Causal association between 637 human metabolites and ovarian cancer: a mendelian randomization study.

BACKGROUND: Current evidence suggests a significant association between metabolites and ovarian cancer (OC); however, the causal relationship between the two remains unclear. This study employs Mendelian randomization (MR) to investigate the causal effects between different metabolites and OC. METHODS: In this study, a total of 637 metabolites were selected as the exposure variables from the Genome-wide Association Study (GWAS) database ( http://gwas.mrcieu.ac.uk/datasets/ ). The OC related GWAS dataset (ieu-b-4963) was chosen as the outcome variable. R software and the TwoSampleMR package were utilized for the analysis in this study. MR analysis employed the inverse variance-weighted method (IVW), MR-Egger and weighted median (WM) for regression fitting, taking into consideration potential biases caused by linkage disequilibrium and weak instrument variables. Metabolites that did not pass the tests for heterogeneity and horizontal pleiotropy were considered to have no significant causal effect on the outcome. Steiger's upstream test was used to determine the causal direction between the exposure and outcome variables. RESULTS: The results from IVW analysis revealed that a total of 31 human metabolites showed a significant causal effect on OC (P < 0.05). Among them, 9 metabolites exhibited consistent and stable causal effects, which were confirmed by Steiger's upstream test (P < 0.05). Among these 9 metabolites, Androsterone sulfate, Propionylcarnitine, 5alpha-androstan-3beta,17beta-diol disulfate, Total lipids in medium VLDL and Concentration of medium VLDL particles demonstrated a significant positive causal effect on OC, indicating that these metabolites promote the occurrence of OC. On the other hand, X-12,093, Octanoylcarnitine, N2,N2-dimethylguanosine, and Cis-4-decenoyl carnitine showed a significant negative causal association with OC, suggesting that these metabolites can inhibit the occurrence of OC. CONCLUSIONS: The study revealed the complex effect of metabolites on OC through Mendelian randomization. As promising biomarkers, these metabolites are worthy of further clinical validation.

Risk factors and prognostic factors for pulmonary large cell neuroendocrine carcinoma with brain metastasis.

BACKGROUND: As the studies regarding the brain metastasis (BM) of pulmonary large cell neuroendocrine carcinoma (LCNEC) are insufficient, the present research aims to describe the risk factors and prognostic factors that are related to cancer-specific survival (CSS) for LCNEC patients with BM. METHODS: The data of LCNEC patients between January 2010 and October 2018 were obtained from the SEER database. Binary logistic regression analyses were utilized to screen the possible risk factors related to BM. Prognostic factors for LCNEC patients with BM were indentified by Cox regression analyses. Moreover, a nomogram was established to predict the 6-, 12-, and 18-month CSS rates. The concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves were utilized to assess the discrimination and reliability of the model. Clinical decision curves (DCAs) were used to evaluate the clinical benefits and utility of our model. RESULTS: Totally, 1875 patients were enrolled, with 294 (15.7%) of them having BM at diagnosis. Multivariate logistic regression analyses revealed that patients with age < 65 (odds ratio, OR = 1.564) and N2 staging (OR = 1.775) had a greater chance of developing BM. Age (≥ 65 vs. < 65: hazard ratio, HR = 1.409), T staging (T1 vs. T0: HR = 4.580; T2 vs. T0: HR = 6.008; T3 vs. T0: HR = 7.065; T4 vs. T0: HR = 6.821), N staging (N2 vs. N0: HR = 1.592; N3 vs. N0: HR = 1.654), liver metastasis (HR = 1.410), primary site surgery (HR = 0.581) and chemotherapy (HR = 0.452) were independent prognostic factors for LCNEC patients with BM. A nomogram prediction model was constructed by incorporating these factors. Using the C-index, calibration curves, ROC curves, and DCAs, we found that the clinical prediction model performed well. CONCLUSION: We described the risk factors and prognostic factors that were associated with CSS for LCNEC patients with BM. The related nomogram was established and validated to help clinicians formulate more rational and effective treatment strategies.