Background: Gastroduodenal ulcers are associated with Helicobacter pylori infection and the use of nonsteroidal anti-inflammatory drugs (NSAIDs). However, the causal relationship between gastroduodenal ulcers and gut microbiota, especially specific gut microbiota, remains unclear.Methods: We conducted an analysis of published data on the gut microbiota and Gastroduodenal ulcer using genome-wide association studies (GWAS). Two-sample Mendelian randomization (MR) analysis was performed to determine the causal relationship between gut microbiota and Gastroduodenal ulcer. Sensitivity, heterogeneity, and pleiotropy analyses were conducted to confirm the accuracy of the research findings.Results: Our study showed that the abundance of Enterobacteriaceae, Butyricicoccus, Candidatus Soleaferrea, Lachnospiraceae NC2004 group, Peptococcus, and Enterobacteriales was negatively correlated with the risk of Gastroduodenal ulcer. Conversely, the abundance of Streptococcaceae, Lachnospiraceae UCG010, Marvinbryantia, Roseburia, Streptococcus, Mollicutes RF9, and NB1n was positively correlated with the risk of Gastroduodenal ulcer. MR analysis revealed causal relationships between 13 bacterial genera and Gastroduodenal ulcer.Conclusion: This study represents a groundbreaking endeavor by furnishing preliminary evidence regarding the potentially advantageous or detrimental causal link between the gut microbiota and Gastroduodenal ulcer, employing Mendelian Randomization (MR) analysis for the first time. These discoveries have the potential to yield fresh perspectives on the prevention and therapeutic approaches concerning Gastroduodenal ulcer, with a specific focus on the modulation of the gut microbiota.

Background: Previous research has posited a potential correlation between the gut microbiota and the onset of appendicitis; however, the precise causal connection between appendicitis and the gut microbiota remains an unresolved and contentious issue.Methods: In this investigation, we performed a Mendelian randomization (MR) analysis employing publicly accessible summary data extracted from genome-wide association studies (GWAS) to elucidate the potential causal nexus between the gut microbiota and the development of appendicitis. We initially identified instrumental variables (IVs) through a comprehensive array of screening methodologies, subsequently executing MR analyses using the Inverse Variance Weighted (IVW) technique as our primary approach, supplemented by several alternative methods such as MR Egger, weighted median, simple mode, and weighted mode. Additionally, we implemented a series of sensitivity analysis procedures, encompassing Cochran’s Q test, MR-Egger intercept test, Mendelian Randomized Polymorphism Residual and Outlier (MR-PRESSO) test, and a leave-one-out test, to affirm the robustness and validity of our findings.Results: Our investigation indicates that an elevated prevalence of Deltaproteobacteria, Christensenellaceae, Desulfovibrionaceae, Eubacterium ruminantium group, Lachnospiraceae NK4A136 group, Methanobrevibacter, Desulfovibrionales, and Euryarchaeota is inversely associated with the risk of appendicitis. Conversely, we observed a positive correlation between an increased abundance of Family XIII, Howardella, and Veillonella and the susceptibility to appendicitis. Sensitivity analyses have corroborated the robustness of these findings, and Mendelian randomization analyses provided no indications of reverse causality.Conclusion: Our Mendelian randomization (MR) analysis has unveiled potential advantageous or detrimental causal associations between the gut microbiota and the occurrence of appendicitis. This study offers novel theoretical and empirical insights into the understanding of appendicitis pathogenesis, along with its implications for preventive and therapeutic strategies.

Background: Previous studies have suggested a link between gut microbiota and skin diseases, including erysipelas, an inflammatory skin condition. Despite this, the precise nature of the relationship between erysipelas and gut microbiota remains unclear and subject to debate.Methods: We conducted a Mendelian Randomization (MR) analysis using publicly available summary data from genome-wide association studies (GWAS) to explore the potential causal relationship between gut microbiota and erysipelas. Instrumental variables (IVs) were identified using a comprehensive set of screening methods. We then performed MR analyses primarily using the Inverse Variance Weighted (IVW) method, complemented by alternative approaches such as MR Egger, weighted median, simple mode, and weighted mode. A series of sensitivity analyses, including Cochran’s Q test, MR-Egger intercept test, Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test, and a leave-one-out test, were executed to ensure the robustness and validity of our findings.Results: We identified potential associations between erysipelas and various gut microbiota, including Alcaligenaceae (OR 1.23; 95% CI 1.06-1.43; p=0.006), Rikenellaceae (OR 0.77; 95% CI 0.67-0.90; p=0.001), and others. Notably, associations with Actinomyces, Lachnospiraceae NC2004 group, Ruminiclostridium 9, Ruminococcaceae UCG014, Odoribacter, and Actinobacteria were also observed. Sensitivity analyses confirmed the robustness of these associations.Conclusion: Our MR analysis suggests both potentially beneficial and harmful causal relationships between various gut microbiota and the incidence of erysipelas. This study provides new theoretical and empirical insights into the pathogenesis of erysipelas and underscores the potential for innovative preventive and therapeutic approaches.

The definition of rectosigmoid junction (RSJ) is still in debate. The treatment and prognosis of patients with rectosigmoid junction cancer (RSJC) and positive lymph nodes (PLN-RSJCs) are mostly based on the American Joint Committee on Cancer (AJCC) staging system. Our study aims to assist clinicians in creating a more intuitive and accurate nomogram model for PLN-RSJCs for the prediction of patient overall survival (OS) after surgery. Based on the Surveillance, Epidemiology, and End Results (SEER) database, we extracted 3384 patients with PLN-RSJCs and randomly divided them into development (n = 2344) and validation (n = 1004) cohorts at a ratio of 7:3. Using univariate and multivariate COX regression analysis, we identified independent risk factors associated with OS in PLN-RSJCs in the development cohort, which were further used to establish a nomogram model. To verify the accuracy of the model, the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and an internal validation cohort have been employed. Decision curve analysis (DCA) was used to assess the clinical applicability and benefits of the generated model. Survival curves of the low- and high-risk groups were calculated using the Kaplan–Meier method together with the log-rank test. Age, marital, chemotherapy, AJCC stage, T and N stage of TNM system, tumor size, and regional lymph nodes were selected as independent risk factors and included in the nomogram model. The C-index of this nomogram in the development (0.751;0.737–0.765) and validation cohorts (0.750;0.764–0.736) were more significant than that of the AJCC 7th staging system (0.681; 0.665–0.697). The ROC curve with the calculated area under the curve (AUC) in the development cohort was 0.845,0.808 and 0.800 for 1-year, 3-year and 5-year OS, AUC in the validation cohort was 0.815,0.833 and 0.814 for 1-year, 3-year and 5-year, respectively. The calibration plots of both cohorts for 1-year,3-year and 5-year OS all demonstrated good agreement between actual clinical observations and predicted outcomes. In the development cohort, the DCA showed that the nomogram prediction model is more advantageous for clinical application than the AJCC 7th staging system. Kaplan–Meier curves in the low and high groups showed significant difference in patient OS. We established an accurate nomogram model for PLN-RSJCs, intended to support clinicians in the treatment and follow-up of patients.

Background Appendiceal cancer is a rare malignancy that is often discovered incidentally in acute appendicitis patients. The diagnosis and treatment of this disease are challenging due to its low incidence and nonspecific symptoms. In this study, we used bioinformatics and machine learning to identify novel diagnostic genes for appendiceal cancer in acute appendicitis. Methods To identify differentially expressed genes (DEGs) between acute appendicitis and appendiceal cancer, we analyzed two GEO datasets (GSE9575 and GSE7535). Then, to select the most important differential expressed genes for diagnosis, we used three machine learning methods (LASSO logistic regression, SVM-RFE, and RandomForest). We also performed functional enrichment analysis and CIBERSORT analysis to explore the biological functions and immune cell infiltration of the feature genes. Results We identified 45 DEGs between appendiceal cancer and acute appendicitis, of which 23 were upregulated and 22 were downregulated in appendiceal cancer. The ROC curve analysis showed that the feature genes had an AUC of 1.000 for discriminating appendiceal cancer from acute appendicitis. The functional enrichment analysis revealed that the feature genes were mainly involved in cell cycle regulation, DNA replication, and DNA repair pathways. The CIBERSORT analysis showed that the feature genes were associated with different immune cell types, such as B cells, T cells, macrophages, and dendritic cells. Conclusion We identified novel critical genes involved in the progression of appendiceal cancer and provided potential biomarkers for its diagnosis. Our findings also suggested that the feature genes might play a role in modulating the immune response in appendiceal cancer.

Objective: The definition of rectosigmoid junction (RSJ) is still in debate. The treatment and prognosis of patients with rectosigmoid junction cancer (RSJC) and positive lymph nodes (PLN-RSJCs) are mostly based on the American Joint Committee on Cancer (AJCC) staging system. Our study aims to assist clinicians in creating a more intuitive and accurate nomogram model for PLN-RSJCs for the prediction of patient overall survival (OS) after surgery. Methods: Based on the Surveillance, Epidemiology, and End Results (SEER) database, we extracted 3384 patients with PLN-RSJCs and randomly divided them into development (n=2344) and validation (n=1004) cohorts at a radio of 7:3. Using univariate and multivariate COX regression analysis, we identified independent risk factors associated with OS in PLN-RSJCs in the development cohort, which were further used to establish a nomogram model. To verify the accuracy of the model, the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and an internal validation cohort have been employed. Decision curve analysis (DCA) was used to assess the clinical applicability and benefits of the generated model. Survival curves of the low- and high-risk groups were calculated using the Kaplan–Meier method together with the log-rank test. Results: Age, marital, chemotherapy, AJCC stage, T and N stage of TNM system, tumor size, and regional lymph nodes were selected as independent risk factors and included in the nomogram model. The C-index of this nomogram in the development (0.751;0.737-0.765) and validation cohorts (0.750;0.764-0.736) were more significant than that of the AJCC 7th staging system (0.681; 0.665-0.697). The ROC curve with the calculated area under the curve (AUC) in the development cohort was 0.845,0.808 and 0.800 for 1-year, 3-year and 5-year OS, AUC in the validation cohort was 0.815,0.833 and 0.814 for 1-year, 3-year and 5-year, respectively. The calibration plots of both cohorts for 1-year,3-year and 5-year OS all demonstrated good agreement between actual clinical observations and predicted outcomes. In the development cohort, the DCA showed that the nomogram prediction model is more advantageous for clinical application than the AJCC 7th staging system. Kaplan-Meier curves in the low and high groups showed significant differences in patient OS. Conclusions: We established an accurate nomogram model for PLN-RSJCs, intended to support clinicians in the treatment and follow-up of patients.