lung

lung performs joint transcriptomic analysis to identify shared differentially expressed genes, transcription factors, enriched pathways, coexpression network patterns (CCPs), and survival-associated hub genes across lung cancer and other pulmonary disease microarray datasets.

Key Features:

• Identification of Deregulated Genes: Identifies differentially expressed genes (DEGs) common across ten microarray datasets (six lung cancer, four other lung diseases).
• Transcriptional Regulator Analysis: Pinpoints transcription factors (TFs) coexpressed with DEGs during lung cancer establishment, including TFs potentially involved in adaptive responses to environmental stressors such as shear stress.
• Gene Enrichment and Pathway Analysis: Performs gene enrichment and pathway analysis using DAVID to link identified genes to tumoral processes and signaling pathways.
• Coexpression Network Construction: Constructs coexpression networks with the Coexnet library integrating DEGs and TFs to identify common connectivity patterns (CCPs) between lung cancer and other pulmonary diseases.
• Survival Analysis: Conducts survival analysis of hub genes within networks to assess correlations with patient outcomes in lung cancer.

Scientific Applications:

• Understanding Comorbidity: Reveals shared genetic and transcriptional features between lung cancer and chronic pulmonary diseases to investigate mechanisms underlying observed comorbid associations.
• Early Detection and Prognosis: Identifies genes correlated with patient survival that can inform biomarker discovery for early detection and prognostic assessment in lung cancer.
• Targeted Therapeutics: Characterizes common pathways and transcriptional regulators to inform development of targeted therapies addressing lung cancer and comorbid pulmonary conditions.

Methodology:

The workflow uses R language libraries and begins with selection of relevant microarray datasets, identification of DEGs and hub TFs via computational tools, gene enrichment analysis with DAVID, coexpression network construction with Coexnet to identify CCPs, and survival analysis of hub genes.