期刊
CELLS
卷 10, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/cells10020402
关键词
non-small-cell lung cancer; protein-protein interactions; random walk with restart; functional modules; signaling transduction
类别
资金
- National Natural Science Foundation of China [31872723]
- Project of State Key Laboratory of Radiation Medicine and Protection, Soochow University [GZK1201902]
- Key Research and Development Program of Jiangsu Province [BE2020656]
- Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions
This study presents a network-based analysis method for identifying functional modules and candidate targets in NSCLC. By integrating structural information into the signaling network, novel protein-protein interactions with therapeutic significance are revealed, providing new mechanistic insights into the development of therapeutic targets for NSCLC.
Non-small-cell lung cancer (NSCLC) represents a heterogeneous group of malignancies that are the leading cause of cancer-related death worldwide. Although many NSCLC-related genes and pathways have been identified, there remains an urgent need to mechanistically understand how these genes and pathways drive NSCLC. Here, we propose a knowledge-guided and network-based integration method, called the node and edge Prioritization-based Community Analysis, to identify functional modules and their candidate targets in NSCLC. The protein-protein interaction network was prioritized by performing a random walk with restart algorithm based on NSCLC seed genes and the integrating edge weights, and then a community network was constructed by combining Girvan-Newman and Label Propagation algorithms. This systems biology analysis revealed that the CCNB1-mediated network in the largest community provides a modular biomarker, the second community serves as a drug regulatory module, and the two are connected by some contextual signaling motifs. Moreover, integrating structural information into the signaling network suggested novel protein-protein interactions with therapeutic significance, such as interactions between GNG11 and CXCR2, CXCL3, and PPBP. This study provides new mechanistic insights into the landscape of cellular functions in the context of modular networks and will help in developing therapeutic targets for NSCLC.
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