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A novel method for finding non-small cell lung cancer diagnosis biomarkers

机译：寻找非小细胞肺癌诊断生物标志物的新方法

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Background One of the most common causes of worldwide cancer premature death is non-small cell lung carcinoma (NSCLC) with a very low survival rate of 8%-15%. Since patients with an early stage diagnosis can have up to four times the survival rate, discovering cost-effective biological markers that can be used to improve the diagnosis and prognosis of the disease is an important clinical challenge. In the last few years, significant progress has been made to address this challenge with identified biomarkers ranging from 5-gene signatures to 133-gene signatures. However, A typical molecular sub-classification method for lung carcinomas would have a low predictive accuracy of 68%-71% because datasets of gene-expression profiles typically have tens of thousands of genes for just few hundreds of patients. This type of datasets create many technical challenges impacting the accuracy of the diagnostic prediction. Results We discovered that a small set of nine gene-signatures (JAG1, MET, CDH5, ABCC3, DSP, ABCD3, PECAM1, MAPRE2 and PDF5) from the dataset of 12,600 gene-expression profiles of NSCLC acts like an inference basis for NSCLC lung carcinoma and hence can be used as genetic markers. This very small and previously unknown set of biological markers gives an almost perfect predictive accuracy (99.75%) for the diagnosis of the disease the sub-type of cancer . Furthermore, we present a novel method that finds genetic markers for sub-classification of NSCLC. We use generalized Lorenz curves and Gini ratios to overcome many challenges arose from datasets of gene-expression profiles. Our method discovers novel genetic changes that occur in lung tumors using gene-expression profiles. Conclusions While proteins encoded by some of these gene-signatures (e.g., JAG1 and MAPRE2) have been showed to involve in the signal transduction of cells and proliferation control of normal cells, specific functions of proteins encoded by other gene-signatures have not yet been determined. Hence, this work opens new questions for structural and molecular biologists about the role of these gene-signatures for the disease.
机译：背景技术世界范围内的癌症过早死亡的最常见原因之一是非小细胞肺癌（NSCLC），其存活率非常低，仅为8％-15％。由于具有早期诊断的患者可以拥有高达四倍的生存率，因此寻找可用于改善疾病诊断和预后的具有成本效益的生物标记物是一项重要的临床挑战。在过去的几年中，通过从5个基因的特征到133个基因的特征识别的生物标志物，在应对这一挑战方面取得了重大进展。但是，针对肺癌的典型分子亚分类方法的预测准确性较低，为68％-71％，因为基因表达谱的数据集通常只有数百名患者拥有数万个基因。这种类型的数据集会产生许多技术挑战，从而影响诊断预测的准确性。结果我们从12,600个NSCLC基因表达谱的数据集中发现了一小部分九个基因签名（JAG1，MET，CDH5，ABCC3，DSP，ABCD3，PECAM1，MAPRE2和PDF5）充当NSCLC肺的推断基础癌，因此可以用作遗传标记。这套极小且以前未知的生物标记物为癌症亚型疾病的诊断提供了近乎完美的预测准确性（99.75％）。此外，我们提出了一种新的方法，该方法可以找到用于NSCLC子分类的遗传标记。我们使用广义的洛伦兹曲线和基尼比来克服基因表达谱数据集引起的许多挑战。我们的方法使用基因表达谱发现了发生在肺部肿瘤中的新型遗传变化。结论虽然已证明由其中一些基因签名编码的蛋白质（例如JAG1和MAPRE2）参与细胞的信号转导和正常细胞的增殖控制，但尚未发现由其他基因签名编码的蛋白质的特定功能。决心。因此，这项工作为结构和分子生物学家提出了有关这些基因签名在疾病中的作用的新问题。

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