Transcriptomic Signatures in TP53 Positive and Negative Tumor Samples in NSCLC

Introduction: Lung cancer, specifically non-small cell lung cancer (NSCLC), is a leading cause of cancer-related mortality worldwide. TP53, a crucial tumor suppressor gene, is often mutated in various cancers, including lung cancer. This study focuses on the differences in transcriptomic profiles between TP53-mutated (TP53+) and TP53-wildtype (TP53-) NSCLC tumor samples, aiming to develop a gene signature that can predict overall survival and immune response, particularly in the context of immunotherapy. It aims to identify differentially expressed genes (DEGs) associated with TP53 status in non-small cell lung cancer and develop a gene signature that can predict overall survival and immune response. Method: Gene expression profiles from TP53-positive and TP53-negative NSCLC tumor samples were analyzed. Data were sourced from the GEO database (GSE8569, n = 69) and the TCGA database (n = 1026). Differential expression analysis was conducted to identify DEGs, which were further analyzed using LASSO regression to develop a prognostic gene signature. Quantitative PCR (qPCR) was performed to validate the expression of selected genes. Results: A total of 535 DEGs (168 up-regulated, 367 down-regulated) were identified in TP53+ samples. Further analysis with TCGA data narrowed this down to 29 genes, from which 12 were identified as prognostic features using LASSO analysis. This 12-gene signature effectively stratified patients into low- and high-risk groups for overall survival. Differences in immune cell infiltration and immune pathway activity were significant between these groups, indicating the potential of the gene signature to predict immune response. Among the genes analyzed, BMP2, LPXN, IER3, ANLN, TNNT1, OGT, KRT8, BARX2, PRC1, and SNX30 showed statistically significant differences in qPCR results. Discussion: The 12-gene signature demonstrates robust predictive capability for survival outcomes and immune response patterns in NSCLC patients, suggesting its potential clinical utility in precision oncology. The observed correlation between TP53 mutation status and immune microenvironment alterations provides valuable insights into the mechanistic basis of immunotherapy resistance and response. Conclusion: This study identifies a TP53-associated transcriptomic signature that is significantly associated with overall survival in lung cancer patients. The gene signature also correlates with differences in immune cell infiltration patterns between risk groups, offering potential insights into the tumor immune microenvironment. These findings may contribute to future efforts to stratify patients and guide immunotherapy decisions, pending further experimental validation.