Genetic selection for lung adenocarcinoma: a statistical approach based on pathological genetic data

Abstract

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC), with high morbidity and mortality worldwide, posing a serious threat to human health. Due to the complex molecular mechanism of lung adenocarcinoma, which involves a variety of genetic and environmental factors, this makes early diagnosis and treatment a major challenge. This research aims to identify the key pathogenic genes of lung adenocarcinoma to solve the challenges in early diagnosis and treatment. In this paper, three differential analysis methods, limma, DESeq2 and edgeR, were used to select 3315 differential genes. Then, combined with Lasso regression and XGboost algorithm, the pathological gene data were analyzed in depth, and through these advanced statistical and machine learning techniques, the genes related to lung adenocarcinoma were screened from a large number of gene expression data, and 17 characteristic genes of the highest importance were found GO enrichment analysis and KEGG enrichment analysis were performed on these characteristic genes to clarify the biological functions of the genes. Finally, 8 significant pathogenic genes were identified by Cox survival analysis. The expression levels of these genes are closely related to the prognosis of lung adenocarcinoma patients, providing a new perspective for understanding the disease mechanism. The conclusions of this study not only improve the understanding of the molecular mechanism of lung adenocarcinoma, but also provide potential biomarkers for clinical diagnosis and treatment. The discovery of these genes is expected to promote the development of early diagnosis technology and guide the formulation of personalized treatment plans, thereby improving the treatment efficacy and quality of life of lung adenocarcinoma patients. In the future, these genes may become key targets for the development of new drugs and therapeutic strategies.