研究生教育是培养高素质人才和创新能力的关键环节,在当前教育改革的大背景下,我们积极践行“以学生为中心”的教学理念,致力于构建符合学生需求和发展规律的研究生系统生物学和生物信息学课程体系,通过实施教学内容模块化、整合优质在线资源、思政教育融入、采用多种教学方法和教学手段、构建多元化评价体系以及加强师资队伍建设等一系列改革措施,增强课程的系统性与实践性,为相关课程的建设和改革提供参考。Graduate education plays a crucial role in nurturing high-quality talent and fostering innovative capabilities. We actively implement a “student-centered” teaching philosophy aimed at developing a curriculum in graduate Systems Biomedicine and Bioinformatics that aligns with students’ needs and developmental principles. This paper focuses on the construction and reform of graduate courses in Systems Biomedicine and Bioinformatics. It outlines a series of reform measures that include the implementation of modular teaching content, the integration of high-quality online resources, the incorporation of ideological and political education, and the adoption of diverse teaching methods and strategies. Additionally, it emphasizes the establishment of a comprehensive evaluation system and the strengthening of faculty expertise. Through these initiatives, the approach aims to enhance the systematic and practical aspects of the courses, ultimately providing valuable insights for the development and reform of related curricula.
目的:本研究旨在利用单细胞转录组测序(scRNA-seq)分析HER2+乳腺癌(PT组)与正常乳腺组织(NM组)之间的细胞组成及细胞通讯差异,以探讨乳腺癌微环境的变化及其潜在的分子机制。方法:数据来源于Gene Expression Omnibus (GEO)数据库的GSE16...
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目的:本研究旨在利用单细胞转录组测序(scRNA-seq)分析HER2+乳腺癌(PT组)与正常乳腺组织(NM组)之间的细胞组成及细胞通讯差异,以探讨乳腺癌微环境的变化及其潜在的分子机制。方法:数据来源于Gene Expression Omnibus (GEO)数据库的GSE161529数据集,共包含3例HER2+乳腺癌患者(PT组)和3例健康乳腺组织样本(NM组)。本研究利用Seurat进行单细胞数据分析,基于差异表达基因(DEGs)进行Gene Ontology (GO)功能富集分析,并采用CellChat解析细胞–细胞相互作用网络,以识别HER2+乳腺癌微环境中的关键信号通路。结果:通过UMAP聚类分析,共鉴定出8种主要细胞类型。与NM组相比,PT组中B细胞、T/NK细胞、上皮细胞和髓系细胞的比例显著升高,而内皮细胞、成纤维细胞和嗜碱性粒细胞比例下降,肥大细胞无显著变化。差异基因分析显示,PT组显著上调的基因富集于免疫应答、细胞增殖及代谢重编程相关通路,而下调基因主要涉及核糖体生物合成、RNA代谢及蛋白翻译等过程。此外,CellChat分析揭示,PT组的细胞通讯显著增强,特别是FN1、MIF和APP信号通路的活跃,涉及细胞外基质重塑、炎症调控及免疫逃逸。结论:HER2+乳腺癌组织的细胞通讯模式发生了显著变化,肿瘤微环境的重塑可能通过增强特定信号通路促进癌症进展。Objective: This study aims to analyze the differences in cellular composition and cell-cell communication between HER2+ breast cancer (PT group) and normal breast tissue (NM group) using single-cell RNA sequencing (scRNA-seq), to explore microenvironmental alterations and their potential molecular mechanisms. Methods: The dataset GSE161529 was obtained from the Gene Expression Omnibus (GEO) database, including three HER2+ breast cancer samples (PT group) and three healthy breast tissue samples (NM group). Single-cell data analysis was performed using the Seurat package, differentially expressed genes (DEGs) were subjected to Gene Ontology (GO) functional enrichment analysis, and CellChat was used to construct the cell-cell interaction network to identify key signaling pathways in the HER2+ breast cancer microenvironment. Results: UMAP clustering analysis identified eight major cell types. Compared to the NM group, the PT group exhibited a significant increase in B cells, T/NK cells, epithelial cells, and myeloid cells, while endothelial cells, fibroblasts, and basophils were decreased, with mast cells showing no significant changes. Differential gene expression analysis revealed that upregulated genes in the PT group were enriched in immune response, cell proliferation, and metabolic reprogramming pathways, whereas downregulated genes were primarily associated with ribosome biogenesis,
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