In this brief survey, various aspects of cancer complexity and how this complexity can be confronted using modern complex networks’ theory and gene expression datasets, are described. In particular, the causes and the basic features of cancer complexity, as well as the challenges it brought are underlined, while the importance of gene expression data in cancer research and in reverse engineering of gene co-expression networks is highlighted. In addition, an introduction to the corresponding theoretical and mathematical framework of graph theory and complex networks is provided. The basics of network reconstruction along with the limitations of gene network inference, the enrichment and survival analysis, evolution, robustness-resilience and cascades in complex networks, are described. Finally, an indicative and suggestive example of a cancer gene co-expression network inference and analysis is given.

在这项简短的调查中，描述了癌症复杂性的各个方面以及如何使用现代复杂网络的理论和基因表达数据集应对这种复杂性。特别强调了癌症复杂性的原因和基本特征，以及它带来的挑战，同时强调了基因表达数据在癌症研究和基因共表达网络的逆向工程中的重要性。此外，还介绍了图论和复杂网络的相应理论和数学框架。描述了网络重建的基础知识，以及基因网络推断的局限性，富集和生存分析，进化，鲁棒性-复原力以及复杂网络中的级联。最后，