Immunogenic cell death-based prognostic model for predicting the response to immunotherapy and common therapy in lung adenocarcinoma

Abstract Lung adenocarcinoma (LUAD) is a malignant tumor in the respiratory system. The efficacy of current treatment modalities varies greatly, and individualization is evident. Therefore, finding biomarkers for predicting treatment prognosis and providing reference and guidance for formulating treatment options is urgent. Cancer immunotherapy has made distinct progress in the past decades and has a significant effect on LUAD. Immunogenic Cell Death (ICD) can reshape the tumor’s immune microenvironment, contributing to immunotherapy. Thus, exploring ICD biomarkers to construct a prognostic model might help individualized treatments. We used a lung adenocarcinoma (LUAD) dataset to identify ICD-related differentially expressed genes (DEGs). Then, these DEGs were clustered and divided into subgroups. We also performed variance analysis in different dimensions. Further, we established and validated a prognostic model by LASSO Cox regression analysis. The risk score in this model was used to evaluate prognostic differences by survival analysis. The treatment prognosis of various therapies were also predicted. LUAD samples were divided into two subgroups. The ICD-high subgroup was related to an immune-hot phenotype more sensitive to immunotherapy. The prognostic model was constructed based on six ICD-related DEGs. We found that high-risk score patients responded better to immunotherapy. The ICD prognostic model was validated as a standalone factor to evaluate the ICD subtype of individual LUAD patients, which might contribute to more effective therapies.