Knowledge-Guided “Community Network” Analysis Reveals the Functional Modules and Candidate Targets in Non-Small-Cell Lung Cancer

Abstract

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.

The Intriguing Thyroid Hormones–Lung Cancer Association as Exemplification of the Thyroid Hormones–Cancer Association: Three Decades of Evolving Research

“…The treatment of lung cancer is based on three pillars: surgical resection, systemic therapy, and radiation therapy. The optimal decision-making is tailored based on disease stage according to the American Joint Committee on Cancer (AJCC) TNM staging system integrated with patient-related and tumor-related characteristics [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ].…”

“…The emerging paradigm shift in cancer treatment from targeting single molecular defects to targeting complex molecular interactions [ 16 ], leveraging the proteomic landscape of tumors [ 22 ], rationalizes capitalizing on the interrelationship of TH signaling with pivotal molecular pathways implicated in lung cancer. Deciphering the TH–lung cancer association is anticipated to refine the management of lung cancer.…”

Identifying Lymph Node Metastasis-related Factors in Breast Cancer using Differential Modular and Mutational Structural Analysis

“…The topological-functional connection (TFC) was used for the prioritization of PPIs. The TFC is the integration of the edge betweenness and the gene ontology (GO) semantic similarity,[35] as a new edge measure: where N represents the number of interactions, and T n and F n represent the edge betweenness and GO semantic similarity, respectively, of interaction n . Thus, the TFC score is proposed to identify key protein interactions by integrating network topology and biological characteristics.…”

“…The TFC is the integration of the edge betweenness and the gene ontology (GO) semantic similarity, [35] as a new edge measure:…”

DM-MOGA: a multi-objective optimization genetic algorithm for identifying disease modules of non-small cell lung cancer

“…Although this method can identify disease modules, the coverage of disease-related genes may be low. The node and edge Prioritization-based Community Analysis is a knowledge-guided and network-based integration method to reveal functional modules in non-small cell lung cancer [ 6 ]. The protein–protein interaction network is prioritized by a random walk algorithm based on NSCLC seed genes and integrating edge weights, and then a "community network" is constructed in combination with Girvan-Newman and Label Propagation algorithms.…”