Protein expression trends of DNMT1 in gastrointestinal diseases: From benign to precancerous lesions to cancer

Abstract

BACKGROUNDIn recent years, the incidence of gastrointestinal (GI) cancer in China has increased annually. Early detection and appropriate therapy are considered to be the key to treat GI cancer. DNMT1 takes an active part in the advancement of GI cancer, which will change as the disease progresses. But its expression characteristics in the dynamic variations of GI carcinogenesis are still unclear.AIMTo investigate the expression characteristics of DNMT1 in different GI diseases.METHODSWe detected the expression of DNMT1 in 650 cases of different GI diseases by immunohistochemistry, including 90 cases of chronic superficial gastritis (CSG), 72 cases of atrophic gastritis with intestinal metaplasia (AG/GIM), 54 cases of low-grade intraepithelial neoplasia (GLIN), 66 cases of high-grade intraepithelial neoplasia (GHIN), 71 cases of early gastric cancer (EGC), 90 cases of normal intestinal mucosa (NIM), 54 cases of intestinal low-grade intraepithelial neoplasia (ILIN), 71 cases of intestinal high-grade intraepithelial neoplasia (IHIN), and 82 cases of early colorectal cancer (ECRC).RESULTSIn the CSG group, all cases showed weakly positive or negative expression of DNMT1. However, in other four groups (AG/GIM, GLIN, GHIN, and EGC), the positive expression rate gradually increased with the severity of the diseases; the negative or weakly positive cases accounted for 55.56% (40/72), 38.89% (21/54), 1.52% (1/66), and 1.41% (1/71), respectively. Besides, the moderately positive cases were 44.44% (32/72), 57.41% (31/54), 80.30% (53/66), and 43.66% (31/71), respectively. The strongly positive cases only existed in the GLIN (3.70%, 2/54), GHIN (18.18%, 12/66), and EGC (54.93%, 39/71) groups. The differences between any two groups were statistically significant (P < 0.05). Similarly, in the NIM group, cases with weakly positive expression of DNMT1 were predominant (91.11%, 82/90), and the rest were moderately positive cases (8.89%, 8/90). In the ILIN, IHIN, and ECRC groups, the rates of cases with weak or negative expression of DNMT1 were 46.30% (25/54), 12.68% (9/71), and 4.88% (4/82), respectively; with moderately positive expression were 53.70% (29/54), 71.83% (51/71), and 34.15% (28/82), respectively; and with strongly positive expression were 0.00% (0/54), 15.49% (11/71), and 60.98% (50/82), respectively. The differences between any two groups were also statistically significant (P < 0.05).CONCLUSIONThe overexpression of DNMT1 protein could effectively predict early GI cancers and severe precancerous lesions, which may have potential clinical application value.

DNMT1 expression partially dictates 5-Azacytidine sensitivity and correlates with RAS/MEK/ERK activity in gastric cancer cells

“…Then, both immunohistochemistry and bioinformatics analysis showed that DNMT1 expression is increased in GC tumour tissues compared with adjacent normal tissues, which is consistent with previous findings that DNMT1 is expressed at higher levels in early gastric cancer or high-grade intraepithelial neoplasia tissues compared to that in non-cancer mucosa or low-grade intraepithelial neoplasia tissues [ 37 ]. Mechanistically, H. pylori-induced chronic inflammation, but not H. Pylori itself, played a direct role in the induction of DNMT1 expression and aberrant DNA methylation in GC [ 38 ].…”

<p>Nomogram for Predicting Risk of Digestive Carcinoma Among Patients with Type 2 Diabetes</p>

“…[10][11][12][13] Epidemiological data suggest that diabetes increases the relative risk of digestive cancers more than twofold, 1 and type 2 diabetes mellitus is associated with more aggressive digestive tract cancers. 14 Universal screening using potential biomarkers such as cancer antigen 19-9, carcinoembryonic antigens, or clinical tests (eg, computed tomography, endoscopic ultrasound, or magnetic resonance imaging) is not feasible, [15][16][17] as the existing biomarkers have limited diagnostic accuracy. Therefore, there is an urgent clinical need for novel prediction models and screening methods that are both efficient and cost-effective for the detection of asymptomatic cases.…”