Alternative therapies in health and medicine · 2024
Identification of Genes and Key Pathways Associated with the Pathophysiology of Lung Cancer and Atrial Fibrillation.
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Yang Dongmei, Chen Yong, Yu Yonglin, Chen Xiaoju
Аннотация
BACKGROUND: Lung cancer is a malignant tumor originating from respiratory epithelial cells in the bronchi, bronchioles, and alveoli, often associated with atrial fibrillation; However, there is a lack of in-depth understanding of its genetic basis and molecular mechanisms. Our goal is to study the genes and signaling networks associated with cancer and atrial fibrillation.
MATERIALS AND METHODS: We obtained microarray datasets for lung tumors from the Gene Expression Omnibus (GEO) database and AF for this investigation: GSE30219, GSE79768, and screened the candidate specimens in both microarrays for differential genes at P < .05 using GEO2R. The outcomes were also examined using the Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Gene and Gene Combinations (KEGG) pathway analysis methods. Using STRING and Cytoscape, protein interaction networks (PPI) were analyzed and visualized. The Molecular Complex Detection (MOCDE) plugin is responsible for filtering important compounds. Candidate genes are then screened by the cytoHubba plugin according to MCC criteria. After taking the intersection of the Hub genes by the Wayne diagram, the ROC curves were plotted separately by combining the data from one lung cancer dataset GSE19804, two AF datasets GSE41177/GSE14975 in the GEO database.
RESULTS: An aggregate of 49 co-expressed differentially expressed genes (co-DEGs) were discovered in lung cancer/AF and healthy controls. Most co-DEGs were found in neutrophil activation, where they were linked to immunological response and interactions between cytokines and cytokine receptors, according to GO and KEGG pathway analyses. Furthermore, due of their significant connectedness in both the lung carcinoma and AF datasets, we chose six key genes. They are MNDA, HP, LYZ, S100A9, S100A8, and S100A12, among others.
CONCLUSIONS: The findings of this research indicate that the onset of lung cancer and AF depends on a small number of distinctive genes. We investigated the functional enrichment, differential gene expression, and PPI of DEGs in lung cancer and AF, and the results offer fresh perspectives on the discovery of prospective biomarkers and priceless therapeutic precursors in these two diseases.
MATERIALS AND METHODS: We obtained microarray datasets for lung tumors from the Gene Expression Omnibus (GEO) database and AF for this investigation: GSE30219, GSE79768, and screened the candidate specimens in both microarrays for differential genes at P < .05 using GEO2R. The outcomes were also examined using the Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Gene and Gene Combinations (KEGG) pathway analysis methods. Using STRING and Cytoscape, protein interaction networks (PPI) were analyzed and visualized. The Molecular Complex Detection (MOCDE) plugin is responsible for filtering important compounds. Candidate genes are then screened by the cytoHubba plugin according to MCC criteria. After taking the intersection of the Hub genes by the Wayne diagram, the ROC curves were plotted separately by combining the data from one lung cancer dataset GSE19804, two AF datasets GSE41177/GSE14975 in the GEO database.
RESULTS: An aggregate of 49 co-expressed differentially expressed genes (co-DEGs) were discovered in lung cancer/AF and healthy controls. Most co-DEGs were found in neutrophil activation, where they were linked to immunological response and interactions between cytokines and cytokine receptors, according to GO and KEGG pathway analyses. Furthermore, due of their significant connectedness in both the lung carcinoma and AF datasets, we chose six key genes. They are MNDA, HP, LYZ, S100A9, S100A8, and S100A12, among others.
CONCLUSIONS: The findings of this research indicate that the onset of lung cancer and AF depends on a small number of distinctive genes. We investigated the functional enrichment, differential gene expression, and PPI of DEGs in lung cancer and AF, and the results offer fresh perspectives on the discovery of prospective biomarkers and priceless therapeutic precursors in these two diseases.