BackgroundThe recent uptrend in colorectal cancer (CRC) incidence in China is causing an increasingly overwhelming social burden. And its occurrence can be effectively reduced by sensitizing CRC screening for early diagnosis and treatment. However, a large number of people in China do not undergo screening due to multiple factors. To address this issue, since 2012, a CRC screening program has been initiated in Tianjin.MethodsResidents aged 40-74 years were eligible for CRC screening. The first was to complete the high-risk factor questionnaire (HRFQ) and undergo fecal immunochemical test (FIT). Then those with a positive result in any of the two screening methods were recommended for a free colonoscopy.ResultsThe detection rate of intestinal diseases increased with age, had a male predominance, and was higher in residents from central urban areas and those with primary school above education level. The sensitivity of predicting CRC after colonoscopy in the high-risk group was 76.02%; the specificity was 25.33%.A significant decrease in the detection rate of intestinal disease, CRC and advanced adenoma was observed from positive FIT, the high-risk group and positive HRFQ, 47.13%, 44.79%, 42.30%; 3.15%, 2.44%, 1.76%; 7.72%, 6.42%, 5.08%, in that order, while no inter-group difference was found for the detection of polyps. In addition, the different combinations of HRFQ and FIT can enroll more high-risk population than FIT or (and) HRFQ only, and thus detect more intestinal diseases (include CRC/AA/Polyp).ConclusionThe superimposition of different screening method for HRFQ and FIT is an effective strategy for the detection of CRC, AA, and Polyp, compared to HRFQ or FIT alone. However, further improvements in screening and interventions are needed to promote colonoscopy compliance.
Myelosuppression is a major adverse effect of 5-fluorouracil (5-FU) chemotherapy. However, recent findings indicate that 5-FU selectively suppresses myeloid-derived suppressor cells (MDSCs), to enhance antitumor immunity in tumor-bearing mice. 5-FU-mediated myelosuppression may thus have a beneficial effect for cancer patients. The molecular mechanism underlying 5-FU’s suppression of MDSCs is currently unknown. We aimed at testing the hypothesis that 5-FU suppresses MDSCs through enhancing MDSC sensitivity to Fas-mediated apoptosis. We observed that, although FasL is highly expressed in T cells, Fas is weakly expressed in myeloid cells in human colon carcinoma, indicating that downregulation of Fas is a mechanism underlying myeloid cell survival and accumulation in human colon cancer. 5-FU treatment upregulated expression of both p53 and Fas, and knocking down p53 diminished 5-FU-induced Fas expression in MDSC-like cells, in vitro. 5-FU treatment also increased MDSC-like cell sensitivity to FasL-induced apoptosis in vitro. Furthermore, we determined that 5-FU therapy increased expression of Fas on MDSCs, suppressed MDSC accumulation, and increased CTL tumor infiltration in colon tumor-bearing mice. In human colorectal cancer patients, 5-FU chemotherapy decreased MDSC accumulation and increased CTL level. Our findings determine that 5-FU chemotherapy activates the p53-Fas pathway, to suppress MDSC accumulation, to increase CTL tumor infiltration.
BackgroundThis study aimed to develop an artificial intelligence predictive model for predicting the probability of developing BM in CRC patients.MethodsFrom SEER database, 50,566 CRC patients were identified between January 2015 and December 2019 without missing data. SVM and LR models were trained and tested on the dataset. Accuracy, area under the curve (AUC), and IDI were used to evaluate and compare the models.ResultsFor bone metastases in the entire cohort, SVM model with poly as kernel function presents the best performance, whose accuracy is 0.908, recall is 0.838, and AUC is 0.926, outperforming LR model. The top three most important factors affecting the model's prediction of BM include extraosseous metastases (EM), CEA, and size.ConclusionOur study developed an SVM model with poly as kernel function for predicting BM in CRC patients. SVM model could improve personalized clinical decision-making, help rationalize the bone metastasis screening process, and reduce the burden on healthcare systems and patients.
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