Recognition and combination of multiple cell-death features showed good predictive value in lung adenocarcinoma.

Background: Cell death is a key regulatory process in organisms and its study has become increasingly important in the field of cancer. While prior research has primarily centered on the individual pathways of cell death in cancer, there has been a lack of comprehensive investigation into the synergistic effects of multiple cell death pathways. Methods: Genes related to autophagy, apoptosis, necroptosis, pyroptosis, and cuproptosis was selected, and patients' data was collected from The Cancer Genome Atlas (TCGA)project. Cell death features were identified using principal component analysis and combined to create a composite score. A scalable prediction model was then created using LASSO regression after a thorough assessment of the composite scores. The model was subsequently validated across multiple external datasets to establish its robustness and reliability. Results: The cell death features effectively represented the gene expression patterns in the samples. The composite score well predicted prognosis, clinical stage, mutation, tumor microenvironment, and immunotherapy effectiveness. The model built on composite scores accurately predicted prognosis and immunotherapy effectiveness across multiple datasets. Conclusion: Models based on multiple cell death pathways have significant predictive power for prognosis and immunotherapy effectiveness in lung adenocarcinoma. This highlights the synergistic role of multiple cell death pathways in cancer development and offers a new perspective for cancer research.