An exploration of the correlations between seven psychiatric disorders and the risks of breast cancer, breast benign tumors and breast inflammatory diseases: Mendelian randomization analyses

BackgroundPrevious observational studies have showed that certain psychiatric disorders may be linked to breast cancer risk, there is, however, little understanding of relationships between mental disorders and a variety of breast diseases. This study aims to investigate if mental disorders influence the risks of overall breast cancer, the two subtypes of breast cancer (ER+ and ER-), breast benign tumors and breast inflammatory diseases.MethodsDuring our research, genome-wide association study (GWAS) data for seven psychiatric disorders (schizophrenia, major depressive disorder, bipolar disorder, post-traumatic stress disorder, panic disorder, obsessive-compulsive disorder and anorexia nervosa) from the Psychiatric Genomics Consortium (PGC) and the UK Biobank were selected, and single-nucleotide polymorphisms (SNPs) significantly linked to these mental disorders were identified as instrumental variables. GWAS data for breast diseases came from the Breast Cancer Association Consortium (BCAC) as well as the FinnGen consortium. We performed two-sample Mendelian randomization (MR) analyses and multivariable MR analyses to assess these SNPs’ effects on various breast diseases. Both heterogeneity and pleiotropy were evaluated by sensitivity analyses.ResultsWhen the GWAS data of psychiatric disorders were derived from the PGC, our research found that schizophrenia significantly increased the risks of overall breast cancer (two-sample MR: OR 1.05, 95%CI [1.03-1.07], p = 3.84 × 10−6; multivariable MR: OR 1.06, 95%CI [1.04-1.09], p = 2.34 × 10−6), ER+ (OR 1.05, 95%CI [1.02-1.07], p = 5.94 × 10−5) and ER- (two-sample MR: OR 1.04, 95%CI [1.01-1.07], p = 0.006; multivariable MR: OR 1.06, 95%CI [1.02-1.10], p = 0.001) breast cancer. Nevertheless, major depressive disorder only showed significant positive association with overall breast cancer (OR 1.12, 95%CI [1.04-1.20], p = 0.003) according to the two-sample MR analysis, but not in the multivariable MR analysis. In regards to the remainder of the mental illnesses and breast diseases, there were no significant correlations. While as for the data from the UK Biobank, schizophrenia did not significantly increase the risk of breast cancer.ConclusionsThe correlation between schizophrenia and breast cancer found in this study may be false positive results caused by underlying horizontal pleiotropy, rather than a true cause-and-effect relationship. More prospective studies are still needed to be carried out to determine the definitive links between mental illnesses and breast diseases

Enhancing the Ammonia Selectivity by Using Nanofiber PVDF Composite Membranes Fabricated with Functionalized Carbon Nanotubes

Conventional hydrophobic membrane-based membrane distillation (MD) has been applied for ammonia recovery from an anaerobic digestion (AD) effluent. However, the typical hydrophobic membranes do not have selectivity for ammonia and water vapor, which results in high energy consumption from the water evaporation. To enhance the selectivity during the ammonia recovery process, the functionalized carbon nanotubes (CNTs)/polyvinylidene fluoride (PVDF) nanofiber membranes were fabricated by electrospinning, and the effects of different CNTs and their contents on the performance of nanofiber membranes were investigated. The results indicate that CNTs can be successfully incorporated into nanofibers by electrospinning. The contact angles of the composite membrane are all higher than those of commercial membrane, and the highest value 138° can be obtained. Most importantly, under the condition of no pH adjustment, the ammonia nitrogen transfer coefficient reaches the maximum value of 3.41 × 10−6 m/s, which is about twice higher than that of commercial membranes. The ammonia separation factor of the carboxylated CNT (C-CNT) composite membrane is higher than that of the hydroxylated CNT(H-CNT) composite membrane. Compared with the application of the novel C-CNT composite membrane, the ammonia separation factor is 47% and 25% higher than that of commercial and neat PVDF membranes. This work gives a novel approach for enhancing ammonia and water selectivity during AD effluent treatment

Establishment and validation of a multigene model to predict the risk of relapse in hormone receptor-positive early-stage Chinese breast cancer patients

Abstract. Background:. Breast cancer patients who are positive for hormone receptor typically exhibit a favorable prognosis. It is controversial whether chemotherapy is necessary for them after surgery. Our study aimed to establish a multigene model to predict the relapse of hormone receptor-positive early-stage Chinese breast cancer after surgery and direct individualized application of chemotherapy in breast cancer patients after surgery. Methods:. In this study, differentially expressed genes (DEGs) were identified between relapse and nonrelapse breast cancer groups based on RNA sequencing. Gene set enrichment analysis (GSEA) was performed to identify potential relapse-relevant pathways. CIBERSORT and Microenvironment Cell Populations-counter algorithms were used to analyze immune infiltration. The least absolute shrinkage and selection operator (LASSO) regression, log-rank tests, and multiple Cox regression were performed to identify prognostic signatures. A predictive model was developed and validated based on Kaplan–Meier analysis, receiver operating characteristic curve (ROC). Results:. A total of 234 out of 487 patients were enrolled in this study, and 1588 DEGs were identified between the relapse and nonrelapse groups. GSEA results showed that immune-related pathways were enriched in the nonrelapse group, whereas cell cycle- and metabolism-relevant pathways were enriched in the relapse group. A predictive model was developed using three genes (CKMT1B, SMR3B, and OR11M1P) generated from the LASSO regression. The model stratified breast cancer patients into high- and low-risk subgroups with significantly different prognostic statuses, and our model was independent of other clinical factors. Time-dependent ROC showed high predictive performance of the model. Conclusions:. A multigene model was established from RNA-sequencing data to direct risk classification and predict relapse of hormone receptor-positive breast cancer in Chinese patients. Utilization of the model could provide individualized evaluation of chemotherapy after surgery for breast cancer patients