Identification of Biomarker in Lung Adenocarcinoma Using Machine Learning and Neural Network - Chaitali Dhande; Preenon Bagchi (International Journal of Pharma and Bio Sciences, 2024)
Lung cancer is one of the leading causes globally. The survival rate is relatively low because symptoms of lung adenocarcinoma frequently do not appear until the illness has spread to every part of the lung. Early disease detection can prevent the spread of cancer and reduce cancer-related mortality. Biomarkers help in early disease detection. Machine learning and neural network approaches, which use mathematical techniques to train a model to learn from data for a particular task, have been widely used in biomarker discovery because identifying biomarkers is a time-consuming procedure. Based on the expression of biomarkers in the various groups, the "Pathway analysis" service evaluates the enrichment of biological processes, gene sets, and signaling pathways. The study aims to find the expressed gene sets, enriched signaling pathways, and biomarkers for lung adenocarcinoma. The "TCGA-LUAD" project’s TCGA data is used to identify biomarkers, and PCA analysis reveals that most lung adenocarcinoma patients have no history of other malignancies. Our examination of the GO biological process overrepresentation reveals that 499/6818 represents the BgRatio of cytokine response, and 129 GOs have P values less than 0.0005, indicating that they are strongly affected by biological processes. The GO Molecular Function Over-Representation Analysis reveals that 447 biomarkers with differential expression and 20 GOs with P values less than 0.001 are substantially affected by molecular function, with a BgRatio of transporter activity of 472/6790. Additionally, GO Cellular Component Over-Representation Analysis reveals that 339 biomarkers with differential expression and 21 GOs with P values less than 1e-07, where the BgRatio of Cell Surface is 495/7043, are substantially affected cellular components.