Identification and Validation of Reference Genes for RT-qPCR Studies of Hypoxia in Squamous Cervical Cancer Patients

<div><p>Hypoxia is an adverse factor in cervical cancer, and hypoxia-related gene expression could be a powerful biomarker for identifying the aggressive hypoxic tumors. Reverse transcription quantitative PCR (RT-qPCR) is a valuable method for gene expression studies, but suitable reference genes for data normalization that are independent of hypoxia status and clinical parameters of cervical tumors are lacking. In the present work, we aimed to identify reference genes for RT-qPCR studies of hypoxia in squamous cervical cancer. From 422 candidate reference genes selected from the literature, we used Illumina array-based expression profiles to identify 182 genes not affected by hypoxia in cervical cancer, i.e. genes regulated by hypoxia in eight cervical cancer cell lines or correlating with the hypoxia-associated dynamic contrast-enhanced magnetic resonance imaging parameter A_Brix in 42 patients, were excluded. Among the 182 genes, nine candidates (<i>CHCHD1</i>, <i>GNB2L1</i>, <i>IPO8</i>, <i>LASP1</i>, <i>RPL27A</i>, <i>RPS12</i>, <i>SOD1</i>, <i>SRSF9</i>, <i>TMBIM6</i>) that were not associated with tumor volume, stage, lymph node involvement or disease progression in array data of 150 patients, were selected for further testing by RT-qPCR. geNorm and NormFinder analyses of RT-qPCR data of 74 patients identified <i>CHCHD1</i>, <i>SRSF9</i> and <i>TMBIM6</i> as the optimal set of reference genes, with stable expression both overall and across patient subgroups with different hypoxia status (A_Brix) and clinical parameters. The suitability of the three reference genes were validated in studies of the hypoxia-induced genes <i>DDIT3</i>, <i>ERO1A</i>, and <i>STC2</i>. After normalization, the RT-qPCR data of these genes showed a significant correlation with Illumina expression (P<0.001, n = 74) and A_Brix (P<0.05, n = 32), and the <i>STC2</i> data were associated with clinical outcome, in accordance with the Illumina data. Thus, <i>CHCHD1</i>, <i>SRSF9</i> and <i>TMBIM6</i> seem to be suitable reference genes for studying hypoxia-related gene expression in squamous cervical cancer samples by RT-qPCR. Moreover, <i>STC2</i> is a promising prognostic hypoxia biomarker in cervical cancer.</p></div

Pre-evaluation of 9 candidate reference genes by RT-qPCR in 10 patients.

<p>(<b>A</b>) Gel electrophoresis of the PCR products for nine candidate reference genes in one patient. Lower (25 bp) and upper (1500 bp) markers are shown in each lane. Gene symbols are indicated. The figure is a composite image where <i>CHCHD1</i> is from a separate image and the ladder from each image is shown. Vertical lines indicate cropping of the image or different images. (<b>B</b>) Box plots of the arithmetic means of duplicate C_q-values for eight candidate reference genes in 10 patients. Boxes indicate the interquartile range (IQR) with median as the black center bar. Extended vertical bars represents 1.5 x IQR below the first quartile and 1.5 x IQR above the third quartile, and circles mark suspected outliers. (<b>C</b>) geNorm analysis of eight candidate reference genes. Average expression stability (M) of the remaining candidates after stepwise removal of the least stable gene is shown. The least stable gene in each step is indicated below. (<b>D</b>) Stability value of each of the eight candidate reference genes from the NormFinder analysis, where a low value indicates more stable expression.</p

Nine candidate reference genes and three hypoxia-induced genes evaluated in this study.

<p>Nine candidate reference genes and three hypoxia-induced genes evaluated in this study.</p

Evaluation of stability across subgroups for 5 candidate reference genes by RT-qPCR in 74 patients.

<p>NormFinder analyses of the stability of five candidate reference genes across patient subgroups. The subgroups assessed were: low (n = 49) and high (n = 25) tumor stage (FIGO 1B-2B vs. 3A-4A), with (n = 32) and without (n = 42) lymph node (LN) involvement at diagnosis, below (n = 36) and above (n = 36) a median tumor volume of 44.6 cm³, with (n = 32) or without (n = 42) treatment recurrence at five years, and different hypoxia status represented by below (n = 16) and above (n = 16) a median A_Brix.</p

Association between gene expression and tumor hypoxia status (A_Brix).

<p>Association between gene expression and tumor hypoxia status (A_Brix).</p

Patient and tumor characteristics.

<p>Patient and tumor characteristics.</p

<i>CHCHD1</i>, <i>SRSF9</i> and <i>TMBIM6</i> as reference genes in studies of hypoxia-induced gene expression in cervical cancer patients.

<p>(<b>A</b>) Gel electrophoresis of the PCR products for the three hypoxia-induced genes <i>DDIT3</i>, <i>ERO1A</i>, and <i>STC2</i>. Lower (25 bp) and upper (1500 bp) markers are shown in each lane. The figure is derived from one image, and vertical lines indicate cropping of the image. Cumulative incidence of disease progression for 74 patients divided into low (< 67% percentile) and high (≥ 67% percentile) <i>STC2</i> expression based on (<b>B</b>) Illumina expression data and (<b>C</b>) RT-qPCR data normalized with <i>CHCHD1</i>, <i>SRSF9</i> and <i>TMBIM</i>6 (-ΔC_q). 60 months recurrence probability, P-values from Gray’s test and number of patients at risk are indicated. Death from other causes than cervical cancer was included as a competing event (n = 5). (<b>D</b>) Intra-tumor variability in <i>STC2</i> expression levels measured by RT-qPCR across eight independent tumors with 2–4 biopsies per tumor, i.e. in total 22 biopsies. Measurement of <i>STC2</i> was unsuccessful for one of the biopsies for tumor 4. <i>STC2</i> data were normalized with <i>CHCHD1</i>, <i>SRSF9</i> and <i>TMBIM</i>6. The samples were classified into a high and low expression group using the same cut-off as in Fig 5C (i.e. –ΔC_q = -4.46). Different biopsies from the same tumor have been plotted with the same color to ease the interpretation of the figure.</p

Posterior probability densities of transcript concentrations (number of transcripts per µg total RNA) of genes known to be involved in communication (green), growth (orange) and signal transduction (blue) for cancer cell lines () and cervix cancer ()

<p><b>Copyright information:</b></p><p>Taken from "Genome-wide estimation of transcript concentrations from spotted cDNA microarray data"</p><p>Nucleic Acids Research 2005;33(17):e143-e143.</p><p>Published online 4 Oct 2005</p><p>PMCID:PMC1243803.</p><p>© The Author 2005. Published by Oxford University Press. All rights reserved</p> The calculations were based on a pool of 10 cancer cell lines and 12 cervix tumours. The number of genes in each functional group is indicated. Some genes were shared by the groups. The distribution of all 10 157 genes and ESTs is also shown (black). All distributions were skewed to the right and had similar median values

Posterior probability density of the transcript concentration (number of transcripts per µg total RNA) for the oncogene in two different cervix tumours

<p><b>Copyright information:</b></p><p>Taken from "Genome-wide estimation of transcript concentrations from spotted cDNA microarray data"</p><p>Nucleic Acids Research 2005;33(17):e143-e143.</p><p>Published online 4 Oct 2005</p><p>PMCID:PMC1243803.</p><p>© The Author 2005. Published by Oxford University Press. All rights reserved</p> The mode of this density is the estimated concentration as listed at . There was a significant difference in the concentration between the tumours ( < 0.001, Kolmogorov–Smirnov test). The qRT-PCR data (relative to TBP) were 0.24 for MM14 and 0.023 for MM18, in agreement with our estimates

Posterior probability density of transcript concentrations (number of transcripts per µg total RNA) for cancer cell lines (black) and cervix cancer (red)

<p><b>Copyright information:</b></p><p>Taken from "Genome-wide estimation of transcript concentrations from spotted cDNA microarray data"</p><p>Nucleic Acids Research 2005;33(17):e143-e143.</p><p>Published online 4 Oct 2005</p><p>PMCID:PMC1243803.</p><p>© The Author 2005. Published by Oxford University Press. All rights reserved</p> The data of 10 157 genes and ESTs were included, and the calculations were based on a pool of 10 cell lines and 12 cervix tumours. The median value of each distribution is shown as a vertical line and was slightly higher for the cell lines than for cervix cancer. Both distributions were skewed towards higher values, and less abundant transcripts were more frequent than high abundant ones