Salmonella infection is a major public health concern, and colonization in humans can be chronic and increases the risk of cancers. Wnt signaling is a key pathway for intestinal renewal and development, inflammation, and tumorigenesis. In the current study, we report a novel role of Wnt1 in infection and colon cancer using cell culture models, a Salmonella-colitis colon cancer model, and human samples. In contrast to the bacteria-induced increases in Wnt2 and Wnt11, Salmonella colonization significantly reduced the level of Wnt1 in intestinal epithelial cells in vivo and in vitro. The bacterial AvrA protein is known to activate the canonical Wnt pathway. Wnt1 expression level was downregulated by AvrA-expressing Salmonella but stabilized by AvrA-deficient Salmonella in the intestine of Salmonella-colitis mice. In a chronic Salmonella-infected cancer model, the Wnt1 protein level was decreased in the AvrA+ infected group. Thus, we further assessed the functional role of Wnt1 downregulation in the inflammatory response and colorectal cancer (CRC) progression. Moreover, downregulation of Wnt1 by the Crispr-Cas9 method promoted cancer cell invasion and migration. Interestingly, we found that Wnt1 was downregulated in human CRC tissue, and Wnt1 downregulation may be correlated with cancer progression. Our study provides insights into mechanisms by which enteric bacteria regulate Wnt1 expression and potentially contribute to infection-associated colon cancer.

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BACKGROUND Artificial intelligence in colonoscopy is an emerging field, and its application may help colonoscopists improve inspection quality and reduce the rate of missed polyps and adenomas. Several deep learning-based computer-assisted detection (CADe) techniques were established from small single-center datasets, and unrepresentative learning materials might confine their application and generalization in wide practice. Although CADes have been reported to identify polyps in colonoscopic images and videos in real time, their diagnostic performance deserves to be further validated in clinical practice. AIM To train and test a CADe based on multicenter high-quality images of polyps and preliminarily validate it in clinical colonoscopies. METHODS With high-quality screening and labeling from 55 qualified colonoscopists, a dataset consisting of over 71000 images from 20 centers was used to train and test a deep learning-based CADe. In addition, the real-time diagnostic performance of CADe was tested frame by frame in 47 unaltered full-ranged videos that contained 86 histologically confirmed polyps. Finally, we conducted a self-controlled observational study to validate the diagnostic performance of CADe in real-world colonoscopy with the main outcome measure of polyps per colonoscopy in Changhai Hospital. RESULTS The CADe was able to identify polyps in the test dataset with 95.0% sensitivity and 99.1% specificity. For colonoscopy videos, all 86 polyps were detected with 92.2% sensitivity and 93.6% specificity in frame-by-frame analysis. In the prospective validation, the sensitivity of CAD in identifying polyps was 98.4% (185/188). Folds, reflections of light and fecal fluid were the main causes of false positives in both the test dataset and clinical colonoscopies. Colonoscopists can detect more polyps (0.90 vs 0.82, P < 0.001) and adenomas (0.32 vs 0.30, P = 0.045) with the aid of CADe, particularly polyps < 5 mm and flat polyps (0.65 vs 0.57, P < 0.001; 0.74 vs 0.67, P = 0.001, respectively). However, high efficacy is not realized in colonoscopies with inadequate bowel preparation and withdrawal time ( P = 0.32; P = 0.16, respectively). CONCLUSION CADe is feasible in the clinical setting and might help endoscopists detect more polyps and adenomas, and further confirmation is warranted.