ABSTRACT
PURPOSE: Aldo-keto reductases (including AKR1B1 and AKR1B10) constitute a family of oxidoreductases that have been implicated in the pathophysiology of diabetes and cancer, including colorectal cancer (CRC). Available data indicate that, despite their similarities in structure and enzymatic functions, their roles in CRC may be divergent. Here, we aimed to determine the expression and functional implications of AKR1B1 and AKR1B10 in CRC. METHODS: AKR1B1 and AKR1B10 gene expression levels were analyzed using publicly available microarray data and ex vivo CRC-derived cDNA samples. Gene Set Enrichment Analysis (GSEA), The Cancer Genome Atlas (TCGA) RNA-seq data and The Cancer Proteome Atlas (TCPA) proteome data were analyzed to determine the effect of high and low AKR1B1 and AKR1B10 expression levels in CRC patients. Proliferation, cell cycle progression, cellular motility, adhesion and inflammation were determined in CRC-derived cell lines in which these genes were either exogenously overexpressed or silenced. RESULTS: We found that the expression of AKR1B1 was unaltered, whereas that of AKR1B10 was decreased in primary CRCs. GSEA revealed that, while high AKR1B1 expression was associated with increased cell cycle progression, cellular motility and inflammation, high AKR1B10 expression was associated with a weak inflammatory phenotype. Functional studies carried out in CRC-derived cell lines confirmed these data. Microarray data analysis indicated that high expression levels of AKR1B1 and AKR1B10 were significantly associated with shorter and longer disease-free survival rates, respectively. A combined gene expression signature of AKR1B10 (low) and AKR1B1 (high) showed a better prognostic stratification of CRC patients independent of confounding factors. CONCLUSIONS: Despite their similarities, the expression levels and functions of AKR1B1 and AKR1B10 are highly divergent in CRC, and they may have prognostic implications.
Subject(s)
Aldehyde Reductase/metabolism , Colorectal Neoplasms/metabolism , Aldehyde Reductase/genetics , Aldo-Keto Reductases , Cell Cycle/genetics , Cell Cycle/physiology , Cell Proliferation/genetics , Cell Proliferation/physiology , Colorectal Neoplasms/genetics , Gene Expression Regulation, Neoplastic/genetics , Gene Expression Regulation, Neoplastic/physiology , HCT116 Cells , Humans , Prognosis , Reactive Oxygen Species/metabolism , Wound Healing/genetics , Wound Healing/physiologyABSTRACT
Background: Prognostic biomarkers for cancer have the power to change the course of disease if they add value beyond known prognostic factors, if they can help shape treatment protocols, and if they are reliable. The aim of this study was to identify such biomarkers for colon cancer and to understand the molecular mechanisms leading to prognostic stratifications based on these biomarkers. Methods and Findings: We used an in house R based script (SSAT) for the in silico discovery of stage-independent prognostic biomarkers using two cohorts, GSE17536 and GSE17537, that include 177 and 55 colon cancer patients, respectively. This identified 2 genes, ULBP2 and SEMA5A, which when used jointly, could distinguish patients with distinct prognosis. We validated our findings using a third cohort of 48 patients ex vivo. We find that in all cohorts, a combined ULBP2/SEMA5A classification (SU-GIB) can stratify distinct prognostic sub-groups with hazard ratios that range from 2.4 to 4.5 (p≤0.01) when overall- or cancer-specific survival is used as an end-measure, independent of confounding prognostic parameters. In addition, our preliminary analyses suggest SU-GIB is comparable to Oncotype DX colon(®) in predicting recurrence in two different cohorts (HR: 1.5-2; p≤0.02). SU-GIB has potential as a companion diagnostic for several drugs including the PI3K/mTOR inhibitor BEZ235, which are suitable for the treatment of patients within the bad prognosis group. We show that tumors from patients with worse prognosis have low EGFR autophosphorylation rates, but high caspase 7 activity, and show upregulation of pro-inflammatory cytokines that relate to a relatively mesenchymal phenotype. Conclusions: We describe two novel genes that can be used to prognosticate colon cancer and suggest approaches by which such tumors can be treated. We also describe molecular characteristics of tumors stratified by the SU-GIB signature.