【佳學基因檢測】生物標志物鑒定的基因組方法及其近期應用

基因檢測有必要做嗎—解答

研討《腫瘤靶向藥物的敏感性及有效性》《Cancer Biomark》在?2006;2(3-4):103-33發(fā)表了一篇題目為《生物標志物鑒定的基因組方法及其近期應用》腫瘤靶向藥物治療基因檢測臨床研究文章。該研究由Yudong D He?等完成。促進了腫瘤的正確治療與個性化用藥的發(fā)展，進一步強調(diào)了基因信息檢測與分析的重要性。

腫瘤靶向藥物及正確治療臨床研究內(nèi)容關鍵詞：

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腫瘤靶向治療基因檢測臨床應用結(jié)果

本文討論了使用賊先進的基因組方法在生物標志物識別和驗證中分析方法和應用的賊新發(fā)展中的選定活動、問題和挑戰(zhàn)。通過基因組學、蛋白質(zhì)組學和代謝組學進行分子分析為研究疾病狀態(tài)和生物系統(tǒng)打開了新的窗口。它還為臨床研究以及藥物發(fā)現(xiàn)和開發(fā)中的新應用提供了令人興奮的機會。在過去的幾年里，我們見證了巨大的進步，特別是 mRNA 或轉(zhuǎn)錄組學的基因表達譜。在簡要回顧了使用微陣列進行基因表達譜分析的技術進步后，我主要討論了基因組方法在兩種主要類型的應用中用于生物標志物識別和驗證的賊新進展。先進種類型涉及基于腫瘤基因表達譜的癌癥診斷和預后示例，而第二種類型涉及藥物發(fā)現(xiàn)和開發(fā)中的生物標志物應用。重點將放在近年來開發(fā)的分析方法和算法上，這些方法和算法通過利用源自微陣列的全基因組表達譜來促進生物標志物的發(fā)現(xiàn)和應用。還討論了與生物標志物發(fā)現(xiàn)和應用相關的實驗設計、數(shù)據(jù)處理、錯誤建模、質(zhì)量控制、性能評估的品質(zhì)因數(shù)以及薈萃分析中的技術問題。在結(jié)束語之前介紹了基于腫瘤表達模式的乳腺癌患者疾病預后預后的案例研究。

腫瘤發(fā)生與反復轉(zhuǎn)移國際數(shù)據(jù)庫描述：

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.