Identification of DYNLT1 associated with proliferation, relapse, and metastasis in breast cancer

BackgroundBreast cancer (BC) is the most common malignant disease worldwide. Although the survival rate is improved in recent years, the prognosis is still bleak once recurrence and metastasis occur. It is vital to investigate more efficient biomarkers for predicting the metastasis and relapse of BC. DYNLT1 has been reported that participating in the progression of multiple cancers. However, there is still a lack of study about the correlation between DYNLT1 and BC.MethodsIn this study, we evaluated and validated the expression pattern and prognostic implication of DYNLT1 in BC with multiple public cohorts and BC tumor microarrays (TMAs) of paraffin-embedded tissues collected from the Affiliated Hospital of Jining Medical University. The response biomarkers for immune therapy, such as tumor mutational burden (TMB), between different DYNLT1 expression level BC samples were investigated using data from the TCGA-BRCA cohort utilizing public online tools. In addition, colony formation and transwell assay were conducted to verify the effects of DYNLT1 in BC cell line proliferation and invasion.ResultsThe results demonstrated that DYNLT1 overexpressed in BC and predicted poor relapse-free survival in our own BC TMA cohort. In addition, DYNLT1 induced BC development by promoting MDA-MB-231 cell proliferation migration, and metastasis.ConclusionAltogether, our findings proposed that DYNLT1 could be a diagnostic and prognostic indicator in BC

Advancements of Artificial Intelligence Techniques in the Realm About Library and Information Subject—A Case Survey of Latent Dirichlet Allocation Method

To investigate the advancements of artificial intelligence techniques in the realm of library and information subject, we have chosen the Latent Dirichlet Allocation method as a case study to explore its current study status and implementations. Traditional theme mining analyses utilize methods such as word frequency statistics, co-occurrence analysis, community detection, and citation analysis to capture external quantitative features of words or documents. In contrast, the Latent Dirichlet Allocation theme modelling method employs a three-layer Bayesian structure of document-topic-word to describe the themes of documents and the semantic relationships among words, enabling a better exploration of latent semantic information in text. This method plays a pivotal role in fine-grained knowledge extraction and analysis. We systematically review more than a decade of relevant literature in the realm about library and information subject. Through content analysis, we construct an analytical architecture for the implementation of the Latent Dirichlet Allocation method. This architecture, viewed from the perspective of the implementation process of Latent Dirichlet Allocation, comprehensively summarizes the core stages and technical challenges, including text pre-processing, model construction (i.e., theme model selection and optimal theme number determination), and model solving. Additionally, we provide a comprehensive overview of the current study status of the Latent Dirichlet Allocation method across various implementation domains, such as theme exploration, knowledge organization, academic evaluation, sentiment analysis, and recommendation study. Our findings indicate that the Latent Dirichlet Allocation method has formed a mature analytical process in the realm of library and information subject, with ongoing growth in study interest

A Faboideae-Specific Floral Scent Betrays Seeds to an Important Granivore Pest

Seed predation by insect herbivores reduces crop production worldwide. Foraging on seeds at pre-dispersal generally means that females need to find the suitable host plant within a relatively short timeframe in order to synchronize larval development with seed production. The mechanistic understanding of host finding by seed pests can be harnessed for more sustainable pest management strategies. We here studied the chemical communication between the bean bug Riptortus pedestris, a major pest of legumes, and several crop species and cultivars in the Fabaceae. Via a comparative chemical analysis, we found that 1-octen-3-ol is the principal constituent of the floral scents of most species tested in the subfamily Faboideae, including soybean and faba bean. With field trapping and laboratory bioassays, including electroantennography, we further revealed that this compound can be perceived, and stimulate attraction responses, by R. pedestris nymphs and adults. The addition of 1-octen-3-ol to pheromone traps might therefore improve trapping efficacy for controlling populations of this important granivore pest on legumes

A Faboideae-Specific Floral Scent Betrays Seeds to an Important Granivore Pest

Seed predation by insect herbivores reduces crop production worldwide. Foraging on seeds at pre-dispersal generally means that females need to find the suitable host plant within a relatively short timeframe in order to synchronize larval development with seed production. The mechanistic understanding of host finding by seed pests can be harnessed for more sustainable pest management strategies. We here studied the chemical communication between the bean bug Riptortus pedestris, a major pest of legumes, and several crop species and cultivars in the Fabaceae. Via a comparative chemical analysis, we found that 1-octen-3-ol is the principal constituent of the floral scents of most species tested in the subfamily Faboideae, including soybean and faba bean. With field trapping and laboratory bioassays, including electroantennography, we further revealed that this compound can be perceived, and stimulate attraction responses, by R. pedestris nymphs and adults. The addition of 1-octen-3-ol to pheromone traps might therefore improve trapping efficacy for controlling populations of this important granivore pest on legumes

Association between GWAS-identified lung adenocarcinoma susceptibility loci and EGFR mutations in never-smoking Asian women, and comparison with findings from Western populations

To evaluate associations by EGFR mutation status for lung adenocarcinoma risk among never-smoking Asian women, we conducted a meta-analysis of 11 loci previously identified in genome-wide association studies (GWAS). Genotyping in an additional 10,780 never-smoking cases and 10,938 never-smoking controls from Asia confirmed associations with eight known single nucleotide polymorphisms (SNPs). Two new signals were observed at genome-wide significance (P < 5 × 10-8), namely, rs7216064 (17q24.3, BPTF), for overall lung adenocarcinoma risk, and rs3817963 (6p21.3, BTNL2) which is specific to cases with EGFR mutations. In further sub-analyses by EGFR status, rs9387478 (ROS1/DCBLD1) and rs2179920 (HLA-DPB1) showed stronger estimated associations in EGFR-positive compared to EGFR-negative cases. Comparison of the overall associations with published results in Western populations revealed that the majority of these findings were distinct, underscoring the importance of distinct contributing factors for smoking and non-smoking lung cancer. Our results extend the catalogue of regions associated with lung adenocarcinoma in non-smoking Asian women and highlight the importance of how the germline could inform risk for specific tumour mutation patterns, which could have important translational implications