Corrigendum: The immune factors have complex causal regulation effects on inflammatory bowel disease.

[This corrects the article DOI: 10.3389/fimmu.2023.1322673.].

Common gastrointestinal diseases and chronic obstructive pulmonary disease risk: a bidirectional Mendelian randomization analysis.

Background: Observational studies suggest an association between gastrointestinal diseases and chronic obstructive pulmonary disease (COPD), but the causal relationship remains unclear. Methods: We conducted bidirectional Mendelian randomization (MR) analysis using summary data from genome-wide association study (GWAS) to explore the causal relationship between common gastrointestinal diseases and COPD. Gastrointestinal diseases included gastroesophageal reflux disease (GERD), peptic ulcer disease (PUD), irritable bowel syndrome (IBS), Crohn's disease (CD), ulcerative colitis (UC), functional dyspepsia (FD), non-infectious gastroenteritis (NGE), and constipation (CP). Significant MR analysis results were replicated in the COPD validation cohort. Results: Bidirectional MR analysis supported a bidirectional causal relationship between GERD and COPD, and COPD was also found to increase the risk of IBS and CP. Our study also provided evidence for a bidirectional causal relationship between PUD and COPD, although the strength of evidence may be insufficient. Furthermore, we provided evidence that there is no causal association between CD, UC, FD, NGE, and COPD. Conclusion: This study offers some evidence to clarify the causal relationship between common gastrointestinal diseases and COPD. Further research is needed to understand the underlying mechanisms of these associations.

Gut microbiota and common gastrointestinal diseases: a bidirectional two-sample Mendelian randomized study.

Background: Several recent studies have shown an association between gut microbiota and gastrointestinal diseases. However, the causal relationship between gut microbiota and gastrointestinal disorders is unclear. Methods: We assessed causal relationships between gut microbiota and eight common gastrointestinal diseases using Mendelian randomization (MR) analyses. IVW results were considered primary results. Cochrane's Q and MR-Egger tests were used to test for heterogeneity and pleiotropy. Leave-one-out was used to test the stability of the MR results, and Bonferroni correction was used to test the strength of the causal relationship between exposure and outcome. Results: MR analyses of 196 gut microbiota and eight common gastrointestinal disease phenotypes showed 62 flora and common gastrointestinal diseases with potential causal relationships. Among these potential causal relationships, after the Bonferroni-corrected test, significant causal relationships remained between Genus Oxalobacter and CD (OR = 1.29, 95% CI: 1.13-1.48, p = 2.5 × 10-4, q = 4.20 × 10-4), and between Family Clostridiaceae1 and IBS (OR = 0.9967, 95% CI: 0.9944-0.9991, p = 1.3 × 10-3, q = 1.56 × 10-3). Cochrane's Q-test showed no significant heterogeneity among the various single nucleotide polymorphisms (SNPs). In addition, no significant level of pleiotropy was found according to the MR-Egger. Conclusion: This study provides new insights into the mechanisms of gut microbiota-mediated gastrointestinal disorders and some guidance for targeting specific gut microbiota for treating gastrointestinal disorders.

Leukocyte telomere length is associated with increased risk of endometriosis: a bidirectional two-sample Mendelian randomization study.

Background: Endometriosis (EMs) is a common gynecological disorder. Observational studies on the relationship between leukocyte telomere length (LTL) and EMs have shown conflicting results. The purpose of this study was to evaluate the precise causal relationship between LTL and EMs using Mendelian randomization (MR) methodology. Methods: We employed MR to assess the causal relationship between LTL and EMs. Summary data from several large-scale genome-wide association studies (GWAS) were used for bidirectional two-sample MR analysis. Sensitivity analyses were conducted to ensure the robustness of our results. All analyses were also replicated in another completely independent EMs dataset. Results: Our MR analysis indicated that genetically predicted longer LTL increased the risk of EMs (IVW: discovery, OR=1.169, 95%CI: 1.059-1.290, p=0.002; validation, OR=1.302, 95%CI: 1.140-1.487, p=0.000), while EMs had no causal impact on LTL (IVW: discovery, OR=1.013, 95%CI: 1.000-1.027, p=0.056; IVW: validation, OR=1.005, 95%CI: 0.995-1.015, p=0.363). Causal estimates were supported by various calculation models (including MR-Egger, Weighted median, MR-PRESSO, and MR-RAPS). Heterogeneity and pleiotropy analyses also indicated robustness of the results. Conclusion: Our findings substantiate the idea that a genetically predicted longer LTL elevates the risk of EMs, with no influence of EMs on LTL risk. This research bolsters the causal link between LTL and EMs, overcoming the constraints of earlier observational studies. It implies that LTL may potentially function as a biomarker for EMs, opening up novel possibilities for EMs prevention and treatment.

A machine learning-based model for predicting distant metastasis in patients with rectal cancer.

Background: Distant metastasis from rectal cancer usually results in poorer survival and quality of life, so early identification of patients at high risk of distant metastasis from rectal cancer is essential. Method: The study used eight machine-learning algorithms to construct a machine-learning model for the risk of distant metastasis from rectal cancer. We developed the models using 23867 patients with rectal cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Meanwhile, 1178 rectal cancer patients from Chinese hospitals were selected to validate the model performance and extrapolation. We tuned the hyperparameters by random search and tenfold cross-validation to construct the machine-learning models. We evaluated the models using the area under the receiver operating characteristic curves (AUC), the area under the precision-recall curve (AUPRC), decision curve analysis, calibration curves, and the precision and accuracy of the internal test set and external validation cohorts. In addition, Shapley's Additive explanations (SHAP) were used to interpret the machine-learning models. Finally, the best model was applied to develop a web calculator for predicting the risk of distant metastasis in rectal cancer. Result: The study included 23,867 rectal cancer patients and 2,840 patients with distant metastasis. Multiple logistic regression analysis showed that age, differentiation grade, T-stage, N-stage, preoperative carcinoembryonic antigen (CEA), tumor deposits, perineural invasion, tumor size, radiation, and chemotherapy were-independent risk factors for distant metastasis in rectal cancer. The mean AUC value of the extreme gradient boosting (XGB) model in ten-fold cross-validation in the training set was 0.859. The XGB model performed best in the internal test set and external validation set. The XGB model in the internal test set had an AUC was 0.855, AUPRC was 0.510, accuracy was 0.900, and precision was 0.880. The metric AUC for the external validation set of the XGB model was 0.814, AUPRC was 0.609, accuracy was 0.800, and precision was 0.810. Finally, we constructed a web calculator using the XGB model for distant metastasis of rectal cancer. Conclusion: The study developed and validated an XGB model based on clinicopathological information for predicting the risk of distant metastasis in patients with rectal cancer, which may help physicians make clinical decisions. rectal cancer, distant metastasis, web calculator, machine learning algorithm, external validation.

Applying machine learning techniques to predict the risk of lung metastases from rectal cancer: a real-world retrospective study.

Background: Metastasis in the lungs is common in patients with rectal cancer, and it can have severe consequences on their survival and quality of life. Therefore, it is essential to identify patients who may be at risk of developing lung metastasis from rectal cancer. Methods: In this study, we utilized eight machine-learning methods to create a model for predicting the risk of lung metastasis in patients with rectal cancer. Our cohort consisted of 27,180 rectal cancer patients selected from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2017 for model development. Additionally, we validated our models using 1118 rectal cancer patients from a Chinese hospital to evaluate model performance and generalizability. We assessed our models' performance using various metrics, including the area under the curve (AUC), the area under the precision-recall curve (AUPR), the Matthews Correlation Coefficient (MCC), decision curve analysis (DCA), and calibration curves. Finally, we applied the best model to develop a web-based calculator for predicting the risk of lung metastasis in patients with rectal cancer. Result: Our study employed tenfold cross-validation to assess the performance of eight machine-learning models for predicting the risk of lung metastasis in patients with rectal cancer. The AUC values ranged from 0.73 to 0.96 in the training set, with the extreme gradient boosting (XGB) model achieving the highest AUC value of 0.96. Moreover, the XGB model obtained the best AUPR and MCC in the training set, reaching 0.98 and 0.88, respectively. We found that the XGB model demonstrated the best predictive power, achieving an AUC of 0.87, an AUPR of 0.60, an accuracy of 0.92, and a sensitivity of 0.93 in the internal test set. Furthermore, the XGB model was evaluated in the external test set and achieved an AUC of 0.91, an AUPR of 0.63, an accuracy of 0.93, a sensitivity of 0.92, and a specificity of 0.93. The XGB model obtained the highest MCC in the internal test set and external validation set, with 0.61 and 0.68, respectively. Based on the DCA and calibration curve analysis, the XGB model had better clinical decision-making ability and predictive power than the other seven models. Lastly, we developed an online web calculator using the XGB model to assist doctors in making informed decisions and to facilitate the model's wider adoption (https://share.streamlit.io/woshiwz/rectal_cancer/main/lung.py). Conclusion: In this study, we developed an XGB model based on clinicopathological information to predict the risk of lung metastasis in patients with rectal cancer, which may help physicians make clinical decisions.

The immune factors have complex causal regulation effects on inflammatory bowel disease.

Background: Although a correlation between immune cell phenotypes and inflammatory bowel disease (IBD) has been established, a causal relationship remains unestablished. Methods: To assess causal associations between immune cell phenotypes and IBD and its subtypes, we employed Mendelian randomization (MR) methods and genome-wide association studies (GWAS) summary statistics. The primary outcomes were determined based on the inverse variance weighting (IVW) results, with the assessment of heterogeneity and pleiotropy conducted through Cochrane's Q-test and MR-Egger. The stability of the MR results was then examined using leave-one-out analysis, and false discovery rate (FDR) correction was applied to evaluate the strength of the causal relationship between exposure and outcome. Furthermore, to identify immunophenotypes strongly associated with IBD, a meta-integration of the effect values of all positive results in both datasets was conducted. Results: The analysis of 731 immune cell phenotypes and IBD using MR techniques revealed potential causal associations between 26 phenotypes and IBD. Subsequent meta-integration of the two datasets provided evidence of solid causal associations between 18 immune phenotypes and IBD and its subtypes. Nominal causal associations were also identified in the remaining eight immune phenotypes and IBD and its subtypes. Conclusion: Our study confirms causal solid associations between 18 immune phenotypes and IBD, thus guiding future clinical studies.

Targeted therapy of RET fusion-positive non-small cell lung cancer.

Lung cancer has very high morbidity and mortality worldwide, and the prognosis is not optimistic. Previous treatments for non-small cell lung cancer (NSCLC) have limited efficacy, and targeted drugs for some gene mutations have been used in NSCLC with considerable efficacy. The RET proto-oncogene is located on the long arm of chromosome 10 with a length of 60,000 bp, and the expression of RET gene affects cell survival, proliferation, growth and differentiation. This review will describe the basic characteristics and common fusion methods of RET genes; analyze the advantages and disadvantages of different RET fusion detection methods; summarize and discuss the recent application of non-selective and selective RET fusion-positive inhibitors, such as Vandetanib, Selpercatinib, Pralsetinib and Alectinib; discuss the mechanism and coping strategies of resistance to RET fusion-positive inhibitors.

Thyroid Function and COVID-19 Susceptibility and Its Severity: A Two-Sample Mendelian Randomization Study.

Several observational studies have confirmed the relationship between thyroid hormones and coronavirus disease 2019 (COVID-19), but this correlation remains controversial. We performed a two-sample Mendelian randomization (MR) analysis based on the largest publicly available summary datasets. Summary statistics with 49 269 individuals for free thyroxine (FT4) and 54 288 for thyroid stimulating hormone (TSH) were used as exposure instruments. Genome-wide association studies of susceptibility (cases = 38 984; controls = 1 644 784), hospitalization (cases: 9986 = controls = 1 877 672), and very severe disease (cases = 5101; controls = 1 383 241) of COVID-19 were used as the outcome. We used the inverse-variance weighted (IVW) method as the primary analysis, and utilized MR-Egger regression, weighted median, and robust adjusted profile score (RAPS) for sensitivity analysis. Genetic predisposition to higher serum levels of FT4 within the normal range was negatively associated with the risk of COVID-19 hospitalization (odds ratio [OR] = 0.818; 95% CI, 0.718-0.932; P = 2.6 × 10-3) and very severe disease (OR = 0.758; 95% CI, 0.626-0.923; P = 5.8 × 10-3), but not susceptibility. There is no evidence that genetically predicted circulating TSH levels are associated with COVID-19 susceptibility and severity risk. Neither apparent pleiotropy nor heterogeneity were detected in the sensitivity analysis. In summary, we found that higher FT4 levels may reduce the risk of COVID-19 severity, suggesting that thyroid function testing may be required for patients with COVID-19.

Application of machine learning techniques in real-world research to predict the risk of liver metastasis in rectal cancer.

Background: The liver is the most common site of distant metastasis in rectal cancer, and liver metastasis dramatically affects the treatment strategy of patients. This study aimed to develop and validate a clinical prediction model based on machine learning algorithms to predict the risk of liver metastasis in patients with rectal cancer. Methods: We integrated two rectal cancer cohorts from Surveillance, Epidemiology, and End Results (SEER) and Chinese multicenter hospitals from 2010-2017. We also built and validated liver metastasis prediction models for rectal cancer using six machine learning algorithms, including random forest (RF), light gradient boosting (LGBM), extreme gradient boosting (XGB), multilayer perceptron (MLP), logistic regression (LR), and K-nearest neighbor (KNN). The models were evaluated by combining several metrics, such as the area under the curve (AUC), accuracy score, sensitivity, specificity and F1 score. Finally, we created a network calculator using the best model. Results: The study cohort consisted of 19,958 patients from the SEER database and 924 patients from two hospitals in China. The AUC values of the six prediction models ranged from 0.70 to 0.95. The XGB model showed the best predictive power, with the following metrics assessed in the internal test set: AUC (0.918), accuracy (0.884), sensitivity (0.721), and specificity (0.787). The XGB model was assessed in the outer test set with the following metrics: AUC (0.926), accuracy (0.919), sensitivity (0.740), and specificity (0.765). The XGB algorithm also shows a good fit on the calibration decision curves for both the internal test set and the external validation set. Finally, we constructed an online web calculator using the XGB model to help generalize the model and to assist physicians in their decision-making better. Conclusion: We successfully developed an XGB-based machine learning model to predict liver metastasis from rectal cancer, which was also validated with a real-world dataset. Finally, we developed a web-based predictor to guide clinical diagnosis and treatment strategies better.