Investigating the shared genetic architecture between primary sclerosing cholangitis and inflammatory bowel diseases: a Mendelian randomization study.

BACKGROUND: Several studies have found that primary sclerosing cholangitis (PSC) and inflammatory bowel disease (IBD) are closely associated. However, the direction and causality of their interactions remain unclear. Thus, this study employs Mendelian Randomization to explore whether there are causal associations of genetically predicted PSC with IBD. METHODS: Genetic variants associated with the genome-wide association study (GWAS) of PSC were used as instrumental variables. The statistics for IBD, including ulcerative colitis (UC), and Crohn's disease (CD) were derived from GWAS. Then, five methods were used to estimate the effects of genetically predicted PSC on IBD, including MR Egger, Weighted median (WM), Inverse variance weighted (IVW), Simple mode, and Weighted mode. Last, we also evaluated the pleiotropic effects, heterogeneity, and a leave-one-out sensitivity analysis that drives causal associations to confirm the validity of the analysis. RESULTS: Genetically predicted PSC was significantly associated with an increased risk of UC, according to the study (odds ratio [OR] IVW= 1.0014, P<0.05). However, none of the MR methods found significant causal evidence of genetically predicted PSC in CD (All P>0.05). The sensitivity analysis results showed that the causal effect estimations of genetically predicted PSC on IBD were robust, and there was no horizontal pleiotropy or statistical heterogeneity. CONCLUSIONS: Our study corroborated a causal association between genetically predicted PSC and UC but did not between genetically predicted PSC and CD. Then, we identification of shared SNPs for PSC and UC, including rs3184504, rs9858213, rs725613, rs10909839, and rs4147359. More animal experiments and clinical observational studies are required to further clarify the underlying mechanisms of PSC and IBD.

Global burden of common cancers attributable to metabolic risks from 1990 to 2019.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is usually accompanied by metabolic syndrome, which is associated with increased risk of cancer. To inform a tailored cancer screen in patients at higher risks, we estimated the global burden of cancer attributable to metabolic risks. METHODS: Data of common metabolism-related neoplasms (MRNs) were derived from the Global Burden of Disease (GBD) 2019 database. Age-standardized, disability-adjusted life year (DALY) rates and death rates of patients with MRNs were extracted from the GBD 2019 database and stratified by metabolic risk, sex, age, and level of socio-demographic index (SDI). The annual percentage changes of age-standardized DALYs and death rates were calculated. FINDINGS: Metabolic risks, consisting of high body mass index and fasting plasma glucose, contributed substantially to the burden of neoplasms, including colorectal cancer (CRC), tracheal, bronchus, and lung cancer (TBLC), etc. Globally, in 2019, there was an estimated age-standardized DALY rate (ASDR) of 234 (95% confidence interval [CI] 124-376) per 100,000 person years for neoplasms attributable to metabolic risks. ASDRs of MRNs were higher for CRC, TBLC, men, patients aged ≥50 years, and patients with high or high-middle SDI. CONCLUSIONS: The findings of this study further underpin the correlation between NAFLD and intrahepatic and extrahepatic cancers and highlight the possibility of tailored cancer screening for the NAFLD population at higher risks. FUNDING: This work was supported by the National Natural Science Foundation of China and Natural Science Foundation of Fujian Province of China.

Global burden of major gastrointestinal cancers and its association with socioeconomics, 1990-2019.

Background: To understand the impact of common cancers of the gastrointestinal tract and help to formulate evidence-based policy, we evaluate the relationship between the burden of GI tract cancers and socioeconomics. Methods: Data on GI tract cancer burden were obtained from the Global Burden of Disease (GBD) 2019 including mortality and incidence rates. According to the Socio-demographic Index (SDI) level, country and territory, and sex, etc., the data were further stratified. The association between the burden of GI tract cancer and socioeconomics, indicated by SDI, was described. Uncertainty analysis was estimated using bootstrap draw. Results: In 2019, five major cancers of the gastrointestinal tract led to an age-standardized incidence rate (ASIR) of 61.9 (95% CI 56.1-67.6) per 100 000 person-years. From 1990 to 2019, five common tumors of the gastrointestinal tract related age-standardized death rates (ASDRs) decreased by -22.7% (-31.1 to -13.5). For the five common tumors, ASIRs and ASDRs were both higher in males than those in females. Globally, Mongolia, and several East Asia countries exhibited the highest ASIRs in 2019. The high SDI, and high-middle SDI locations recorded the highest incidence rate and death rate of colon and rectum cancer and pancreatic cancer. On the contrary, the low-middle SDI, and low SDI locations possessed the highest incidence rate and death rate of stomach cancer and esophageal cancer. Conclusion: There is a profound association between socioeconomics and burden of common cancers of the gastrointestinal tract. It would be helpful for the high SDI, and high-middle SDI locations to pay special attention to the screening of colon and rectum cancer and pancreatic cancer while the low-middle SDI, and low SDI locations should pay more attention to the screening of stomach cancer and esophageal cancer.

Deep Learning-Based Universal Expert-Level Recognizing Pathological Images of Hepatocellular Carcinoma and Beyond.

Background and Aims: We aim to develop a diagnostic tool for pathological-image classification using transfer learning that can be applied to diverse tumor types. Methods: Microscopic images of liver tissue with and without hepatocellular carcinoma (HCC) were used to train and validate the classification framework based on a convolutional neural network. To evaluate the universal classification performance of the artificial intelligence (AI) framework, histological images from colorectal tissue and the breast were collected. Images for the training and validation sets were obtained from the Xiamen Hospital of Traditional Chinese Medicine, and those for the test set were collected from Zhongshan Hospital Xiamen University. The accuracy, sensitivity, and specificity values for the proposed framework were reported and compared with those of human image interpretation. Results: In the human-machine comparisons, the sensitivity, and specificity for the AI algorithm were 98.0, and 99.0%, whereas for the human experts, the sensitivity ranged between 86.0 and 97.0%, while the specificity ranged between 91.0 and 100%. Based on transfer learning, the accuracies of the AI framework in classifying colorectal carcinoma and breast invasive ductal carcinoma were 96.8 and 96.0%, respectively. Conclusion: The performance of the proposed AI framework in classifying histological images with HCC was comparable to the classification performance achieved by human experts, indicating that extending the proposed AI's application to diagnoses and treatment recommendations is a promising area for future investigation.

Liver stiffness measurement-based scoring system for significant inflammation related to chronic hepatitis B.

OBJECTIVES: Liver biopsy is indispensable because liver stiffness measurement alone cannot provide information on intrahepatic inflammation. However, the presence of fibrosis highly correlates with inflammation. We constructed a noninvasive model to determine significant inflammation in chronic hepatitis B patients by using liver stiffness measurement and serum markers. METHODS: The training set included chronic hepatitis B patients (n = 327), and the validation set included 106 patients; liver biopsies were performed, liver histology was scored, and serum markers were investigated. All patients underwent liver stiffness measurement. RESULTS: An inflammation activity scoring system for significant inflammation was constructed. In the training set, the area under the curve, sensitivity, and specificity of the fibrosis-based activity score were 0.964, 91.9%, and 90.8% in the HBeAg(+) patients and 0.978, 85.0%, and 94.0% in the HBeAg(-) patients, respectively. In the validation set, the area under the curve, sensitivity, and specificity of the fibrosis-based activity score were 0.971, 90.5%, and 92.5% in the HBeAg(+) patients and 0.977, 95.2%, and 95.8% in the HBeAg(-) patients. The liver stiffness measurement-based activity score was comparable to that of the fibrosis-based activity score in both HBeAg(+) and HBeAg(-) patients for recognizing significant inflammation (G ≥3). CONCLUSIONS: Significant inflammation can be accurately predicted by this novel method. The liver stiffness measurement-based scoring system can be used without the aid of computers and provides a noninvasive alternative for the prediction of chronic hepatitis B-related significant inflammation.

Reactive oxygen species: a double-edged sword in oncogenesis.

Reactive oxygen species (ROS) are molecules or ions formed by the incomplete one-electron reduction of oxygen. Of interest, it seems that ROS manifest dual roles, cancer promoting or cancer suppressing, in tumorigenesis. ROS participate simultaneously in two signaling pathways that have inverse functions in tumorigenesis, Ras-Raf-MEK1/2-ERK1/2 signaling and the p38 mitogen-activated protein kinases (MAPK) pathway. It is well known that Ras-Raf-MEK1/2-ERK1/2 signaling is related to oncogenesis, while the p38 MAPK pathway contributes to cancer suppression, which involves oncogene-induced senescence, inflammation-induced cellular senescence, replicative senescence, contact inhibition and DNA-damage responses. Thus, ROS may not be an absolute carcinogenic factor or cancer suppressor. The purpose of the present review is to discuss the dual roles of ROS in the pathogenesis of cancer, and the signaling pathway mediating their role in tumorigenesis.