摘要
转录组分析已成为理解基因表达调控及肿瘤发生发展机制的重要工具。该文基于转录组分析,探讨了一种融合生物学、医学、统计学与计算机科学的综合性教学设计,旨在帮助学生在复杂的数据环境中开展科学探究,增强其跨学科能力和批判性思维。实验结果显示,肝癌组织中上调的基因主要富集于细胞周期和DNA复制相关通路,而下调的基因则富集于羧酸代谢和PPAR信号通路。基因表达数据能够有效区分正常样本与肝癌样本。PLVAP基因在肝癌组织中显著上调,且高表达的PLVAP基因与患者良好的预后相关,表明转录组分析在药靶发现及临床决策中的潜力。
[Objective]In the rapidly evolving field of biomedical research,transcriptome analysis has emerged as a pivotal tool for understanding gene expression regulation and the mechanisms underlying tumor initiation and progression.The integration of biology,medicine,statistics,and computer science has become essential for addressing the complexities of cancer biology.This paper presents a comprehensive experimental teaching design aimed at equipping students with the skills to analyze tumor transcriptome data,fostering interdisciplinary collaboration,and enhancing critical thinking.The study focuses on liver cancer(LIHC),leveraging publicly available datasets to explore gene expression patterns,identify potential biomarkers,and evaluate their clinical relevance.The goal is to prepare students for the challenges of modern biomedical research by providing hands-on experience with cutting-edge analytical tools and methodologies.[Methods]The experimental teaching design is structured around a series of analytical steps,each targeting a specific aspect of transcriptome analysis.The course is tailored for second-and third-year undergraduate students in biological sciences,biotechnology,and bioinformatics.The primary methods employed include:1)Data Acquisition and Preprocessing:Students are introduced to the Cancer Genome Atlas(TCGA)database,a comprehensive resource for cancer genomics data.They download RNA-Seq expression data and corresponding clinical information for LIHC and adjacent normal tissues.2)Differential Expression Analysis:Using the DESeq2 package in R,students perform differential expression analysis to identify genes that are significantly upregulated or downregulated in tumor tissues compared to normal tissues.Visualization tools,such as volcano plots,are used to illustrate the distribution of differentially expressed genes.3)Functional Enrichment Analysis:The ClusterProfiler package is employed to conduct Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses.This step helps students understand the biological processes and pathways associated with the differentially expressed genes,such as cell cycle regulation,DNA replication,and metabolic pathways.4)Principal Component Analysis(PCA):PCA is performed to reduce the dimensionality of the gene expression data,allowing students to visualize the separation between tumor and normal samples.This step highlights the heterogeneity of tumor tissues and the utility of dimensionality reduction techniques in data interpretation.5)Survival Analysis:Students integrate gene expression data with clinical survival information to assess the prognostic value of specific genes.Kaplan-Meier survival curves are generated,and log-rank tests are used to compare survival outcomes between groups with high and low expression of selected genes,such as PLVAP.[Results]A total of 9,039 differentially expressed genes were identified,with 7,120 genes upregulated and 1,919 genes downregulated in liver cancer tissues compared to normal tissues.Notably,the PLVAP gene was significantly upregulated in tumor tissues,consistent with its potential role in tumor angiogenesis.GO and KEGG analyses revealed that upregulated genes were predominantly enriched in pathways related to cell cycle regulation and DNA replication,while downregulated genes were associated with metabolic processes such as fatty acid degradation and PPAR signaling.These findings align with the known metabolic reprogramming in cancer cells.PCA effectively distinguished between tumor and normal samples.Survival analysis indicated that high expression of PLVAP was associated with better prognosis in liver cancer patients,suggesting its potential as a therapeutic target or prognostic marker.[Conclusions]This experimental teaching design successfully integrates multiple disciplines to provide students with a comprehensive understanding of tumor transcriptome analysis.By combining theoretical knowledge with practical data analysis,students gain hands-on experience with state-of-the-art bioinformatics tools and methodologies.The course not only enhances students'technical skills but also fosters critical thinking,interdisciplinary collaboration,and the ability to interpret complex biological data.The findings underscore the importance of transcriptome analysis in uncovering the molecular mechanisms of cancer and identifying potential therapeutic targets.As the field of cancer research continues to advance,this interdisciplinary approach will be invaluable in preparing the next generation of scientists to tackle the challenges of precision medicine and personalized cancer therapy.
作者
刘凤麟
LIU Fengin(School of Life Sciences,Peking University,Beijing 100871,China)
出处
《实验技术与管理》
北大核心
2025年第5期180-185,共6页
Experimental Technology and Management
关键词
转录组
差异表达
功能富集
主成分分析
生存分析
transcriptome
differential expression
functional enrichment
principal component analysis(PCA)
survival analysis.
