Sama Rezasoltani, Mahrooyeh Hadizadeh, Mina Golmohammadi, Ehsan Nazemalhossini-Mojarad, Sina Salari, Hamid Rezvani, Hamid Asadzadeh-Aghdaei, Michael Ladomery, Chris Young, Fakhrosadat Anaraki, Sarah Almond, Maziar Ashrafian Bonab
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引用次数: 1
Abstract
Aberrant activation of the WNT/CTNNB1 pathway is notorious in colorectal cancer (CRC). Here, we demonstrate that the expression of specific and crucial WNT signaling pathway genes is linked to disease progression in colonic adenomatous (AP) and hyperplastic (HP) polyps in an Iranian patient population. Thus, we highlight potential gene expression profiles as candidate novel biomarkers for the early detection of CRC. From a 12-month study (2016-2017), 44 biopsy samples were collected during colonoscopy from the patients with colorectal polyps and 10 healthy subjects for normalization. Clinical and demographic data were collected in all cases, and mRNA expression of APC, CTNNB1, CDH1, AXIN1, and AXIN2 genes was investigated using real-time polymerase chain reaction (PCR). CTNNB1 and CDH1 expression levels were unaltered in AP and HP subjects, whereas mRNA expression of APC was decreased in AP contrasted with HP subjects, with a significant association between APC downregulation and polyp size. Although AXIN1 showed no changes between AP and HP groups, a significant association between AXIN1 and dysplasia grade was found. Also, significant upregulation of AXIN2 in both AP and HP subjects was detected. In summary, we have shown increased expression of AXIN2 and decreased expression of APC correlating with grade of dysplasia and polyp size. Hence, AXIN2 and APC should be explored as biomarker candidates for early detection of AP and HP polyps in CRC.
期刊介绍:
The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.