AA-NAT, MT1 and MT2 Correlates with Cancer Stem-Like Cell Markers in Colorectal Cancer: Study of the Influence of Stage and p53 Status of Tumors

In this article, the incidence, mortality, and survival rates for colorectal cancer are reviewed, with attention paid to regional variations and changes over time. A concise overview of known risk factors associated with colorectal cancer is provided, including familial and hereditary factors, as well as environmental lifestyle-related risk factors such as physical inactivity, obesity, smoking, and alcohol consumption.

Free radicals are ubiquitous in our body and are generated by normal physiological processes, including aerobic metabolism and inflammatory responses, to eliminate invading pathogenic microorganisms. Because free radicals can also inflict cellular damage, several defences have evolved both to protect our cells from radicals--such as antioxidant scavengers and enzymes--and to repair DNA damage. Understanding the association between chronic inflammation and cancer provides insights into the molecular mechanisms involved. In particular, we highlight the interaction between nitric oxide and p53 as a crucial pathway in inflammatory-mediated carcinogenesis.

Cancer is often viewed as a caricature of normal developmental processes, but the extent by which its cellular heterogeneity truly recapitulates multi-lineage differentiation processes of normal tissues remains unknown. Here, we implement “single-cell PCR gene-expression analysis” (SINCE-PCR) to dissect the cellular composition of primary human normal colon and colon cancer epithelia. We show that human colon cancer tissues contain distinct cell populations whose transcriptional identities mirror those of the different cellular lineages of normal colon. By creating monoclonal tumor xenografts from injection of a single-cell (n = 1), we show that transcriptional diversity of cancer tissues is largely explained by in vivo multi-lineage differentiation, not only by clonal genetic heterogeneity. Finally, we show that perturbations in gene-expression programs linked to multi-lineage differentiation strongly associate with patient survival. Guided by SINCE-PCR data, we develop two-gene classifier systems (KRT20 vs CA1, MS4A12, CD177, SLC26A3) that predict clinical outcomes with hazard-ratios superior to pathological grade and comparable to microarray-derived multi-gene expression signatures.