Network Properties of Cancer Prognostic Gene Signatures in the Human Protein Interactome.

Jifeng Zhang,Cheng Jiang,Chenrun Wang,Zhicheng Ji,Shoubao Yan,Weidong Tian

doi:10.3390/genes11030247

Jifeng Zhang, Cheng Jiang + Show 4 more

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https://doi.org/10.3390/genes11030247

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Abstract

Prognostic gene signatures are critical in cancer prognosis assessments and their pinpoint treatments. However, their network properties remain unclear. Here, we obtained nine prognostic gene sets including 1439 prognostic genes of different cancers from related publications. Four network centralities were used to examine the network properties of prognostic genes (PG) compared with other gene sets based on the Human Protein Reference Database (HPRD) and String networks. We also proposed three novel network measures for further investigating the network properties of prognostic gene sets (PGS) besides clustering coefficient. The results showed that PG did not occupy key positions in the human protein interaction network and were more similar to essential genes rather than cancer genes. However, PGS had significantly smaller intra-set distance (IAD) and inter-set distance (IED) in comparison with random sets (p-value < 0.001). Moreover, we also found that PGS tended to be distributed within network modules rather than between modules (p-value < 0.01), and the functional intersection of the modules enriched with PGS was closely related to cancer development and progression. Our research reveals the common network properties of cancer prognostic gene signatures in the human protein interactome. We argue that these are biologically meaningful and useful for understanding their molecular mechanism.

Highlights

Prognostic genes (PG) have many crucial clinical applications, such as accurate predictions of cancer types, stages and their survival time for cancer patients
Our study showed that prognostic genes (PG) did not possess higher network centralities than cancer gene set (CA), prognostic gene sets (PGS) had tighter network connections and closer inter-gene set distances than background, and the network modules they were in had many common functions that were closely related to cancer
Taking into account the number of gene sets and cancer types, we combined the gene sets with the smaller number of genes and got nine large prognostic gene sets (PGS), which consisted of 1439 prognostic genes (PG) after normalizing gene names and removing duplicates

Summary

Introduction

Prognostic genes (PG) have many crucial clinical applications, such as accurate predictions of cancer types (or subtypes), stages and their survival time for cancer patients. In the past 20 years, there have been tremendous efforts to investigate PG, and a large amount of prognostic gene signatures have been identified in different cancers [2,3,4,5,6,7,8,9,10]. Biological networks provide a convenient platform of complex relationship studies between biomolecules to trace genetic phenomena and disease mechanisms on a system level [12,13,14]. Network topology analysis helps to discover groups of nodes with special network characteristics in biological networks, as well as associations between groups (e.g., plant immunity [15] and human disease [16,17])

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