Identification of Biomarkers and Molecular Pathways Implicated in Smoking and COVID-19 Associated Lung Cancer Using...

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Lung cancer (LC) is a major global health concern, primarily linked to smoking.
• Epidemiological data suggest smokers are more vulnerable to COVID-19.
• Understanding the interplay between smoking, COVID-19, and LC is crucial for developing targeted therapies.

Purpose of the Study:

• To investigate the molecular links between lung cancer, smoking, and COVID-19 using bioinformatics.
• To identify shared differentially expressed genes (DEGs) and key hub genes across these conditions.
• To explore potential therapeutic targets for smoking and COVID-19-associated lung cancer.

Main Methods:

• Comparative analysis of gene expression datasets for LC, smoking, and COVID-19.
• Identification of shared DEGs and construction of protein-protein interaction networks.
• Weighted Gene Co-expression Network Analysis (WGCNA), survival analysis, gene ontology, pathway analysis, and machine learning algorithms.

Main Results:

• Identified shared DEGs between LC and smoking (63 genes) and between LC and COVID-19 (13 genes).
• Discovered ten significant hub genes (SLC22A18, CHAC1, ROBO4, TEK, NOTCH4, CD24, CD34, SOX2, PITX2, GMDS) through network analysis.
• Machine learning models using these genes demonstrated high performance in predicting LC outcomes.

Conclusions:

• The study highlights specific molecular pathways and hub genes common to lung cancer, smoking, and COVID-19.
• These identified genes and pathways represent promising candidates for novel therapeutic strategies.
• Findings provide a foundation for developing targeted treatments for individuals with smoking and COVID-19-related lung cancer.