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.

Liquid biopsy techniques and lung cancer: diagnosis, monitoring and evaluation.

Lung cancer stands as the most prevalent form of cancer globally, posing a significant threat to human well-being. Due to the lack of effective and accurate early diagnostic methods, many patients are diagnosed with advanced lung cancer. Although surgical resection is still a potential means of eradicating lung cancer, patients with advanced lung cancer usually miss the best chance for surgical treatment, and even after surgical resection patients may still experience tumor recurrence. Additionally, chemotherapy, the mainstay of treatment for patients with advanced lung cancer, has the potential to be chemo-resistant, resulting in poor clinical outcomes. The emergence of liquid biopsies has garnered considerable attention owing to their noninvasive nature and the ability for continuous sampling. Technological advancements have propelled circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), extracellular vesicles (EVs), tumor metabolites, tumor-educated platelets (TEPs), and tumor-associated antigens (TAA) to the forefront as key liquid biopsy biomarkers, demonstrating intriguing and encouraging results for early diagnosis and prognostic evaluation of lung cancer. This review provides an overview of molecular biomarkers and assays utilized in liquid biopsies for lung cancer, encompassing CTCs, ctDNA, non-coding RNA (ncRNA), EVs, tumor metabolites, TAAs and TEPs. Furthermore, we expound on the practical applications of liquid biopsies, including early diagnosis, treatment response monitoring, prognostic evaluation, and recurrence monitoring in the context of lung cancer.

Association between oral microbiome and breast cancer in the east Asian population: A Mendelian randomization and case-control study.

BACKGROUND: The causal relationship between breast cancer (BC) and the oral microbiome remains unclear. In this case-control study, using two-sample Mendelian randomization (MR), we thoroughly explored the relationship between the oral microbiome and BC in the East Asian population. METHODS: Genetic summary data related to oral microbiota and BC were collected from genome-wide association studies involving participants of East Asian descent. MR estimates were generated by conducting various analyses. Sequencing data from a case-control study were used to verify the validity of these findings. RESULTS: MR analysis revealed that 30 tongue and 37 salivary bacterial species were significantly associated with BC. Interestingly, in both tongue and salivary microbiomes, we observed the causal effect of six genera, namely, Aggregatibacter, Streptococcus, Prevotella, Haemophilus, Lachnospiraceae, Oribacterium, and Solobacterium, on BC. Our case-control study findings suggest differences in specific bacteria between patients with BC and healthy controls. Moreover, sequencing data confirmed the MR analysis results, demonstrating that compared with the healthy control group, the BC group had a higher relative abundance of Pasteurellaceae and Streptococcaceae but a lower relative abundance of Bacteroidaceae. CONCLUSIONS: Our MR analysis suggests that the oral microbiome exerts a causative effect on BC risk, supported by the sequencing data of a case-control study. In the future, studies should be undertaken to comprehensively understand the complex interaction mechanisms between the oral microbiota and BC.

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.

Association between psychiatric disorders and glioma risk: evidence from Mendelian randomization analysis.

BACKGROUND: Observational studies have explored the association of psychiatric disorders and the risk of brain cancers. However, the causal effect of specific mental illness on glioma remains elusive due to the lack of solid evidence. METHODS: We performed a two-sample bidirectional Mendelian randomization (MR) analysis to explore the causal relationships between 5 common psychiatric disorders (schizophrenia, major depressive disorder, bipolar disorder, autism spectrum disorder, and panic disorder) and glioma. Summary statistics for psychiatric disorders and glioma were extracted from Psychiatric Genomics Consortium (PGC) and 8 genome-wide association study (GWAS) datasets respectively. We calculated the MR estimates for odds ratio of glioma associated with each psychiatric disorder by using inverse-variance weighting (IVW) method. Sensitivity analyses such as weighted median estimator, MR-Egger and MR-PRESSO were leveraged to assess the strength of causal inference. RESULTS: A total of 30,657 participants of European ancestry were included in this study. After correction for multiple testing, we found that genetically predicted schizophrenia was associated with a statistically significant increase in odds of non-glioblastoma multiforme (non-GBM) (OR = 1.13, 95% CI: 1.03-1.23, P = 0.0096). There is little evidence for the causal relationships between the other 4 psychiatric disorders with the risk of glioma. CONCLUSIONS: In this MR analysis, we revealed an increased risk of non-GBM glioma in individuals with schizophrenia, which gives an insight into the etiology of glioma.

Oral VV116 versus placebo in patients with mild-to-moderate COVID-19 in China: a multicentre, double-blind, phase 3, randomised controlled study.

BACKGROUND: Spread of SARS-CoV-2 led to a global pandemic, and there remains unmet medical needs in the treatment of Omicron infections. VV116, an oral antiviral agent that has potent activity against SARS-CoV-2, was compared with a placebo in this phase 3 study to investigate its efficacy and safety in patients with mild-to-moderate COVID-19. METHODS: This multicentre, double-blind, phase 3, randomised controlled study enrolled adults in hospitals for infectious diseases and tertiary general hospitals in China. Eligible patients were randomly assigned in a 1:1 ratio using permuted block randomisation to receive oral VV116 (0·6 g every 12 h on day 1 and 0·3 g every 12 h on days 2-5) or oral placebo (on the same schedule as VV116) for 5 days. Randomisation stratification factors included SARS-CoV-2 vaccination status and the presence of high-risk factors for progression to severe COVID-19. Inclusion criteria were a positive SARS-CoV-2 test, an initial onset of COVID-19 symptoms 3 days or less before the first study dose, and a score of 2 or more for any target COVID-19-related symptoms in the 24 h before the first dose. Patients who had severe or critical COVID-19 or who had taken any antiviral drugs were excluded from the study. The primary endpoint was the time to clinical symptom resolution for 2 consecutive days. Efficacy analyses were performed on a modified intention-to-treat population, comprising all patients who received at least one dose of VV116 or placebo, tested positive for SARS-CoV-2 nucleic acid, and did not test positive for influenza virus before the first dose. Safety analyses were done on all participants who received at least one dose of VV116 or placebo. This study was registered with ClinicalTrials.gov, NCT05582629, and has been completed. FINDINGS: A total of 1369 patients were randomly assigned to treatment groups and 1347 received either VV116 (n=674) or placebo (n=673). At the interim analysis, VV116 was superior to placebo in reducing the time to sustained clinical symptom resolution among 1229 patients (hazard ratio [HR] 1·21, 95% CI 1·04-1·40; p=0·0023). At the final analysis, a substantial reduction in time to sustained clinical symptom resolution was observed for VV116 compared with placebo among 1296 patients (HR 1·17, 95% CI 1·04-1·33; p=0·0009), consistent with the interim analysis. The incidence of adverse events was similar between groups (242 [35·9%] of 674 patients vs 283 [42·1%] of 673 patients). INTERPRETATION: Among patients with mild-to-moderate COVID-19, VV116 significantly reduced the time to sustained clinical symptom resolution compared with placebo, with no observed safety concerns. FUNDING: Shanghai Vinnerna Biosciences, Shanghai Science and Technology Commission, and the National Key Research and Development Program of China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

A prospective comparison of a modified miniaturised hand-held epifluorescence microscope and touch imprint cytology for evaluation of axillary sentinel lymph nodes intraoperatively in breast cancer patients.

BACKGROUND: The management of axillary lymph nodes in early-stage breast cancer patients has changed considerably, with the primary focus shifting from the examination of sentinel lymph nodes (SLNs) to toward the detection of all macro-metastases. However, current methods, such as touch imprint cytology (TIC) and frozen sections, are inadequate for clinical needs. To address this issue, we proposed a novel miniaturised epifluorescence widefield microscope (MEW-M) to assess SLN status intraoperatively for improved diagnostic efficiency. METHODS: A prospective, side-by-side comparison of intraoperative SLN evaluation between MEW-M and TIC was performed. RESULTS: A total of 73 patients with 319 SLNs consecutive enrolled in this study. MEW-M showed significantly superior image quality compared to TIC (median score 3.1 vs 2.1, p < 0.0001) and had a shorter time to issue results (10.3 vs 19.4 min, p < 0.0001). Likelihood ratio analysis illustrated that the positive likelihood ratio value of MEW-M compared with TIC was infinitely great vs 52.37 (95% CI, 21.96-124.90) in model 1 (classifying results into negative/positive), infinitely great vs 52.37 (95% CI, 21.96-124.90) in model 2 (classifying results into macro-metastasis/others, and TIC results followed the same classification as model 1), respectively. Similarly, the negative likelihood ratio values of MEW-M compared with TIC were 0.055 (95% CI, 0.018-0.160) and 0.074 (95% CI, 0.029-0.190) in model 1; and 0.019 (95% CI, 0.003-0.130) vs 0.020 (95% CI, 0.003-0.140) in model 2, respectively. CONCLUSIONS: MEW-M is a promising technique that can be utilised to provide a rapid and accurate intraoperative assessment of SLN in a clinical setting to help improve decision-making in axillary surgery.

Association between viral infections and glioma risk: a two-sample bidirectional Mendelian randomization analysis.

BACKGROUND: Glioma is one of the leading types of brain tumor, but few etiologic factors of primary glioma have been identified. Previous observational research has shown an association between viral infection and glioma risk. In this study, we used Mendelian randomization (MR) analysis to explore the direction and magnitude of the causal relationship between viral infection and glioma. METHODS: We conducted a two-sample bidirectional MR analysis using genome-wide association study (GWAS) data. Summary statistics data of glioma were collected from the largest meta-analysis GWAS, involving 12,488 cases and 18,169 controls. Single-nucleotide polymorphisms (SNPs) associated with exposures were used as instrumental variables to estimate the causal relationship between glioma and twelve types of viral infections from corresponding GWAS data. In addition, sensitivity analyses were performed. RESULTS: After correcting for multiple tests and sensitivity analysis, we detected that genetically predicted herpes zoster (caused by Varicella zoster virus (VZV) infection) significantly decreased risk of low-grade glioma (LGG) development (OR = 0.85, 95% CI: 0.76-0.96, P = 0.01, FDR = 0.04). No causal effects of the other eleven viral infections on glioma and reverse causality were detected. CONCLUSIONS: This is one of the first and largest studies in this field. We show robust evidence supporting that genetically predicted herpes zoster caused by VZV infection reduces risk of LGG. The findings of our research advance understanding of the etiology of glioma.

Association between oral microbiome and seven types of cancers in East Asian population: a two-sample Mendelian randomization analysis.

Background: The oral microbiome has been intricately linked to various pathological conditions, notably cancer, though clear causal links remain elusive. This study aimed to investigate the potential causal relationships between the oral microbiome and seven major cancers: breast, lung, pancreatic, colorectal, gastric, ovarian, and prostate cancers, leveraging Mendelian randomization (MR). Methods: A two-sample MR analysis was conducted using genome-wide association study (GWAS) data specific to oral microbiota in individuals of East Asian descent. Single nucleotide polymorphisms (SNPs) independent of confounders served as instrumental variables (IVs) to deduce causality. MR methodologies such as the inverse variance weighted (IVW) method, weighted median (WM) method, and Mendelian randomization-Egger (MR-Egger) method were employed. The study utilized datasets encapsulating a multitude of cancer cases and controls, focusing on Asian populations. Results: Our analysis revealed intricate associations between specific bacterial genera of the oral microbiome and diverse cancers. Notably, Fusobacterium showed mixed associations with various cancers, while genera like Prevotella and Streptococcus exhibited nuanced roles across malignancies. The genus Aggregatibacter demonstrated a multifaceted influence, positively correlating with some cancers while inhibiting others. Conclusion: Our findings underscore the profound implications of the oral microbiome in systemic malignancies, suggesting potential modulatory roles in cancer etiology. These insights, though preliminary, accentuate the need for deeper exploration and could pave the way for novel therapeutic strategies.

Relationships among breast, gut, and oral microbiota across diverse pathological types of breast cancer, a Chinese cohort study.

Background: Recent research has unveiled the association between microbiota and the onset and progression of breast cancer (BC). This study investigates the microbiota in breast tissue, the gut, and the oral cavity in relation to different pathological types of breast diseases, aiming to unveil the microbiota-BC relationship and provide new perspectives for BC diagnosis and treatment. Methods: The study encompassed a total of 98 breast cancer patients, with 52 diagnosed with Luminal A BC, 17 with Luminal B BC, 18 with HER2 BC, and 11 with TNBC. In addition, there were 46 patients with non-malignant breast diseases. The V3-V5 region of the 16S rRNA gene of breast tissue, feces, and the oral cavity was sequenced. Based on Amplicon Sequence Variants (ASV) representative sequences and abundance information, a series of statistical analyses were conducted including community diversity analysis, community composition analysis, species difference analysis, correlation analysis, and functional prediction analysis. Results: Notable divergences in α-diversity and ß-diversity were discerned in breast tissue between BC patients and non-malignant breast disease patients. The linear discriminant analysis effect size (LEfSe) and random forest examinations pinpoint Pasteurellaceae as a significant predictor in BC cohorts. Further exploration revealed significant microbial distribution divergences across distinct pathological types of BC, with notable variations in the relative abundance of microbial species such as Streptococcus, Serratia, and Pseudomonas, underscoring the diverse microbial diversity across BC subtypes and sample origins. Conclusion: This venture sheds light on the complex microbiota milieu across varying body sites and pathological types of BC, emphasizing microbiota-BC connectivity. This articulation of a multisite microbiota-BC interrelation significantly advances a holistic grasp of BC pathogenesis.

Diagnostic value of circRNA in coronary heart disease: a meta-analysis.

Aim: Studies have indicated that circRNAs have diagnostic value for coronary heart disease (CHD), but the efficacy varies greatly; therefore, a meta-analysis was conducted to assess the diagnostic value of circRNAs in CHD. Materials & methods: 16 studies with 3962 subjects (2239 cases and 1723 controls) were included by searching PubMed, Web of Science and MEDLINE. The pooled sensitivity and specificity, summary receiver operating characteristic and area under the curve, positive likelihood ratio and negative likelihood ratio were calculated. Results: The pooled area under the curve of circRNAs for the diagnosis of CHD was 0.80 (sensitivity and specificity were 0.77 and 0.68, respectively), and more indexes were calculated. Conclusion: circRNAs may be good biomarkers for diagnosing CHD.

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.

Occult breast cancer patients with mastectomy have better prognosis than those with breast-conserving therapy.

Background: This single-center retrospective study compared the efficacy of breast-conserving therapy along with axillary lymph node dissection (ALND) with mastectomy and ALND with regard to survival of Chinese patients with occult breast cancer. Materials & methods: Univariate Kaplan-Meier analysis and multivariate Cox proportional hazards model were used to compare treatments and prognosis. Results: A total of 111 patients with a median follow-up of 72.9 months were included. 39 patients with mastectomy + ALND had better disease-free survival than 72 patients with breast-conserving therapy + ALND (HR = 0.31; p = 0.012). Patients with radiotherapy demonstrated inferior survival for both overall survival (HR = 2.67; p = 0.071) and disease-free survival (HR = 5.35; p = 0.002). Surgical strategies and radiotherapy remained significantly predictive of better disease-free survival in multivariate analyses. Conclusion: Mastectomy and ALND demonstrate superior disease-free prognosis compared with breast-conserving therapy and ALND in occult breast cancer.

Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep Learning.

BACKGROUND: Breast cancer is one of the most common malignant tumors in women. A noninvasive ultrasound examination can identify mammary-gland-related diseases and is well tolerated by dense breast, making it a preferred method for breast cancer screening and of significant clinical value. However, the diagnosis of breast nodules or masses via ultrasound is performed by a doctor in real time, which is time-consuming and subjective. Junior doctors are prone to missed diagnoses, especially in remote areas or grass-roots hospitals, due to limited medical resources and other factors, which bring great risks to a patient's health. Therefore, there is an urgent need to develop fast and accurate ultrasound image analysis algorithms to assist diagnoses. METHODS: We propose a breast ultrasound image-based assisted-diagnosis method based on convolutional neural networks, which can effectively improve the diagnostic speed and the early screening rate of breast cancer. Our method consists of two stages: tumor recognition and tumor classification. (1) Attention-based semantic segmentation is used to identify the location and size of the tumor; (2) the identified nodules are cropped to construct a training dataset. Then, a convolutional neural network for the diagnosis of benign and malignant breast nodules is trained on this dataset. We collected 2057 images from 1131 patients as the training and validation dataset, and 100 images of the patients with accurate pathological criteria were used as the test dataset. RESULTS: The experimental results based on this dataset show that the MIoU of tumor location recognition is 0.89 and the average accuracy of benign and malignant diagnoses is 97%. The diagnosis performance of the developed diagnostic system is basically consistent with that of senior doctors and is superior to that of junior doctors. In addition, we can provide the doctor with a preliminary diagnosis so that it can be diagnosed quickly. CONCLUSION: Our proposed method can effectively improve diagnostic speed and the early screening rate of breast cancer. The system provides a valuable aid for the ultrasonic diagnosis of breast cancer.

Research Progress of α-Glucosidase Inhibitors Produced by Microorganisms and Their Applications.

Based on the easy cultivation of microorganisms and their short cycle time, research on α-glucosidase inhibitors (α-GIs) of microbial origin is receiving extensive attention. Raw materials used in food production, such as cereals, dairy products, fruits, and vegetables, contain various bioactive components, like flavonoids, polyphenols, and alkaloids. Fermentation with specific bacterial strains enhances the nutritional value of these raw materials and enables the creation of hypoglycemic products rich in diverse active ingredients. Additionally, conventional food processing often results in significant byproduct generation, causing resource wastage and environmental issues. However, using bacterial strains to ferment these byproducts into α-GIs presents an innovative solution. This review describes the microbial-derived α-GIs that have been identified. Moreover, the production of α-GIs using industrial food raw materials and processing byproducts as a medium in fermentation is summarized. It is worth analyzing the selection of strains and raw materials, the separation and identification of key compounds, and fermentation broth research methods. Notably, the innovative ideas in this field are described as well. This review will provide theoretical guidance for the development of microbial-derived hypoglycemic foods.

Development of an MCL-1-related prognostic signature and inhibitors screening for glioblastoma.

Introduction: The effect of the conventional treatment methods of glioblastoma (GBM) is poor and the prognosis of patients is poor. The expression of MCL-1 in GBM is significantly increased, which shows a high application value in targeted therapy. In this study, we predicted the prognosis of glioblastoma patients, and therefore constructed MCL-1 related prognostic signature (MPS) and the development of MCL-1 small molecule inhibitors. Methods: In this study, RNA-seq and clinical data of 168 GBM samples were obtained from the TCGA website, and immunological analysis, differential gene expression analysis and functional enrichment analysis were performed. Subsequently, MCL-1-associated prognostic signature (MPS) was constructed and validated by LASSO Cox analysis, and a nomogram was constructed to predict the prognosis of patients. Finally, the 17931 small molecules downloaded from the ZINC15 database were screened by LibDock, ADME, TOPKAT and CDOCKER modules and molecular dynamics simulation in Discovery Studio2019 software, and two safer and more effective small molecule inhibitors were finally selected. Results: Immunological analysis showed immunosuppression in the MCL1_H group, and treatment with immune checkpoint inhibitors had a positive effect. Differential expression gene analysis identified 449 differentially expressed genes. Build and validate MPS using LASSO Cox analysis. Use the TSHR HIST3H2A, ARGE OSMR, ARHGEF25 build risk score, proved that low risk group of patients prognosis is better. Univariate and multivariate analysis proved that risk could be used as an independent predictor of patient prognosis. Construct a nomogram to predict the survival probability of patients at 1,2,3 years. Using a series of computer-aided techniques, two more reasonable lead compounds ZINC000013374322 and ZINC000001090002 were virtually selected. These compounds have potential inhibitory effects on MCL-1 and provide a basis for the design and further development of MCL-1 specific small molecule inhibitors. Discussion: This study analyzed the effect of MCL-1 on the prognosis of glioblastoma patients from the perspective of immunology, constructed a new prognostic model to evaluate the survival rate of patients, and further screened 2 MCL-1 small molecule inhibitors, which provides new ideas for the treatment and prognosis of glioblastoma.

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.

Inflammatory factors and risk of meningiomas: a bidirectional mendelian-randomization study.

Background: Meningiomas are one of the most common intracranial tumors, and the current understanding of meningioma pathology is still incomplete. Inflammatory factors play an important role in the pathophysiology of meningioma, but the causal relationship between inflammatory factors and meningioma is still unclear. Method: Mendelian randomization (MR) is an effective statistical method for reducing bias based on whole genome sequencing data. It's a simple but powerful framework, that uses genetics to study aspects of human biology. Modern methods of MR make the process more robust by exploiting the many genetic variants that may exist for a given hypothesis. In this paper, MR is applied to understand the causal relationship between exposure and disease outcome. Results: This research presents a comprehensive MR study to study the association of genetic inflammatory cytokines with meningioma. Based on the results of our MR analysis, which examines 41 cytokines in the largest GWAS datasets available, we were able to draw the relatively more reliable conclusion that elevated levels of circulating TNF-ß, CXCL1, and lower levels of IL-9 were suggestive associated with a higher risk of meningioma. Moreover, Meningiomas could cause lower levels of interleukin-16 and higher levels of CXCL10 in the blood. Conclusion: These findings suggest that TNF-ß, CXCL1, and IL-9 play an important role in the development of meningiomas. Meningiomas also affect the expression of cytokines such as IL-16 and CXCL10. Further studies are needed to determine whether these biomarkers can be used to prevent or treat meningiomas.

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.

Histopathological bladder cancer gene mutation prediction with hierarchical deep multiple-instance learning.

Gene mutation detection is usually carried out by molecular biological methods, which is expensive and has a long-time cycle. In contrast, pathological images are ubiquitous. If clinically significant gene mutations can be predicted only through pathological images, it will greatly promote the widespread use of gene mutation detection in clinical practice. However, current gene mutation prediction methods based on pathological images are ineffective because of the inability to identify mutated regions in gigapixel Whole Slide Image (WSI). To address this challenge, hereby we propose a carefully designed framework for WSI-based gene mutation prediction, which consists of three parts. (i) The first part of cancerous area segmentation, based on supervised learning, quickly filters out a large number of non-mutated regions; (ii) the second part of cancerous patch clustering, based on the representations derived from contrastive learning, ensures the comprehensiveness of patch selection; and (iii) the third part of mutation classification, based on the proposed hierarchical deep multi-instance learning method (HDMIL), ensures that sufficient patches are considered and inaccurate selections are ignored. In addition, benefiting from a two-stage attention mechanism in HDMIL, the patches that are highly correlated with gene mutations can be identified. This interpretability can help a pathologist to analyze the correlation between gene mutation and histopathological morphology. Experimental results demonstrate that the proposed gene mutation prediction framework significantly outperforms the state-of-the-art methods. In the TCGA bladder cancer dataset, five clinically relevant gene mutations are well predicted.