The Dysregulation of Polyamine Metabolism in Colorectal Cancer Is Associated with Overexpression of c-Myc and C/EBPβ rather than Enterotoxigenic Bacteroides fragilis Infection.
Anastasiya V SnezhkinaGeorge S KrasnovAnastasiya V LipatovaAsiya F SadritdinovaOlga L KardymonMaria S FedorovaNataliya V MelnikovaOleg A StepanovAndrew R ZaretskyAndrey D KaprinBoris Y AlekseevAlexey A DmitrievAnna V KudryavtsevaPublished in: Oxidative medicine and cellular longevity (2016)
Colorectal cancer is one of the most common cancers in the world. It is well known that the chronic inflammation can promote the progression of colorectal cancer (CRC). Recently, a number of studies revealed a potential association between colorectal inflammation, cancer progression, and infection caused by enterotoxigenic Bacteroides fragilis (ETBF). Bacterial enterotoxin activates spermine oxidase (SMO), which produces spermidine and H2O2 as byproducts of polyamine catabolism, which, in turn, enhances inflammation and tissue injury. Using qPCR analysis, we estimated the expression of SMOX gene and ETBF colonization in CRC patients. We found no statistically significant associations between them. Then we selected genes involved in polyamine metabolism, metabolic reprogramming, and inflammation regulation and estimated their expression in CRC. We observed overexpression of SMOX, ODC1, SRM, SMS, MTAP, c-Myc, C/EBPβ (CREBP), and other genes. We found that two mediators of metabolic reprogramming, inflammation, and cell proliferation c-Myc and C/EBPβ may serve as regulators of polyamine metabolism genes (SMOX, AZIN1, MTAP, SRM, ODC1, AMD1, and AGMAT) as they are overexpressed in tumors, have binding site according to ENCODE ChIP-Seq data, and demonstrate strong coexpression with their targets. Thus, increased polyamine metabolism in CRC could be driven by c-Myc and C/EBPβ rather than ETBF infection.
Keyphrases
- oxidative stress
- cell proliferation
- genome wide
- poor prognosis
- end stage renal disease
- transcription factor
- ejection fraction
- newly diagnosed
- chronic kidney disease
- single cell
- high throughput
- machine learning
- squamous cell carcinoma
- young adults
- cell cycle
- gene expression
- rna seq
- long non coding rna
- pi k akt
- signaling pathway
- patient reported outcomes
- circulating tumor cells
- network analysis
- human health
- lymph node metastasis