Abstract
Introduction Field cancerisation refers to the process whereby cells acquire pro-tumourigenic mutations that predispose to malignant transformation but do not produce morphological change. 1 Previous colorectal cancer studies have assumed that the macroscopically normal mucosa (MNM) adjacent to a cancer is biologically unaltered. The aim of this study was to determine if the genetic expression profile of the MNM around a cancer or adenoma is different to that found in healthy controls. Method 15 patients undergoing colonoscopy were recruited over 12 months; 5 healthy controls, 5 with colorectal adenomas and 5 with adenocarcinoma. Two mucosal pinch biopsies were taken in the rectum, right colon and adjacent to polyp or cancer. mRNA was extracted and gene expression was assessed using standard whole genome micro-array analysis. Differentially expressed genes were identified using three methods of analysis: LIMMA (fold change ratio >1.5 and p value 2 software to identify important biological processes that were dysregulated. Results A large number of genes were dysregulated in the MNM adjacent to cancer or adenoma compared with controls (Table 1). Interestingly, the greatest differences were seen between MNM adjacent to cancer and polyp in chromatin organisation, nucleosome processing, nuclear transport and histone assembly. The most significantly upregulated genes consisted of FUT2, CTSA, MUC2 and SDS and downregulated genes consisted of GREM1, SFRP, HIST1H, IL17B and TFF1. Conclusion The MNM adjacent to a cancer or adenoma has a different gene expression profile to the MNM found in healthy controls. Further characterisation of this field defect could aid in early diagnosis of patients with colorectal cancer and help risk stratify patients undergoing surgery who are at greater risk of recurrence. Disclosure of interest None Declared. References Luo Y, Yu M, Grady WM. Field cancerization in the colon: a role for aberrant DNA methylation? Gastroenterol Rep (Oxf). 2014;2(1):16–20 Huang DW, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protocols. 2009;4(1):44–57
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