Abstract
Combining MS-based proteomic data with network and topological features of such network would identify more clinically relevant molecules and meaningfully expand the repertoire of proteins derived from MS analysis. The integrative topological indexes representing 95.96% information of seven individual topological measures of node proteins were calculated within a protein-protein interaction (PPI) network, built using 244 differentially expressed proteins (DEPs) identified by iTRAQ 2D-LC-MS/MS. Compared with DEPs, differentially expressed genes (DEGs) and comprehensive features (CFs), structurally dominant nodes (SDNs) based on integrative topological index distribution produced comparable classification performance in three different clinical settings using five independent gene expression data sets. The signature molecules of SDN-based classifier for distinction of early from late clinical TNM stages were enriched in biological traits of protein synthesis, intracellular localization and ribosome biogenesis, which suggests that ribosome biogenesis represents a promising therapeutic target for treating ESCC. In addition, ITGB1 expression selected exclusively by integrative topological measures correlated with clinical stages and prognosis, which was further validated with two independent cohorts of ESCC samples. Thus the integrative topological analysis of PPI networks proposed in this study provides an alternative approach to identify potential biomarkers and therapeutic targets from MS/MS data with functional insights in ESCC.
Highlights
Rapid advances in proteomics allow hundreds to thousands of molecular changes being simultaneously identified during progression of disease, providing a comprehensive picture of malfunction relative to healthy state[1,2]
The degree distribution of the network is scale-free (Fig. 1B) and the power-law exponent is around −1.7770, which resembles another investigation on large-scale human protein-protein interaction (PPI) networks in reference[41]
The PPI network is small-world with very short average path length and high clustering coefficient, and the small-world SW index equals to 221.1198, which indicates the small-worldness of the network[41]
Summary
Rapid advances in proteomics allow hundreds to thousands of molecular changes being simultaneously identified during progression of disease, providing a comprehensive picture of malfunction relative to healthy state[1,2]. Differentially expressed molecules extracted from various independent studies suffering low consistency pose difficulties in subsequent clinical application[7,8,9,10] This approach can overlook biologically meaningful molecules without largest fold change such as transcription factors[4]. Combining MS-based proteomic data with network and topological features of such network could identify more clinically relevant molecules and meaningfully expand the repertoire of proteins returned via MS analysis. We identified structurally dominant nodes (SDNs) by integrative topological analysis of seven individual measures as potential molecular signatures for ESCC and determined the clinical relevance of these SDNs in comparison with DEPs and differentially expressed genes (DEGs) as well
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
