purity was verified by an average A260 / A280 ratio of 1.98 ( range 1.97-2.01 ) and A260 / A230 ration of 1.7 ( range 1.5-1.83 ) .	PMC2825202	-1	[]
Candidate Endogenous Control Genes .	PMC2825202	-1	[]
Based on literature search six commonly used candidate endogenous control genes were selected for analysis : ACTB , GAPDH , HPRT , B2M , PPIA and MRPL19 .	PMC2825202	14,380	[ 26 ]
An additional panel of seven genes : HCRT , SLC25A23 , DTX3 , APOC4 , RTDR1 , KRTAP12-3 and CHRNB4 , was also selected for analysis ( Table 2 ) .	PMC2825202	1,498	[ 13 ]
To our knowledge all genes have independent cellular functions and were assumed not to be co-regulated .	PMC2825202	-1	[]
cDNA Synthesis and RQ-PCR .	PMC2825202	-1	[]
First strand cDNA was synthesised using Superscript ™ III reverse transcriptase ( Invitrogen Life technologies , Paisley , UK ) and random primers ( N9 ; 1 μ g , MWG Biotech , AG , Ebersberg , Germany ) .	PMC2825202	-1	[]
Negative control samples were included in each set of reactions .	PMC2825202	-1	[]
Reactions were incubated at 25 ° C for 5 minutes followed by 50 ° C for 1 hour and final denaturation at 72 ° C for 15 minutes .	PMC2825202	-1	[]
Samples were subsequently diluted to 50 μ L in nuclease-free water and stored at -20 ° C.	PMC2825202	-1	[]
The expression of each EC gene was analysed by RQ-PCR using TaqMan gene expression assays using a 7900HT instrument ( Applied Biosystems , Foster city , USA ) .	PMC2825202	-1	[]
All reactions were performed in 20 μ L reactions , in triplicate within the same PCR run .	PMC2825202	-1	[]
Negative controls were included for each gene target under assay .	PMC2825202	-1	[]
On each plate , an interassay control was included to account for any variations between runs .	PMC2825202	-1	[]
For each well 2 μ l of cDNA from each sample was added to 18 μ l of PCR reaction mix which consisted of 10 × TaqMan universal master mix , No AmpErase UNG , 7 × nuclease free water and 1 × gene expression assay primer-probe mix ( Applied Biosystems , Foster city , USA ) .	PMC2825202	-1	[]
The PCR reactions were initiated with a 10 minute incubation at 95 ° C followed by 40 cycles of 95 ° C for 15 seconds and 60 ° C for 60 seconds , in accordance with the manufacturer 's recommendations .	PMC2825202	-1	[]
PCR Amplification Efficiency .	PMC2825202	-1	[]
Amplification efficiencies for each EC gene assay were calculated applying the formula E = ( 10-1 / slope - 1 ) × 100 , using the slope of the plot of Ct versus log input of cDNA ( 10-fold dilution series ) .	PMC2825202	-1	[]
A threshold of 10 % above and below 100 % efficiency was applied .	PMC2825202	-1	[]
PCR amplification efficiency for each candidate EC gene is shown in table 2 .	PMC2825202	-1	[]

The expression of RTDR1 , HCRT , APOC4 and KRTAP12-3 could not be determined in all 64 tissue samples , resultantly these candidates were excluded from further analysis.Mean C values for the remaining genes ranged from 19.48 ( ± 0.14 s.e.m ) for B2M to 32.30 ( ± 0.19 s.e.m ) for CHRNB4 .

PMC2825202

1,498

[ 7 ]

B2M displayed the narrowest range of C values between 17.5 and 21.5 ( mean 19.5 ± 0.14 s.e.m , range of 4.04 ) followed by PPIA and MRPL19 , while ACTB had the widest range of C values between 33.8 and 21.1 .

PMC2825202

14,380

[ 27 ]

The genes broadly fell into three categories , those least abundant genes with mean C values of 27-32 ( SLC25A23 , MRPL19 , DTX3 and CHRNB4 ) , moderately abundant genes with mean C values of 22-26 ( HPRT and ACTB ) and the most abundant highly expressed genes with mean C values of 19-21 ( B2M , PPIA and GAPDH ) Table 2 .

PMC2825202

14,380

[ 21 ]

Identification of Optimal EC genes .

PMC2825202

-1

[]

Scaled expression levels across the remaining nine candidate ECs analysed ( figure 1 ) indicated within-gene differences in expression between tumour and normal tissue groups in both SLC25A23 ( p = 0.040 ) and CHRNB4 ( P = 0.002 ) but not in the remaining genes ( p > 0.05 ) , ( figure 2A ) .

PMC2825202

-1

[]

Therefore , SLC25A23 and CHRNB4 genes were excluded from further analysis .

PMC2825202

-1

[]

Significant differences in variance of EC expression were identified using Levene 's test ( p < 0.001 , figure 2B ) .

PMC2825202

-1

[]

These findings necessitated further evaluation of each candidate EC gene prior to their possible use to accurately quantitate gene expression levels of the target genes CXCL12 , FABP1 , MUC2 and PDCD4.The stability of candidate EC genes was analysed using geNorm [ 8] and NormFinder [ 35 ] programmes .

PMC2825202

-1

[]

Stability was further evaluated using qBasePlus [ 8,36 ] , a commercially available RQ-PCR data mining package .

PMC2825202

16,786

[ 5 ]

These programmes were used to calculate amplification efficiency-corrected relative quantities from raw fluorescence data .

PMC2825202

-1

[]

The ranking of candidate EC genes as determined by each of these programmes is illustrated in Table 3 .

PMC2825202

-1

[]

In the case of GeNorm the variable V indicating the pairwise variation ( Vn / Vn +1 ) between two sequential normalisation factors ( NFn / NFn +1 ) indicated that three EC genes was the optimal number of genes for accurate normalisation ( figure 2 ) , however , target genes expression did not differ significantly if two rather than three EC genes were used ( figure 3 ) .

PMC2825202

-1

[]

Use of all three programmes confirmed that B2M and PPIA was the best combination of genes for normalising RQ-PCR data in CRC tissues ( table 3 ) .

PMC2825202

-1

[]

The Equivalence test [ 37 ] was used to examine the expression of candidate ECs .

PMC2825202

-1

[]

All genes were equivalently expressed between the normal and tumour colorectal tissues using a fold cut-off of 2 ( figure 4 ) .

PMC2825202

-1

[]

Association between EC genes and target genes .

PMC2825202

-1

[]

There was a significant effect of the expression of the candidate EC genes on relative expression of CXCL12 ( p < 0.001 ) , FABP1 ( p < 0.001 ) , MUC2 ( p < 0.001 ) and PDCD4 ( p < 0.001 ) ( figure 5A and 5B ) .

PMC2825202

-1

[]

Moreover , a significant effect of the choice of EC with regard to the estimation of error ( figure 5C ) was also detected .

PMC2825202

-1

[]

These findings were further confirmed for each EC gene compared to each other by ANOVA Tukey post hoc tests ( Additional files 1 ) .

PMC2825202

-1

[]

The combined use of B2M and PPIA significantly reduced the magnitude of error in comparison to the use of either gene individually for both CXCL12 and PDCD4 expression .

PMC2825202

-1

[]

The addition of a third EC gene ( MRPL19 ) to the B2M / PPIA combination did not result in any further improvement of the estimation of error ( figure 3C ) .

PMC2825202

14,380

[ 8 ]

Non-normalised expression levels of target genes .

PMC2825202

-1

[]

To assess whether normalisation was necessary in a large cohort such as this in which the biological effect of the target genes is already established , we compared the expression of the four target genes in tumour vs. normal tissues using non normalised cycle threshold ( C ) values in the entire sample set ( n = 64 ) and in a sub-set of randomly selected 10 normal and 10 tumour tissues ( n = 20 ) .

PMC2825202

-1

[]

This analysis showed down-regulated target gene ( CXCL12 , FABP1 , MUC2 and PDCD4 ) expression in tumour compared to normal tissues ( figure 6 ) , in keeping with their documented tumour suppressor functions , when using the larger set of samples .

PMC2825202

-1

[]

The unchanged target gene expression levels in the large cohort could be explained by the fact that in larger sample sizes the biological milieu may diminish subtle variations in individual samples .

PMC2825202

-1

[]

In contrast , when the smaller sample size was used , no significant differences in target gene expression were observed .

PMC2825202

-1

[]

Furthermore the expression levels of PDCD4 appeared slightly higher in tumours than in normal tissues .

PMC2825202

-1

[]

When the same subset of 20 samples were normalised with PPIA / B2M , significant differences in target gene expression were observed .

PMC2825202

-1

[]

Discussion .

PMC2825202

-1

[]

Since its introduction in 1996 [ 38 ] many methods have been developed for the analysis real-time quantitative PCR data .

PMC2825202

-1

[]

Relative quantification has come to the fore as the method of choice due to its superior flexibility and reduction in inherent variation associated with sample preparation .

PMC2825202

-1

[]

Prior to the availability of high-throughput realtime PCR instrumentation , a handful of genes were commonly used to normalise real-time data .

PMC2825202

-1

[]

Major technological advances enabling high throughput analysis of both samples and target genes have enabled investigation and validation of putative EC genes prior to their use to normalise target gene expression .

PMC2825202

-1

[]

It is now accepted that the use of more than one gene to normalise RQ data improves experimental accuracy compared to the use of a single EC gene [ 8,35,38 ] In their study of EC gene expression in breast and colon cancer tissues Tricarico et al [ 39 ] illustrated significant variation in the expression levels of 10 commonly used housekeeping genes including 18S rRNA , both between individuals and between biopsies taken from the same patient .

PMC2825202

-1

[]

They concluded that normalisation to a single EC gene was inappropriate for human tissue samples .

PMC2825202

-1

[]

Moreover , Vandesompele et al identified errors of up to 6.5 fold when a single EC gene was used in comparison to the use of multiple genes for data normalisation [ 8] thereby clearly indicating the potential for superior accuracy when due consideration is paid to the choice of EC genes.Many analytical programmes for relative quantification have been developed , certain of which enable the identification of EC genes from a study population [ 37,40,41 ] .

PMC2825202

-1

[]

In the present study the stability of expression of candidate EC genes was determined using a pair-wise comparison model : geNorm [ 8] and an MS Excel ANOVA based model , NormFinder [ 35 ] .

PMC2825202

-1

[]

No effect of disease status EC gene expression was identified in colorectal tissue .

PMC2825202

-1

[]

Since both geNorm and NormFinder are based on the assumption that candidate genes are not differentially expressed between samples , this was an important first step prior to their continued use [ 9,10 ] .

PMC2825202

-1

[]

In this study GeNorm was used to identify the most stably expressed EC genes from our panel of candidates and also provided a measure of the optimal number of EC genes .

PMC2825202

-1

[]

B2M and PPIA were identified as the most stable pairing .

PMC2825202

-1

[]

In order to achieve a pair-wise variation value ( V ) below the cut-off of 0.15 additional genes should theoretically be used ; however this cut-off point is not absolute [ 14 ] and may not always be achievable [ 42 ] .

PMC2825202

-1

[]

No significant difference in target gene expression was observed when the top three most stable EC genes identified by geNorm were used confirming that using of a pair of genes may be more practicable given cost , work load and sample availability considerations.NormFinder was designed to identify EC genes with the lowest stability values ; these values are calculated based on intra - and inter-group variation .

PMC2825202

-1

[]

In this study NormFinder was used to define the best combination of genes using tumour and normal as group identifiers in the calculations .

PMC2825202

-1

[]

MRPL19 was selected as the most stable gene using these criteria ; however B2M and PPIA were highlighted as the best combination of genes with even lower stability value compared to MRPL19 alone .

PMC2825202

14,380

[ 0, 31 ]

QBasePlus real-time PCR data manager programme was developed based on geNorm and qBase [ 36 ] algorithms .

PMC2825202

16,786

[ 0 ]

QBasePlus was used to confirm our selection of the B2M and PPIA pairing as the best combination of ECs in colorectal tissue.Equivalence testing was developed in biostatistics to address the situation where the aim is not to show the difference between groups , but rather to establish that two methods are equal to one another .

PMC2825202

16,786

[ 0 ]

In equivalence testing , the null hypothesis is that the two groups are not equivalent to one another , and hence rejection of the null indicates that the two groups are equivalent .

PMC2825202

-1

[]

Therefore , as stated by Haller et al , there is a risk of accepting non differentially expressed genes as suitable controls although they are not equivalently expressed [ 43 ] .

PMC2825202

-1

[]

Equivalence of expression between tumour and normal colorectal tissue was confirmed for all candidate EC genes using the equivalence test and a fold cut-off of 2 .

PMC2825202

-1

[]

DTX3 , B2M , MRPL19 and PPIA showed the minimum of variability in the confidence interval hence can be used for normalisation.In their study to identify EC genes to monitor enterocyte differentiation and to compare normal and adenocarcinoma of the colon from microarray data , Dydensborg et al [ 5 ] recommended RPLP0 for normalising gene quantification in human intestinal epithelial cells and B2M for studying gene expression in human colon cancer .

PMC2825202

14,380

[ 4 ]

In addition , Blanquicett et al [ 44 ] analysed the extent of variability in gene expression between tumour and normal colorectal and liver tissues using two-tailed T tests .

PMC2825202

-1

[]

They showed that 18S , S9 and GUS were the least variable genes in normal and metastatic liver specimens and were also appropriate for normal and tumour colorectal tissues .

PMC2825202

-1

[]

In the present study , we confirmed that more than one EC gene is required for optimal normalisation in colorectal tissue .

PMC2825202

-1

[]

We used clinico-pathologically diverse tissues to systematically evaluate normalisation of gene expression data in colorectal tissues .

PMC2825202

-1

[]

We also conducted equivalence testing to confirm the equality of expression of each EC gene .

PMC2825202

-1

[]

Thereby , the risks of incorrect rejection ( type 1 error ) and of false negativity ( type 2 error ) were minimised.As stated above significant differences in target gene expression were noticed when using each of the EC genes and the combination of PPIA and B2M .

PMC2825202

-1

[]

Moreover , significant effect of EC on the magnitude of error associated with estimation of target gene expression was also determined in this study ( figure 6 ) .

PMC2825202

-1

[]

Our results were further confirmed by post hoc testing of individual levels of EC gene expression ( Additional files 1 ) .

PMC2825202

-1

[]

Reduction in the magnitude of error achieved using the combination of PPIA and B2M in comparison to using individual EC genes alone , further indicates that using two EC genes to normalise real-time data achieves greater accuracy in the determination of gene expression levels .

PMC2825202

-1

[]

Conclusions .

PMC2825202

-1

[]

The findings reported in this study confirm that use of two EC genes to normalise RQ-PCR data resulted in superior accuracy in the quantification of gene expression in colorectal tissue .

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The combined use of B2M and PPIA was validated as the optimal pair of EC genes with which to estimate the expression of all four target genes in colorectal cancer tissue .

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Although these ECs may not be ideal in other tissue types , the approach described herein could serve as a template to identify valid ECs in other tissue types .

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Methods .

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Tissue Samples .

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A study group of 64 biopsies of human colon tissue samples was gathered from consenting patients at the time of primary curative surgical resection at Galway University Hospital , Ireland .

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The cohort comprised of 30 colorectal tumour specimens and 34 and tumour-associated normal ( TAN ) tissues .

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Following excision , all samples were subject to histopathological review prior immediate snap-freezing in liquid nitrogen and archival at -80 ° C until further use .

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Concomitant clinicopathological data on patients and specimens was obtained from the Department of Surgery Biobank , NUI Galway as detailed in Table 4 .

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Ethical approval for this study was granted by the Clinical Research Ethics Committee , Galway University Hospitals .

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RNA Extraction and Analysis .

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Tissue samples ( 50-100 mg ) were homogenised using a hand-held homogenizer ( Polytron PT1600E , Kinematica AG , Littau-Luzem , Switzerland ) in 1-2 ml of QIAzol reagent ( Qiagen , Crawley , UK ) .

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To minimise variation in sample processing , tumour and TAN samples were homogenised separately , but on the same day .

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RNA was extracted as previously described ( Davoren et al ) using the RNeasy Plus Mini Kit and RNeasy MinElute cleanup kit ( Qiagen , Crawley , UK ) according to the manufacturer 's instructions .

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Briefly , large ( > 200 nt ) and small RNA ( < 200 nt ) fractions were isolated separately .

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For this study , only large RNA was utilised for further analysis .

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RNA was eluted in 60 μ l volumes and stored at -80 ° C.RNA concentration and purity was assessed in duplicate samples using a using a NanoDrop ™ ND-1000 Spectrophotometer ( Thermo Fisher Scientific , USA ) .

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RNA integrity was evaluated using the RNA 6000 Nano Chip Kit ( Series II ) and the Agilent 2100 Bioanalyzer System ( Agilent technologies , Palo Alto , CA , USA ) .

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An RNA integrity number ( RIN ) was generated for each sample using the Agilent 2100 Expert Software ( Version B.02.03 ) based on the ratio of ribosomal bands and also the presence or absence of degradation products on the electrophoretic and gel-like images .

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A threshold value of RIN ≥ 7 was applied and RNA purity was verified by an average A260 / A280 ratio of 1.98 ( range 1.97-2.01 ) and A260 / A230 ration of 1.7 ( range 1.5-1.83 ) .

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Candidate Endogenous Control Genes .

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Based on literature search six commonly used candidate endogenous control genes were selected for analysis : ACTB , GAPDH , HPRT , B2M , PPIA and MRPL19 .

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[ 26 ]

An additional panel of seven genes : HCRT , SLC25A23 , DTX3 , APOC4 , RTDR1 , KRTAP12-3 and CHRNB4 , was also selected for analysis ( Table 2 ) .

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To our knowledge all genes have independent cellular functions and were assumed not to be co-regulated .

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cDNA Synthesis and RQ-PCR .

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First strand cDNA was synthesised using Superscript ™ III reverse transcriptase ( Invitrogen Life technologies , Paisley , UK ) and random primers ( N9 ; 1 μ g , MWG Biotech , AG , Ebersberg , Germany ) .

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Negative control samples were included in each set of reactions .

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Reactions were incubated at 25 ° C for 5 minutes followed by 50 ° C for 1 hour and final denaturation at 72 ° C for 15 minutes .

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Samples were subsequently diluted to 50 μ L in nuclease-free water and stored at -20 ° C.

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The expression of each EC gene was analysed by RQ-PCR using TaqMan gene expression assays using a 7900HT instrument ( Applied Biosystems , Foster city , USA ) .

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All reactions were performed in 20 μ L reactions , in triplicate within the same PCR run .

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Negative controls were included for each gene target under assay .

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On each plate , an interassay control was included to account for any variations between runs .

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For each well 2 μ l of cDNA from each sample was added to 18 μ l of PCR reaction mix which consisted of 10 × TaqMan universal master mix , No AmpErase UNG , 7 × nuclease free water and 1 × gene expression assay primer-probe mix ( Applied Biosystems , Foster city , USA ) .

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The PCR reactions were initiated with a 10 minute incubation at 95 ° C followed by 40 cycles of 95 ° C for 15 seconds and 60 ° C for 60 seconds , in accordance with the manufacturer 's recommendations .

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PCR Amplification Efficiency .

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Amplification efficiencies for each EC gene assay were calculated applying the formula E = ( 10-1 / slope - 1 ) × 100 , using the slope of the plot of Ct versus log input of cDNA ( 10-fold dilution series ) .

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A threshold of 10 % above and below 100 % efficiency was applied .

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PCR amplification efficiency for each candidate EC gene is shown in table 2 .

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