【佳学基因检测】基因-环境相互作用：克服方法学挑战

中国肿瘤基因检测公司排名详解

讨化肿瘤个性化药物研究路径做了备注《Novartis Found Symp》在. 2008;293:13-26; discussion 26-30, 68-70.发表了一篇题目为《基因-环境相互作用：克服方法学挑战》肿瘤靶向药物治疗基因检测临床研究文章。该研究由Rudolf Uher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：

结直肠癌,免疫疗法,乳酸评分,微环境,预后模型

肿瘤靶向治疗基因检测临床应用结果

虽然基因和环境暴露 (G x E) 的相互作用的生物效应构成人类健康障碍的因果框架的自然组成部分，但 G x E 的检测依赖于在人口水平上观察到的统计相互作用的推断。这种推论的有效性受到质疑，因为统计交互的存在与否取决于测量尺度和统计模型。此外，G x E 研究的可行性受到以下事实的威胁：统计相互作用测试需要大量样本，并且由于基因、环境暴露和病理学评估的不可靠性而大大降低了它们的能力。研究表明，可以通过整合观察数据和实验数据来解决对统计模型和缩放的担忧。基因和环境因素的明智选择应该限制多重测试。为了克服低统计能力的挑战，建议最大限度地提高测量的可靠性，在贝叶斯框架下整合先验知识，并通过使用标准化的分层报告促进跨研究的数据汇集。研究之间的一致性和差异可用于基因解码基因检测的研究方法学分析和模型规范。免疫疗法；乳酸评分；微环境；预后模型。

肿瘤发生与复发转移国际数据库描述：

While interacting biological effects of genes and environmental exposures (G x E) form a natural part of the causal framework underlying disorders of human health, the detection of G x E relies on inference from statistical interactions observed at population level. The validity of such inference has been questioned because the presence or absence of statistical interaction depends on measurement scale and statistical model. Furthermore, the feasibility of G x E research is threatened by the fact that tests of statistical interaction require large samples and their power is substantially reduced by unreliability in the assessments of genes, environmental exposures and pathology. It is demonstrated that concerns about statistical models and scaling can be addressed by integration of observational and experimental data. Judicious selection of genes and environmental factors should limit multiple testing. To overcome the challenge of low statistical power, it is suggested to maximize the reliability of measurement, integrate prior knowledge under Bayesian framework and facilitate pooling of data across studies by use of standardized stratified reporting. Consistencies and discrepancies among studies can be exploited for methodological analysis and model specification.Keywords: colorectal cancer; immunotherapy; lactate score; microenvironment; prognostic model.

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