Research and Analysis of the Impact of Significant Genes on Lung Cancer in the Context of Global Exacerbation

Abstract

Lung cancer ranks among the most prevalent and severe cancers globally. This study investigates the correlation between specific genes and lung cancer and aims to develop a gene expression-based model for predicting lung cancer. Machine learning analysis of gene expression data from 551 individuals revealed that genes such as CLDN18, GKN2, LYVE-1, GPIHBP1, and CLIC5 play significant roles in the onset and progression of lung cancer. The mechanisms of action of these genes were analyzed to understand their potential in the diagnosis and treatment of lung cancer. Additionally, a comprehensive prediction model was constructed, achieving an accuracy of 99.2%. Genes such as CLDN18, LYVE-1, and GPIHBP1 were found to be upregulated in lung cancer tissues, suggesting their potential as therapeutic targets. A thorough review of literature concerning these key genes and their impacts on humans was also conducted to further validate the study's accuracy and practicality. This research contributes to new approaches in the early detection and treatment of lung cancer, offering valuable insights and serving as a reference for future studies.