Lung cancer is one of the leading causes globally. The survival rate is relatively low because symptoms of lungadenocarcinoma frequently do not appear until the illness has spread to every part of the lung. Early disease detection can preventthe spread of cancer and reduce cancer-related mortality. Biomarkers help in early disease detection. Machine learning and neuralnetwork approaches, which use mathematical techniques to train a model to learn from data for a particular task, have been widelyused in biomarker discovery because identifying biomarkers is a time-consuming procedure. Based on the expression ofbiomarkers in the various groups, the "Pathway analysis" service evaluates the enrichment of biological processes, gene sets, andsignaling pathways. The study aims to find the expressed gene sets, enriched signaling pathways, and biomarkers for lungadenocarcinoma. The "TCGA-LUAD" project's TCGA data is used to identify biomarkers, and PCA analysis reveals that most lungadenocarcinoma patients have no history of other malignancies. Our examination of the GO biological process over-representation 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 revealsthat 447 biomarkers with differential expression and 20 GOs with P values less than 0.001 are substantially affected by molecularfunction, with a BgRatio of transporter activity of 472/6790. Additionally, GO Cellular Component Over-Representation Analysisreveals that 339 biomarkers with differential expression and 21 GOs with P values less than 1e-07, where the BgRatio of CellSurface is 495/7043, are substantially affected cellular components.

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