An exploration of causal relationships between nine neurological diseases and the risk of breast cancer: a Mendelian randomization study.

BACKGROUND: Some preceding researches have observed that certain neurological disorders, such as Alzheimer's disease and multiple sclerosis, may affect breast cancer risk. However, whether there are causal relationships between these neurological conditions and breast cancer is inconclusive. This study was designed to explore whether neurological disorders affected the risks of breast cancer overall and of the two subtypes (ER+ and ER-). METHODS: In the course of this study, genome-wide association study (GWAS) data for nine neurological diseases (Alzheimer's disease, multiple sclerosis, Parkinson's disease, myasthenia gravis, generalized epilepsy, intracerebral haemorrhage, cerebral atherosclerosis, brain glioblastoma, and benign meningeal tumour) were collected from the Complex Trait Genetics lab and the MRC Integrative Epidemiology Unit, and single-nucleotide polymorphisms (SNPs) extensively associated with these neurological ailments had been recognized as instrumental variables (IVs). GWAS data on breast cancer were collected from the Breast Cancer Association Consortium (BCAC). Two-sample Mendelian randomization (MR) analyses as well as multivariable MR analyses were performed to determine whether these SNPs contributed to breast cancer risk. Additionally, the accuracy of the results was evaluated using the false discovery rate (FDR) multiple correction method. Both heterogeneity and pleiotropy were evaluated by analyzing sensitivities. RESULTS: According to the results of two-sample MR analyses, Alzheimer's disease significantly reduced the risks of overall (OR 0.925, 95% CI [0.871-0.982], P = 0.011) and ER+ (OR 0.912, 95% CI [0.853-0.975], P = 0.007) breast cancer, but there was a negative result in ER- breast cancer. However, after multiple FDR corrections, the effect of Alzheimer's disease on overall breast cancer was not statistically significant. In contrast, multiple sclerosis significantly increased ER+ breast cancer risk (OR 1.007, 95% CI [1.003-1.011], P = 0.001). In addition, the multivariable MR analyses showed that Alzheimer's disease significantly reduced the risk of ER+ breast cancer (IVW: OR 0.929, 95% CI [0.864-0.999], P=0.047; MR-Egger: OR 0.916, 95% CI [0.846-0.992], P=0.031); however, multiple sclerosis significantly increased the risk of ER+ breast cancer (IVW: OR 1.008, 95% CI [1.003-1.012], P=4.35×10-4; MR-Egger: OR 1.008, 95% CI [1.003-1.012], P=5.96×10-4). There were no significant associations between the remainder of the neurological diseases and breast cancer. CONCLUSIONS: This study found the trends towards a decreased risk of ER+ breast cancer in patients with Alzheimer's disease and an increased risk in patients with multiple sclerosis. However, due to the limitations of Mendelian randomization, we cannot determine whether there are definite causal relationships between neurological diseases and breast cancer risk. For conclusive evidences, more prospective randomized controlled trials will be needed in the future.

An exploratory study of whether axillary lymph node dissection can be avoided in breast cancer patients with positive lymph nodes.

Background: Breast cancer patients with positive axillary lymph nodes usually require axillary lymph node dissection (ALND), with many postoperative complications, such as lymphedema. For these patients, whether sentinel lymph node biopsy (SLNB) can replace ALND has been a research hotspot in the field of breast cancer. This study developed two risk stratification models for predicting the clinical outcomes of breast cancer patients with positive axillary lymph nodes receiving SLNB alone or ALND to determine which patients could potentially avoid ALND. Methods: A total of 21,942 breast cancer patients, including a training set (n=15,362) and a testing set (n=6,580), were enrolled in this study from Surveillance, Epidemiology, and End Results (SEER) between 2000 and 2017. The risk factors associated with breast cancer-specific survival (BCSS) and overall survival (OS) were evaluated using multivariate Cox regression analysis and then integrated into nomograms and risk stratification models examined by receiver operating characteristic (ROC) curves and calibration curves. The survival discrepancies were compared between the SLNB and ALND subgroups with different risk scores with Kaplan-Meier plots. Results: In multivariate Cox regression analyses, grade, marital status, T stage, radiotherapy and lymph node metastasis (GMTRL) were independent risk factors in breast cancer patients with both OS and BCSS status in the ALND cohort from the training set. Nomograms have been developed based on these factors to predict 3- and 5-year OS and BCSS in patients with ALND. Calibration curves and ROC curves in both the training and testing sets confirmed the excellent overall predictive performance of the nomograms. Furthermore, we developed two risk stratification models based on OS and BCSS status, revealing that patients with low GMTRL scores might avoid ALND in both OS and BCSS status [OS: hazard ratio (HR) =0.929, 95% confidence interval (CI): 0.841-1.027, P=0.150; BCSS: HR =0.953, 95% CI: 0.831-1.094, P=0.495], but patients with moderate (OS: HR =0.756, 95% CI: 0.666-0.859, P<0.001; BCSS: HR =0.643, 95% CI: 0.537-0.768, P<0.001) and high GMTRL scores could not (OS: HR =0.719, 95% CI: 0.549-0.940, P=0.014; BCSS: HR =0.731, 95% CI: 0.549-0.974, P=0.031). Conclusions: Breast cancer patients with positive nodes could be treated with SLNB alone rather than ALND without affecting prognosis based on GMTRL scores. Patients with high or moderate GMTRL scores benefited greatly from ALND, but not for patients with low GMTRL scores. This study may assist clinicians in tailoring treatments.

Applications of photodynamic therapy in extramammary Paget's disease.

Extramammary Paget's disease (EMPD) is a rare form of adenocarcinoma usually found in apocrine gland-containing cutaneous regions. EMPD affects the vulvar area most commonly, followed by the perianal area, scrotum, penis, and axillary region. In its initial form, EMPD presents as an erythematous plaque with well-defined edges, fine scaling, excoriations, exulcerations, and lichenification. Generally, a definitive diagnosis can be made through histopathological analysis. Importantly, associated malignancies should be investigated prior to treatment initiation. Photodynamic therapy (PDT) is a modern, noninvasive treatment strategy for non-oncological diseases as well as various cancers. In recent years, PDT has been widely used to treat EMPD. This present article presents a discussion of the diagnosis and treatment of EMPD as well as the usefulness of PDT in its management.

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.

Background: Previous 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. Methods: During 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. Results: When 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. Conclusions: The 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.

Infiltrating myeloid cell diversity determines oncological characteristics and clinical outcomes in breast cancer.

BACKGROUND: Breast cancer presents as one of the top health threats to women around the world. Myeloid cells are the most abundant cells and the major immune coordinator in breast cancer tumor microenvironment (TME), target therapies that harness the anti-tumor potential of myeloid cells are currently being evaluated in clinical trials. However, the landscape and dynamic transition of myeloid cells in breast cancer TME are still largely unknown. METHODS: Myeloid cells were characterized in the single-cell data and extracted with a deconvolution algorithm to be assessed in bulk-sequencing data. We used the Shannon index to describe the diversity of infiltrating myeloid cells. A 5-gene surrogate scoring system was then constructed and evaluated to infer the myeloid cell diversity in a clinically feasible manner. RESULTS: We dissected the breast cancer infiltrating myeloid cells into 15 subgroups including macrophages, dendritic cells (DCs), and monocytes. Mac_CCL4 had the highest angiogenic activity, Mac_APOE and Mac_CXCL10 were highly active in cytokine secretion, and the DCs had upregulated antigen presentation pathways. The infiltrating myeloid diversity was calculated in the deconvoluted bulk-sequencing data, and we found that higher myeloid diversity was robustly associated with more favorable clinical outcomes, higher neoadjuvant therapy responses, and a higher rate of somatic mutations. We then used machine learning methods to perform feature selection and reduction, which generated a clinical-friendly scoring system consisting of 5 genes (C3, CD27, GFPT2, GMFG, and HLA-DPB1) that could be used to predict clinical outcomes in breast cancer patients. CONCLUSIONS: Our study explored the heterogeneity and plasticity of breast cancer infiltrating myeloid cells. By using a novel combination of bioinformatic approaches, we proposed the myeloid diversity index as a new prognostic metric and constructed a clinically practical scoring system to guide future patient evaluation and risk stratification.