高通量技术的快速发展,特别是下一代测序(NGS),通过研究遗传变异如SNP,彻底改变了癌症研究,拷贝数变化,基因表达,和蛋白质水平。这些技术提升了精准肿瘤学的重要性,创造了对生物标志物识别和验证的需求。这篇综述探讨了肿瘤学的复杂相互作用,癌症生物学,和生物信息学工具,强调统计学习中的挑战,实验验证,数据处理,和质量控制支撑着这个变革领域。本文概述了生物信息学工具在癌症基因组学研究中的方法和应用。包含数据结构工具,途径分析,网络分析,用于分析生物标志物签名的工具,体细胞变体解释,基因组数据分析,和可视化工具。开源工具和存储库,如癌症基因组图谱(TCGA),基因组数据共用空间(GDC),cBioPortal,UCSC基因组浏览器,ArrayExpress,和基因表达综合(GEO)已经出现,以简化癌症组学数据分析。生物信息学对癌症研究产生了重大影响,发现新的生物标志物,驱动突变,致癌途径,和治疗目标。整合多组数据,网络分析,和晚期ML将是未来生物标志物发现和患者预后预测的关键。
The rapid advancement of high-throughput technologies, particularly next-generation sequencing (NGS), has revolutionized cancer research by enabling the investigation of genetic variations such as SNPs, copy number variations, gene expression, and protein levels. These technologies have elevated the significance of precision oncology, creating a demand for biomarker identification and validation. This
review explores the complex interplay of oncology, cancer biology, and bioinformatics tools, highlighting the challenges in statistical learning, experimental validation, data processing, and quality control that underpin this transformative field. This
review outlines the methodologies and applications of bioinformatics tools in cancer genomics research, encompassing tools for data structuring, pathway analysis, network analysis, tools for analyzing biomarker signatures, somatic variant interpretation, genomic data analysis, and visualization tools. Open-source tools and repositories like The Cancer Genome Atlas (TCGA), Genomic Data Commons (GDC), cBioPortal, UCSC Genome Browser, Array Express, and Gene Expression Omnibus (GEO) have emerged to streamline cancer omics data analysis. Bioinformatics has significantly impacted cancer research, uncovering novel biomarkers, driver mutations, oncogenic pathways, and therapeutic targets. Integrating multi-omics data, network analysis, and advanced ML will be pivotal in future biomarker discovery and patient prognosis prediction.