PLoS ONE · 2019 · 25 citations · 58 references
Life SciencesBioinformaticsOncologyGene ExpressionMolecular OncologyProteomicsCancer GenomicsCancer ProteomicsComparative ProteomicsGene Co-expression AnalysisMass Spectrometry-based ProteomicsProtein-protein Interaction AnalysisComparative Cancer Gene Expression ProfilesGene Co-expression Network AnalysisGene Expression Network AnalysisLarge Cell Lung CarcinomaLarge Cell Neuroendocrine CarcinomaProtein Expression ProfilingPulmonary Neuroendocrine TumorsWeighted Gene Coexpression Network Analysis
Small-cell lung carcinoma (SCLC) and large-cell neuroendocrine lung carcinoma (LCNEC) are high-grade lung neuroendocrine tumors (NET). However, comparative protein expression within SCLC and LCNEC remains unclear. Here, protein expression profiles were obtained via mass spectrometry-based proteomic analysis. Weighted gene co-expression network analysis (WGCNA) identified co-expressed modules and hub genes. Of 34 identified modules, six were significant and selected for protein-protein interaction (PPI) network analysis and pathway enrichment. Within the six modules, the activation of cellular processes and complexes, such as alternative mRNA splicing, translation initiation, nucleosome remodeling and deacetylase (NuRD) complex, SWItch/Sucrose Non-Fermentable (SWI/SNF) superfamily-type complex, chromatin remodeling pathway, and mRNA metabolic processes, were significant to SCLC. Modules enriched in processes, including signal recognition particle (SRP)-dependent co-translational protein targeting to membrane, nuclear-transcribed mRNA catabolic process of nonsense-mediated decay (NMD), and cellular macromolecule catabolic process, were characteristically activated in LCNEC. Novel high-degree hub genes were identified for each module. Master and upstream regulators were predicted via causal network analysis. This study provides an understanding of the molecular differences in tumorigenesis and malignancy between SCLC and LCNEC and may help identify potential therapeutic targets.
58
WGCNA: an R package for weighted correlation network analysis
Peter Langfelder, Steve Horvath · BMC Bioinformatics · 2008 · 29.1K citations · Full text
KEGG: new perspectives on genomes, pathways, diseases and drugs
Minoru Kanehisa, Miho Furumichi, Mao Tanabe et al. · Nucleic Acids Research · 2016 · 9.4K citations · Full text
cytoHubba: identifying hub objects and sub-networks from complex interactome
Chia-Hao Chin, Shu-Hwa Chen, Hsin-Hung Wu et al. · BMC Systems Biology · 2014 · 6.8K citations · Full text
Causal analysis approaches in Ingenuity Pathway Analysis
A. Krämer, Jeff Green, Jack Pollard et al. · Bioinformatics · 2013 · 6.1K citations · Full text