Shannon熵测量的信号复杂度及其在个性化医疗中的应用（表6）

数据集获取（Figshare）

• 源站标题：Table_6_Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine.xls
• DOI：10.3389/fgene.2019.00930.s007
• 发布日期：2019-10-21
• 文件数：1 个
• 文件总体积：57.34 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_6_Signaling_Complexity_Measured_by_Shannon_Entropy_and_Its_Application_in_Personalized_Medicine_xls/10008008

命令行下载

curl -L -o "Table_6_Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine.xls" "https://ndownloader.figshare.com/files/18049694"

文件清单

• Table_6_Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine.xls（57.34 KB）

文件总体积 57.34 KB（源站 API 实测）。

数据说明

Traditional approaches to cancer therapy seek common molecular targets in tumors from different patients. However, molecular profiles differ between patients, and most tumors exhibit inherent heterogeneity. Hence, imprecise targeting commonly results in side effects, reduced efficacy, and drug resistance. By contrast, personalized medicine aims to establish a molecular diagnosis specific to each patient, which is currently feasible due to the progress achieved with high-throughput technologies. In this report, we explored data from human RNA-seq and protein–protein interaction (PPI) networks using bioinformatics to investigate the relationship between tumor entropy and aggressiveness. To compare PPI subnetworks of different sizes, we calculated the Shannon entropy associated with vertex connections of differentially expressed genes comparing tumor samples with their paired control tissues. We found that the inhibition of up-regulated connectivity hubs led to a higher reduction of subnetwork entropy compared to that obtained with the inhibition of targets selected at random. Furthermore, these hubs were described to be participating in tumor processes. We also found a significant negative correlation between subnetwork entropies of tumors and the respective 5-year survival rates of the corresponding cancer types. This correlation was also observed considering patients with lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) based on the clinical data from The Cancer Genome Atlas database (TCGA). Thus, network entropy increases in parallel with tumor aggressiveness but does not correlate with PPI subnetwork size. This correlation is consistent with previous reports and allowed us to assess the number of hubs to be inhibited for therapy to be effective, in the context of precision medicine, by reference to the 100% patient survival rate 5 years after diagnosis. Large standard deviations of subnetwork entropies and variations in target numbers per patient among tumor types characterize tumor heterogeneity.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fgene.2019.00930.s007（source: https://frontiersin.figshare.com/articles/dataset/Table_6_Signaling_Complexity_Measured_by_Shannon_Entropy_and_Its_Application_in_Personalized_Medicine_xls/10008008）。