Quantitative analysis of gene expression in fixed colorectal carcinoma samples as a method for biomarker validation

Biomarkers have been described as the future of oncology. Modern proteomics provide an invaluable tool for the near-whole proteome screening for proteins expressed differently in neoplastic vs. healthy tissues. However, in order to select the most promising biomarkers, an independent method of validation is required. The aim of the current study was to propose a methodology for the validation of biomarkers. Due to material availability the majority of large scale biomarker studies are performed using formalin-fixed paraffin-embedded (FFPE) tissues, therefore these were selected for use in the current study. A total of 10 genes were selected from what have been previously described as the most promising candidate biomarkers, and the expression levels were analyzed with reverse transcription-quantitative polymerase chain reaction (RT-qPCR) using calibrator normalized relative quantification with the efficiency correction. For 6/10 analyzed genes, the results were consistent with the proteomic data; for the remaining four genes, the results were inconclusive. The upregulation of karyopherin α 2 (KPNA2) and chromosome segregation 1-like (CSE1L) in colorectal carcinoma, in addition to downregulation of chloride channel accessory 1 (CLCA1), fatty acid binding protein 1 (FABP1), sodium channel, voltage gated, type VII α subunit (SCN7A) and solute carrier family 26 (anion exchanger), member 3 (SLC26A3) was confirmed. With the combined use of proteomic and genetic tools, it was reported, for the first time to the best of our knowledge, that SCN7A was downregulated in colorectal carcinoma at mRNA and protein levels. It had been previously suggested that the remaining five genes served an important role in colorectal carcinogenesis, however the current study provided strong evidence to support their use as biomarkers. Thus, it was concluded that combination of RT-qPCR with proteomics offers a powerful methodology for biomarker identification, which can be used to analyze FFPE samples.