College of Computer Science and Technology, Jilin University, Changchun, China.
Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, USA.
Sci Rep. 2020 Jun 30;10(1):10624. doi: 10.1038/s41598-020-67476-7.
A novel method is developed for predicting the stage of a cancer tissue based on the consistency level between the co-expression patterns in the given sample and samples in a specific stage. The basis for the prediction method is that cancer samples of the same stage share common functionalities as reflected by the co-expression patterns, which are distinct from samples in the other stages. Test results reveal that our prediction results are as good or potentially better than manually annotated stages by cancer pathologists. This new co-expression-based capability enables us to study how functionalities of cancer samples change as they evolve from early to the advanced stage. New and exciting results are discovered through such functional analyses, which offer new insights about what functions tend to be lost at what stage compared to the control tissues and similarly what new functions emerge as a cancer advances. To the best of our knowledge, this new capability represents the first computational method for accurately staging a cancer sample. The R source code used in this study is available at GitHub (https://github.com/yxchspring/CECS).
一种新的方法被开发出来，用于根据给定样本和特定阶段样本之间的共表达模式一致性水平来预测癌症组织的阶段。预测方法的基础是，具有相同阶段的癌症样本具有共同的功能，这反映在共表达模式中，这些模式与其他阶段的样本不同。测试结果表明，我们的预测结果与癌症病理学家手动标注的阶段一样好，甚至可能更好。这种新的基于共表达的能力使我们能够研究癌症样本的功能如何随着从早期到晚期的演变而发生变化。通过这种功能分析发现了新的令人兴奋的结果，这些结果提供了与对照组织相比，在哪个阶段哪些功能更容易丧失，以及随着癌症的发展哪些新功能出现的新见解。据我们所知，这种新能力代表了准确分期癌症样本的第一种计算方法。本研究中使用的 R 源代码可在 GitHub 上获得（https://github.com/yxchspring/CECS）。
