Comprehensive Bioinformatic Investigation of TP53 Dysregulation in Diverse Cancer Landscapes

In this study, we present a comprehensive analysis of the role of TP53 in various cancers, its impact on disease-free survival (DFS), and the mathematical models used to understand TP53 protein network dynamics. TP53 Analysis: We analyzed a diverse set of cancers, including BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, HNSC, KICH, KIRC, LIHC, LUAD, LUSC, and UCEC, and identified TP53 overexpression as a significant prognostic factor in PRAD. The analysis, supported by a significant p-value (p < 0.05), revealed the distinctive influence of TP53 overexpression on DFS outcomes in PRAD. Our study includes graphical representations of overall survival (OS) analysis, vividly illustrating the stark contrast in OS time between tumors with higher TP53 expression (depicted by the red line) and those with lower TP53 expression (indicated by the blue line). Additionally, the hazard ratio (HR) underscores the impact of TP53 on overall survival. TP53 Protein Network Analysis: Furthermore, we investigate the TP53 protein network, identifying genes with strong positive correlations with TP53 expression in 13 out of 27 cancers, along with some negative correlations with important tumor suppressor genes. This network analysis revealed key proteins such as SIRT1, CBP, p300, ATM, DAXX, HSP 90-alpha, Mdm2, RPA70, 14-3-3 protein sigma, p53, and ASPP2, which play critical roles in cell cycle regulation, DNA damage response, and transcriptional control. Mathematical Models: We also describe mathematical models used to simulate TP53 protein network dynamics. These models consider variables such as protein concentrations ( P i ), correlation coefficients ( C ij ), rate constants ( k ij ), and degradation rate constants ( λ i ). The models’ equations capture the dynamics of protein concentrations influenced by correlations and rate constants specific to different cancer types. Our findings provide compelling evidence of the pivotal roles played by TP53 and TP53 overexpression in shaping cancer prognosis across various tumor types. These results carry significant implications for clinical management and underscore the need for further research into the underlying molecular mechanisms driving these associations.