Abstract
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.
Highlights
Statistical and epidemiological data indicate that cancer is a growing global health problem
Only four patients shared the same top-10 combination, two from Breast invasive carcinoma (BRCA) and two from PRAD. This means that 99% of patients had a unique combination of top10 hubs, even if some hubs could be found conserved across a significant part of the patient population. This property can be related to the variation in the number of connections for each hub according to the patients’ subnetwork of up-regulated genes and to the variation of hubs that are up-regulated from one tumor to the other
Among the measures of node and path metrics, we focused this study on the application of Shannon entropy to subnetworks of tumors’ up-regulated genes
Summary
Statistical and epidemiological data indicate that cancer is a growing global health problem. Cancer initiation and progression involves genetic and epigenetic changes that reprogram complex regulatory circuits. Within this context, Hanahan and Weinberg (Hanahan and Weinberg, 2011) characterized 10 consensus processes, called cancer hallmarks, which are representative of oncogenesis. Chemotherapy drugs may result in harmful side effects for patients due to their low selectivity that adversely affects both tumor and normal cells (Siegel et al, 2012). The process of therapeutic target identification is complex and implies the recognition of molecular differences between tumor and healthy cells, most of them based on gene regulation. The profile of up-regulated genes in tumor tissues is used in a personalized (individualized) medicine approach. Personalized medicine is expected to bring higher benefits to patients
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