A TP53-Pathway-Based Prognostic Signature for Radiotherapy and Functional Validation of TP53I3 in Non-Small-Cell Lung...

Background: Radiation therapy is an important treatment method for non-small-cell lung cancer (NSCLC). However, predicting patient prognosis remains challenging due to considerable interpatient heterogeneity. The TP53 signaling pathway, implicated in tumor radiosensitivity and treatment outcomes, represents a promising predictive biomarker. Accordingly, in this study, we aimed to identify TP53-signaling pathway-related genes and develop a novel prognostic model for risk stratification for NSCLC patients undergoing radiation therapy. Methods: Publicly available NSCLC transcriptomic datasets were obtained from the GEO and TCGA databases. Utilizing bioinformatics approaches, we identified differentially expressed genes (DEGs) associated with the TP53 signaling pathway. Feature selection was performed using LASSO regression, followed by the construction of a multivariate-Cox-regression-based prognostic prediction model. In vitro validation was performed using a cell viability assay, colony formation, cell cycle analysis, apoptosis detection, γH2AX immunofluorescence staining and comet electrophoresis. In vivo validation was performed utilizing a subcutaneous tumor-bearing mouse model, where radiosensitivity was assessed by monitoring tumor volume post-irradiation. Results: We constructed a robust prognostic prediction model based on five TP53-signaling-pathway-related genes (MDM2, THBS1, TP53I3, ATM, and SESN3), achieving a 5-year AUC of 0.828 in the training set and a 3-year AUC of 0.824 in the validation set. The model exhibited a significant ability to stratify patients into distinct high- and low-risk groups, demonstrating good predictive performance. The poor prognosis observed in the high-risk group was associated with lower infiltration of anti-tumor immune cells but higher infiltration of immunosuppressive cells. Both in vitro and in vivo experiments demonstrated that TP53I3 knockdown significantly enhanced the radiosensitivity of NSCLC through increased DNA damage, cell cycle arrest and apoptosis. Conclusions: In this study, a five-gene signature derived from the TP53 signaling pathway was developed, and the model was shown to effectively predict the prognoses of NSCLC patients undergoing radiotherapy. This signature has the potential to be developed into a clinically applicable tool for personalizing radiotherapy regimens for NSCLC.