Back

Anterior Gradient-2 (AGR2) overexpression in colon cancer: a potential prognostic biomarker

Fessart, D.; Mahouche, I.; Brouste, V.; Velasco, V.; Soubeyran, I.; Soubeyran, P.; Pernot, S.; Chevet, E.; Evrard, S.; Robert, J.; Delom, F.

2021-09-08 cancer biology
10.1101/2021.09.07.459258 bioRxiv
Show abstract

BackgroundColon cancer is one of the most common leading causes of death worldwide. Prognostic at an early stage is an efficient way to decrease mortality. The Endoplasmic Reticulum (ER)-resident protein anterior gradient-2 (AGR2), a Protein Disulfide Isomerase (PDI) is highly expressed in various tumours and is involved in tumour-associated processes. This study aims at examining the expression of AGR2 protein in colon cancer. MethodsAGR2 protein expression was determined using immunohistochemistry on tissue samples issued from a cohort of 82 colorectal carcinomas. ResultsAGR2 protein expression was significantly higher in tumours than in adjacent nontumour controls. AGR2 expression subgroup analyses indicated that AGR2 low expression in colon cancer patients was significantly associated with worse overall survival. Mucinous colon cancers exhibited higher AGR2 expression levels than non-mucinous cancers. Additionally, tumours with microsatellite instability (MSI) were characterised by a strong upregulation of AGR2 mRNA and protein expression despite an absence of MLH1/MSH2 mutations. ConclusionsOur findings indicate that high AGR2 protein expression is correlated with longer patient survival and that AGR2 overexpression is associated with MSI tumours and could represent an MSI biomarker. Overall, AGR2 might serve as a biomarker to stratify colon tumours and to contribute to the prognosis of colon cancer patients.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.