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Measuring the combinatorial expression of solute transporters and metalloproteinases transcripts in colorectal cancer
© Kerr et al; licensee BioMed Central Ltd. 2009
- Received: 13 May 2009
- Accepted: 19 August 2009
- Published: 19 August 2009
It was hypothesised that colorectal cancer (CRC) could be diagnosed in biopsies by measuring the combined expression of a small set of well known genes. Genes were chosen based on their role in either the breakdown of the extracellular matrix or with changes in cellular metabolism both of which are associated with CRC progression
Gene expression data derived from quantitative real-time PCR for the solute transporter carriers (SLCs) and the invasion-mediating matrix metalloproteinases (MMPs) were examined using a Linear Descriminant Analysis (LDA). The combination of MMP-7 and SLC5A8 was found to be the most predictive of CRC.
A combinatorial analysis technique is an effective method for both furthering our understanding on the molecular basis of some aspects of CRC, as well as for leveraging well defined cancer-related gene sets to identify cancer. In this instance, the combination of MMP-7 and SLC5A8 were optimal for identifying CRC.
- Linear Discriminant Analysis
- Human Colorectal Cancer Cell Line
- Candidate Transcript
- Transporter SLC2A1
- Transcript Differential Expression
Colorectal cancer is the third-most common cancer in males and second-most common in females worldwide . Its prevalence highlights a need to more deeply understand the molecular interactions that lead to its progression. Two important and well documented pathways in the progression of colorectal cancer are changes in energy source for cellular metabolism and break down of the extracellular matrix.
Healthy colonocytes use short-chain monocarboxylates, in particular butyrate, as their main source of energy . The solute-linked carrier (SLC) SLC5A8, a Na+-coupled transporter, and monocarboxylate transporter (MCT1) SLC16A, are possibly vehicles by which short-chain monocarboxylates are transported into the colonic epithelium [3–5]. SLC5A8 and SLC16A1 have been purported to provide a mechanism for the suppression of tumour growth in colorectal and gastric cancers [3, 6] and are down-regulated with tumour progression . As colonocytes become cancerous there is a shift in energy source away from butyrate to glucose, resulting in increased levels of glucose in colorectal cancer cells  and in carcinomas . Associated with this is an up-regulation of the glucose transporter SLC2A1, which has been shown in a significant proportion of aggressive human tumours [e.g. ]. Together, these changes are believed to facilitate tumour growth and proliferation .
Matrix metalloproteinases (MMPs) are a family of zinc- and calcium-dependent proteolytic enzymes that degrade macromolecules of the extracellular matrix. Members of this family, such as MMP-2, -9 and -7, have been shown to be associated with the breakdown of type IV collagen and the basement membrane. They have been implicated in tumour progression and invasion in human cancer tissues [11–13]. The proteolytic activity of some MMPs (e.g. MMP-2, -9 and -14) can be suppressed by Reversion-inducing cysteine-rich protein with kazal motifs (RECK) . Decreased expression of RECK is believed to result in increased invasion, metastasis and angiogenesis [reviewed by ] and is associated with poor prognosis in cancer patients .
This paper investigates genes in combination from two previous well defined processes in colorectal cancer. The abundance of transcripts from well described candidate genes implicated in either the tumorigenic process or metabolic changes associated with carcinogenesis were examined in human colorectal cancer cell lines and human cancer and healthy colonic tissues. In particular, the expression of the nutrient transporter genes (SLC2A1, SLC16A1 and SLC5A8), genes encoding proteins involved in tissue remodelling and tumour invasion (MMP-2, -7, -9 and -12, and the MMP regulator RECK), were examined in two sets of normal human colon and colorectal tumour samples and in four human colorectal cancer cell lines. The study used a combinatorial transcript expression bioinformatic approach to leverage described information on a small gene set in order to discriminate between normal and colorectal tumour tissue and help to define interrelationships between processes known to change during carcinogenesis.
Summary of tissue sample details^
Total RNA extraction, cDNA synthesis and real-time PCR
The human tissue samples were obtained from resections of specimens and placed in OCT (optimal cutting temperature cryopreservation medium) , snap-frozen in liquid nitrogen and then stored at -86°C. After histological verification RNA was extracted by placing samples in 1 ml of Trizol® Reagent (Invitrogen, Sydney, Australia), then homogenised using beads (mix of 2.5 mm glass and 0.1 – 1.0 mm diameter silicon-zirconium beads) in a MiniBeadbeater-8™ (BioSpec Products Inc., Oklahoma, USA) and extracted according to Invitrogen's instructions. Samples were then further processed using RNAeasy mini spin columns (QIAGEN, Doncaster, Australia) with contaminating DNA being removed via DNase on-column digestion as per the manufacturer's instructions. Similarly, cultured cells that were at least 70% confluent were extracted directly using the RNAeasy spin columns. The integrity of RNA samples from Study 2 and the cell lines were checked using a Bioanalyzer 2100 (Agilent Technologies) . All of the RNA samples were then quantified using a NanoDrop® ND-1000 Spectrophotometer. Samples were then diluted to100 ng/ul.
Gene and assay details.
Gene Name and Symbol
Genbank accession numbers
TaqMan Primer/Probe ID
Eukaryotic 18S rRNA.
HECT, UBA and WWE domain containing 1 (HUWE1).
Ribosomal protein, large, P0 (60s).
Matrix metalloproteinase 12 (MMP12).
Matrix metalloproteinase 2 (MMP2) (gelatinase A, 72 kDa gelatinase, 72 kDa type IV collagenase).
Matrix metalloproteinase 7 (MMP7).
Metalloproteinase 9 (MMP9)(gelatinase B, 92 kDa gelatinase, 92 kDa type IV collagenase).
Reversion-inducing-cysteine-rich protein with kazal motifs (RECK).
Solute carrier family 2 (facilitated glucose transporter) member 1: SLC2A1, alias Glut1.
Solute carrier family 5 (iodide transporter), member 8.
Solute carrier family 16, member 1 (monocarboxylic acid transporter 1).
This process allowed for the data to be analysed for artefacts, real-time PCR repeatability and stability of HK expression. As three HK genes were used, the process was then repeated with each housekeeping gene and the median calculated; the ratio of the gene expression is 2-ΔΔCt.
The normalised ΔΔCt data sets were then combined and all subset variable selection with Linear Discriminant Analysis (LDA) was performed to ascertain the best combination of transcripts that separated tumour from normal. The error rate for the model was estimated using 'leave-one-out estimates' for cross validation .
Transcript expression from cultured colorectal cancer cell lines (HT29, HCT116, Caco2 and LIM1215) was then used to further test the optimal combinations using LDA. The effect of tissue sampling site (i.e. left, transverse or right colon), the type of 'normal' and Dukes stage was also analysed.
This communication investigated expression patterns of transcripts associated with processes involved in the development of colorectal cancer. Genes examined were the solute transporters SLC2A1, SLC5A8 and SLC16A1, which are associated with changes in the cellular import of energy sources, and MMP-2, MMP-7, MMP-9 and MMP-12, which are related to the breakdown of the extracellular matrix, and the MMP negative regulator, RECK. Individual differential gene expression patterns were established for normal and cancerous tissue samples. When the data were combined, a combination of MMP-7 and SLC5A8 (and, to a lesser extent, RECK) provided the greatest separation between healthy colon tissue and colorectal cancer (tissue or cell lines). One possible interpretation of these results is that the mechanisms which act to break down the extracellular matrix and promote tumour invasion also induce MMP negative regulation. Whilst in parallel, SLC5A8 levels in tumours were reduced compared to normal tissue and cell lines, which is consistent with previous studies  showing an association between SLC5A8 down-regulation and tumour progression.
This study has demonstrated that it is advantageous to use a combinatorial approach to defining biomarkers of carcinogenesis processes compared to using individual candidate transcript markers. Others have used systematic approaches when analysing transcripts for cancer biomarkers (e.g. pancreatic cancer by ) and have shown that markers, which individually are suboptimal, can be combined to yield higher sensitivity and specificity. Even though our study uses a small patient tissue library, it demonstrates a proof-of-concept for the combinatorial approach to transcript biomarkers that now needs to be validated in larger controlled data sets [26, 27]. In addition, our technique may prove useful to validate other colorectal cancer candidate transcripts, such as those defined in a recent study  which applied a meta-analysis or genome wide studies (e.g. microarrays) to comprehensively evaluate microarray data for biomarkers. Although using tumour-related gene expression may not be an optimal platform for colorectal cancer detection, this combinatorial approach demonstrates a method for biomarker discovery based on a priori hypotheses originating from other studies that may prove useful either in elucidating early biomarkers or in establishing auxiliary markers of prognosis. This approach could be applied in the clinical setting to increase the sensitivity and specificity of biomarkers by combining the analyses with other markers .
This research was funded by CSIRO's Preventative Health Flagship program. We would like to thank Siok Hwee Tan for her laboratory assistance and Peter Molloy, Lloyd Graham and Andre-Denis Wright for their manuscript critique.
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