CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China.
University of Chinese Academy of Sciences, Shanghai 200031, China.
J Mol Cell Biol. 2021 Dec 6;13(9):622-635. doi: 10.1093/jmcb/mjab031.
Tumor development is a process involving loss of the differentiation phenotype and acquisition of stem-like characteristics, which is driven by intracellular rewiring of signaling network. The measurement of network reprogramming and disorder would be challenging due to the complexity and heterogeneity of tumors. Here, we proposed signaling entropy (SR) to assess the degree of tumor network disorder. We calculated SR for 33 tumor types in The Cancer Genome Atlas database based on transcriptomic and proteomic data. The SR of tumors was significantly higher than that of normal samples and was highly correlated with cell stemness, cancer type, tumor grade, and metastasis. We further demonstrated the sensitivity and accuracy of using local SR in prognosis prediction and drug response evaluation. Overall, SR could reveal cancer network disorders related to tumor malignant potency, clinical prognosis, and drug response.
肿瘤的发生发展是一个涉及分化表型丧失和获得干细胞样特征的过程，这一过程是由细胞内信号网络的重排所驱动的。由于肿瘤的复杂性和异质性，对网络重编程和紊乱的测量具有挑战性。在这里，我们提出信号熵（SR）来评估肿瘤网络紊乱的程度。我们基于转录组和蛋白质组数据，计算了来自癌症基因组图谱数据库的 33 种肿瘤类型的 SR。肿瘤的 SR 明显高于正常样本，并且与细胞干性、癌症类型、肿瘤分级和转移高度相关。我们进一步证明了使用局部 SR 进行预后预测和药物反应评估的敏感性和准确性。总的来说，SR 可以揭示与肿瘤恶性潜能、临床预后和药物反应相关的癌症网络紊乱。
