Background: Several observational studies have explored the associations between Sjögren’s syndrome (SS) and certain cancers. Nevertheless, the causal relationships remain unclear. Mendelian randomization (MR) method was used to investigate the causality between SS and different types of cancers. Methods: We conducted the two-sample Mendelian randomization with the public genome-wide association studies (GWASs) summary statistics in European population to evaluate the causality between SS and nine types of cancers. The sample size varies from 1080 to 372,373. The inverse variance weighted (IVW) method was used to estimate the causal effects. A Bonferroni-corrected threshold of P < 0.0031 was considered significant, and P value between 0.0031 and 0.05 was considered to be suggestive of an association. Sensitivity analysis was performed to validate the causality. Moreover, additional analysis was used to assess the associations between SS and well-accepted risk factors of cancers. Results: After correcting the heterogeneity and horizontal pleiotropy, the results indicated that patients with SS were significantly associated with an increased risk of lymphomas (odds ratio [OR] = 1.0010, 95% confidence interval [CI]: 1.0005–1.0015, P = 0.0002) and reduced risks of prostate cancer (OR = 0.9972, 95% CI: 0.9960–0.9985, P = 2.45 × 10⁻⁵) and endometrial cancer (OR = 0.9414, 95% CI: 0.9158–0.9676, P = 1.65 × 10⁻⁵). Suggestive associations were found in liver and bile duct cancer (OR = 0.9999, 95% CI: 0.9997–1.0000, P = 0.0291) and cancer of urinary tract (OR = 0.9996, 95% CI: 0.9992–1.0000, P = 0.0281). No causal effect of SS on other cancer types was detected. Additional MR analysis indicated that causal effects between SS and cancers were not mediated by the well-accepted risk factors of cancers. No evidence of the causal relationship was observed for cancers on SS. Conclusions: SS had significant causal relationships with lymphomas, prostate cancer, and endometrial cancer, and suggestive evidence of association was found in liver and bile duct cancer and cancer of urinary tract, indicating that SS may play a vital role in the incidence of these malignancies.

Inflammatory breast cancer (IBC) is an aggressive and rare phenotype of breast cancer, which has a poor prognosis. Thus, it is necessary to establish a novel predictive model of high accuracy for the prognosis of IBC patients. Clinical information of 1,230 IBC patients from 2010 to 2020 was extracted from the Surveillance, Epidemiology and End Results (SEER) database. Cox analysis was applied to identify clinicopathological characteristics associated with the overall survival (OS) of IBC patients. Random survival forest (RSF) algorithm was adopted to construct an accurate prognostic prediction model for IBC patients. Kaplan-Meier analysis was performed for survival analyses. Race, N stage, M stage, molecular subtype, history of chemotherapy and surgery, and response to neoadjuvant therapy were identified as independent predictive factors for the OS of IBC patients. The top five significant variables included surgery, response to neoadjuvant therapy, chemotherapy, breast cancer molecular subtypes, and M stage. The C-index of RSF model was 0.7704 and the area under curve (AUC) values for 1, 3, 5 years in training and validation datasets were 0.879-0.955, suggesting the excellent predictive performance of RSF model. IBC patients were divided into high-risk group and low-risk group according the risk score of RSF model, and the OS of patients in the low-risk group was significantly longer than those in the high-risk group. In this study, we constructed a prognosis prediction model for IBC patients through RSF algorithm, which may potentially serve as a useful tool during clinical decision-making.

Background: Using Mendelian randomization (MR) analysis, this study sought to determine the causal relationship between genetically predicted cheese intake, probiotics and breast cancer. Methods: A MR analysis including univariable and multivariable MR approaches using the public genome-wide association studies summary statistics was conducted to assess the causal effects. Results: Genetically predicted cheese intake per standard-deviation increase was associated with decreased risks of luminal A-like breast cancer (odds ratio [OR] = 0.80, 95 % confidence interval [CI]: 0.66–0.97, P = 0.03). No causal effect was detected between probiotics and breast cancer. Multivariable MR analysis showed that the causal effect between cheese intake and luminal A-like breast cancer might be driven by several major ingredients in cheese, including calcium, vitamin D and IGF-1 levels. Conclusions: Cheese intake is causally associated with luminal A-like breast cancer.