【佳学基因检测】生物标志物鉴定的基因组方法及其近期应用

基因检测有必要做吗—解答

研讨《肿瘤靶向药物的敏感性及有效性》《Cancer Biomark》在 2006;2(3-4):103-33发表了一篇题目为《生物标志物鉴定的基因组方法及其近期应用》肿瘤靶向药物治疗基因检测临床研究文章。该研究由Yudong D He 等完成。促进了肿瘤的精准治疗与个性化用药的发展，进一步强调了基因信息检测与分析的重要性。

肿瘤靶向药物及精准治疗临床研究内容关键词：

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肿瘤靶向治疗基因检测临床应用结果

本文讨论了使用最先进的基因组方法在生物标志物识别和验证中分析方法和应用的最新发展中的选定活动、问题和挑战。通过基因组学、蛋白质组学和代谢组学进行分子分析为研究疾病状态和生物系统打开了新的窗口。它还为临床研究以及药物发现和开发中的新应用提供了令人兴奋的机会。在过去的几年里，我们见证了巨大的进步，特别是 mRNA 或转录组学的基因表达谱。在简要回顾了使用微阵列进行基因表达谱分析的技术进步后，我主要讨论了基因组方法在两种主要类型的应用中用于生物标志物识别和验证的最新进展。第一种类型涉及基于肿瘤基因表达谱的癌症诊断和预后示例，而第二种类型涉及药物发现和开发中的生物标志物应用。重点将放在近年来开发的分析方法和算法上，这些方法和算法通过利用源自微阵列的全基因组表达谱来促进生物标志物的发现和应用。还讨论了与生物标志物发现和应用相关的实验设计、数据处理、错误建模、质量控制、性能评估的品质因数以及荟萃分析中的技术问题。在结束语之前介绍了基于肿瘤表达模式的乳腺癌患者疾病预后预后的案例研究。

肿瘤发生与复发转移国际数据库描述：

This paper discusses selected activities, issues, and challenges in recent development of analytical methods and applications in biomarker identification and validation using state-of-the-art genomic approaches. Molecular profiling via genomics, proteomics, and metabonomics has opened new windows to study disease states and biological systems. It has also provided exciting opportunities for novel applications in clinical research as well as in drug discovery and development. In the past several years, we have witnessed enormous progress resulting particularly from gene expression profiling of mRNA or transcriptomics. After a brief review on technology advances in gene expression profiling using microarrays, I mainly discuss recent developments of the genomic approaches to biomarker identification and validation in two major types of applications. The first type involves examples in cancer diagnostics and prognostics based on tumor gene expression profiling, whereas the second type involves biomarker applications in drug discovery and development. The focus will be on analytical methods and algorithms that have been developed in recent years facilitating biomarker discovery and application by leveraging genome-wide expression profiles derived from microarrays. Technical issues in experimental design, data processing, error modeling, quality control, figures of merit for performance evaluation, and meta-analysis related to biomarker discovery and application are also discussed. A case study of disease outcome prognosis for breast cancer patients based on tumor expression pattern is presented before closing remarks.