Background and aims: Radiotherapy is widely applied for lung adenocarcinoma (LUAD), while individualized differences led to different outcomes. This study aimed to establish a multi-gene risk scoring model to predict the benefits of LUAD patients from radiotherapy, based on different types of cell death respectively.
Results: Other than autophagy, pyroptosis, ferroptosis and Immunogenic cell death (ICD), the LUAD prognostic model based on apoptosis had the best performance, and the area under curves (AUCs) of the receiver operating curve (ROC) for 1-, 3-, and 5-year OS were 0.700,0.736,0.723,respectively. Such genes were involved as SLC7A5, EXO1, ABAT, NLRP1 and GAR1. Then patients were divided into high and low risk groups by the median apoptosis-LUAD risk score. For patients in the high-risk group, i.e., the radiotherapy-tolerant group, we screened adjuvant chemotherapy and found that besides the conventional first-line chemotherapy regimen, drugs such as Fludarabine, Pevonedistat, and Podophyllotoxin Bromide may also have potential therapeutic value.
Conclusion: The multi-gene risk scoring model based on apoptosis might predict the radiotherapy benefits of LUAD patients and for those radioresistant patients classified by the model we also provided effective adjuvant chemicals, which would be used to guide clinical treatment.
Keywords: Cell death; Ionizing radiation; Lung adenocarcinoma; Machine learning; Multi-gene risk model; Prognosis.
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