Abstract：
BACKGROUND: The gene signatures have been considered as a promising early diagnosis and prognostic analysis to identify disease subtypes and to determine subsequent treatments. Tissue-specific gene signatures of a specific disease are an emergency requirement for precision medicine to improve the accuracy and reduce the side effects. Currently, many approaches have been proposed for identifying gene signatures for diagnosis and prognostic. However, they often lack of tissue-specific gene signatures.
RESULTS: Here, we propose a new method, consensus mutual information (CoMI) for analyzing omics data and discovering gene signatures. CoMI can identify differentially expressed genes in multiple cancer omics data for reflecting both cancer-related and tissue-specific signatures, such as Cell growth and death in multiple cancers, Xenobiotics biodegradation and metabolism in LIHC, and Nervous system in GBM. Our method identified 50-gene signatures effectively distinguishing the GBM patients into high- and low-risk groups (log-rank p = 0.006) for diagnosis and prognosis.
CONCLUSIONS: Our results demonstrate that CoMI can identify significant and consistent gene signatures with tissue-specific properties and can predict clinical outcomes for interested diseases. We believe that CoMI is useful for analyzing omics data and discovering gene signatures of diseases.
RESULTS: Here, we propose a new method, consensus mutual information (CoMI) for analyzing omics data and discovering gene signatures. CoMI can identify differentially expressed genes in multiple cancer omics data for reflecting both cancer-related and tissue-specific signatures, such as Cell growth and death in multiple cancers, Xenobiotics biodegradation and metabolism in LIHC, and Nervous system in GBM. Our method identified 50-gene signatures effectively distinguishing the GBM patients into high- and low-risk groups (log-rank p = 0.006) for diagnosis and prognosis.
CONCLUSIONS: Our results demonstrate that CoMI can identify significant and consistent gene signatures with tissue-specific properties and can predict clinical outcomes for interested diseases. We believe that CoMI is useful for analyzing omics data and discovering gene signatures of diseases.
摘要：
背景：基因特征被认为是一种有希望的早期诊断和预后分析，以鉴定疾病亚型并确定后续治疗。特定疾病的组织特异性基因特征是精准医学的紧急要求，以提高准确性并减少副作用。目前,已经提出了许多方法来鉴定用于诊断和预后的基因特征.然而,它们通常缺乏组织特异性基因特征。
结果：这里，我们提出了一种新的方法，用于分析组学数据和发现基因特征的共识互信息(CoMI)。CoMI可以识别多个癌症组学数据中的差异表达基因，以反映癌症相关和组织特异性特征。如多种癌症的细胞生长和死亡，LIHC中的异种生物的生物降解和代谢，和GBM中的神经系统。我们的方法鉴定了50个基因特征，有效地将GBM患者分为高危组和低危组（log-rankp=0.006）进行诊断和预后。
结论：我们的结果表明，CoMI可以识别具有组织特异性的显著且一致的基因特征，并可以预测感兴趣的疾病的临床结果。我们相信CoMI对于分析组学数据和发现疾病的基因特征很有用。
结果：这里，我们提出了一种新的方法，用于分析组学数据和发现基因特征的共识互信息(CoMI)。CoMI可以识别多个癌症组学数据中的差异表达基因，以反映癌症相关和组织特异性特征。如多种癌症的细胞生长和死亡，LIHC中的异种生物的生物降解和代谢，和GBM中的神经系统。我们的方法鉴定了50个基因特征，有效地将GBM患者分为高危组和低危组（log-rankp=0.006）进行诊断和预后。
结论：我们的结果表明，CoMI可以识别具有组织特异性的显著且一致的基因特征，并可以预测感兴趣的疾病的临床结果。我们相信CoMI对于分析组学数据和发现疾病的基因特征很有用。
