Abstract
Lung adenocarcinoma is one of the major contributors to cancer-related mortality, with immunotherapy emerging as a key treatment. However, many patients exhibit resistance to immune checkpoint inhibitors. Cellular senescence has been linked to tumor progression and drug resistance, influencing the tumor microenvironment. This study applied consensus clustering to classify lung adenocarcinoma patients into two clusters based on senescence-related gene expression, revealing differing immune characteristics. One of the identified clusters exhibited immunosuppressive characteristics and showed resistance to immunotherapy. A senescence-related risk score was developed using machine learning to predict immunotherapy response and prognosis. High senescence-related risk score correlated with poorer survival and increased immunotherapy resistance across multiple cancer types. The senescence-related risk score model showed robust predictive ability in both the training and validation cohorts. These findings suggest a link between senescence and immunotherapy resistance, and further investigation into their relationship could reveal new perspectives for cancer treatment.