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. 2024 Apr 30;15(5):577.
doi: 10.3390/genes15050577.

Comprehensive Bioinformatic Investigation of TP53 Dysregulation in Diverse Cancer Landscapes

Ruby Khan  1 , Bakht Pari  2 , Krzysztof Puszynski  1
Affiliations

Comprehensive Bioinformatic Investigation of TP53 Dysregulation in Diverse Cancer Landscapes

Ruby Khan et al. Genes (Basel). .

Abstract

P53 overexpression plays a critical role in cancer pathogenesis by disrupting the intricate regulation of cellular proliferation. Despite its firmly established function as a tumor suppressor, elevated p53 levels can paradoxically contribute to tumorigenesis, influenced by factors such as exposure to carcinogens, genetic mutations, and viral infections. This phenomenon is observed across a spectrum of cancer types, including bladder (BLCA), ovarian (OV), cervical (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and uterine corpus endometrial carcinoma (UCEC). This broad spectrum of cancers is often associated with increased aggressiveness and recurrence risk. Effective therapeutic strategies targeting tumors with p53 overexpression require a comprehensive approach, integrating targeted interventions aimed at the p53 gene with conventional modalities such as chemotherapy, radiation therapy, and targeted drugs. In this extensive study, we present a detailed analysis shedding light on the multifaceted role of TP53 across various cancers, with a specific emphasis on its impact on disease-free survival (DFS). Leveraging data from the TCGA database and the GTEx dataset, along with GEPIA, UALCAN, and STRING, we identify TP53 overexpression as a significant prognostic indicator, notably pronounced in prostate adenocarcinoma (PRAD). Supported by compelling statistical significance (p < 0.05), our analysis reveals the distinct influence of TP53 overexpression on DFS outcomes in PRAD. Additionally, graphical representations of overall survival (OS) underscore the notable disparity in OS duration between tumors exhibiting elevated TP53 expression (depicted by the red line) and those with lower TP53 levels (indicated by the blue line). The hazard ratio (HR) further emphasizes the profound impact of TP53 on overall survival. Moreover, our investigation delves into the intricate TP53 protein network, unveiling genes exhibiting robust positive correlations with TP53 expression across 13 out of 27 cancers. Remarkably, negative correlations emerge with pivotal tumor suppressor genes. This network analysis elucidates critical proteins, including SIRT1, CBP, p300, ATM, DAXX, HSP 90-alpha, Mdm2, RPA70, 14-3-3 protein sigma, p53, and ASPP2, pivotal in regulating cell cycle dynamics, DNA damage response, and transcriptional regulation. Our study underscores the paramount importance of deciphering TP53 dynamics in cancer, providing invaluable insights into tumor behavior, disease-free survival, and potential therapeutic avenues.

Keywords: TCGA database; TP53 protein network; cancer development; disease-free survival; p53 overexpression; targeted therapy; tumor progression.

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Conflict of interest statement

The authors declare that there are no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Exploring the TP53 gene expression profile across TCGA tumors.
Figure 2
Figure 2
Comparison of mean tumor and normal samples across different cancer types.
Figure 3
Figure 3
Survival analyses for LGG, PRAD, and BRCA. (a) Survival analysis for LGG (Low-Grade Glioma). (b) Survival analysis for PRAD (Prostate Adenocarcinoma). (c) Survival analysis for BRCA (Breast Invasive Carcinoma).
Figure 4
Figure 4
Survival analysis for COAD and disease-related analysis for PRAD. (a) Survival analysis for COAD (Colon Adenocarcinoma). (b) Disease-related analysis for PRAD (Prostate Adenocarcinoma).
Figure 5
Figure 5
Comparative analysis of TP53 expression in Brain Tissue (BRCA) and Adenoid Cystic Carcinoma (ACC) stages: box plot visualization. (a) Box plot illustrating the expression of TP53 in different stages of brain tissue. (b) Box plot illustrating the expression of TP53 in stages of ACC.
Figure 6
Figure 6
Exploring TP53 expression patterns in Cervical Squamous Cell Carcinoma (CESC) and Cholangiocarcinoma (CHOL) progression: box plot analysis. (a) Box plot illustrating the expression of TP53 in different stages (CESC). (b) Box plot illustrating the expression of TP53 in stages of CHOL.
Figure 7
Figure 7
Analyzing TP53 expression dynamics in Colorectal Adenocarcinoma (COAD) and Esophageal Carcinoma (ESCA) progression: a box plot examination. (a) Box plot illustrating the expression of TP53 in stages of COAD. (b) Box plot illustrating the expression of TP53 in stages of ESCA.
Figure 8
Figure 8
Box plot analysis depicting TP53 expression dynamics in stages of Head and Neck Squamous Cell Carcinoma (HNSC) and Kidney Chromophobe (KICH) tumorigenesis. (a) Box plot illustrating the expression of TP53 in stages of HNSC. (b) Box plot illustrating the expression of TP53 in stages of KICH.
Figure 9
Figure 9
Box plots depicting the expression of TP53 in various stages of Kidney Renal Clear Cell Carcinoma (KIRC) and Kidney Renal Papillary Cell Carcinoma (KIRP), shedding light on the differential expression patterns across tumor stages. (a) Box plot illustrating the expression of TP53 in stages of KIRC. (b) Box plot illustrating the expression of TP53 in stages of KIRP.
Figure 10
Figure 10
Box plots showcasing the expression levels of TP53 across different stages of Liver Hepatocellular Carcinoma (LIHC) and Lung Adenocarcinoma (LUAD), providing insights into the variations in TP53 expression throughout tumor progression. (a) Box plot illustrating the expression of TP53 in stages of LIHC. (b) Box plot illustrating the expression of TP53 in stages of LUAD.
Figure 11
Figure 11
Analysis of TP53 expression across stages of Lung Squamous Cell Carcinoma (LUSC) and Pancreatic Adenocarcinoma (PAAD) using box plots. (a) Box plot illustrating the expression of TP53 in stages of LUSC. (b) Box plot illustrating the expression of TP53 in stages of PAAD.
Figure 12
Figure 12
Exploring TP53 expression patterns in Rectum Adenocarcinoma (READ) and Skin Cutaneous Melanoma (SKCM) through box plot analysis. (a) Box plot illustrating the expression of TP53 in stages of READ. (b) Box plot illustrating the expression of TP53 in stages of SKCM.
Figure 13
Figure 13
Characterizing TP53 expression patterns in Stomach Adenocarcinoma (STAD) and Thyroid Carcinoma (THCA) using box plot analysis. (a) Box plot illustrating the expression of TP53 in stages of STAD. (b) Box plot illustrating the expression of TP53 in stages of THCA.
Figure 14
Figure 14
Analyzing TP53 expression variations in Uterine Corpus Endometrial Carcinoma (UCEC) and Uveal Melanoma (UVM) through box plot analysis. (a) Box plot illustrating the expression of TP53 in stages of UCEC. (b) Box plot illustrating the expression of TP53 in stages of UVM.
Figure 15
Figure 15
TP53 protein network from STRING database.
Figure 16
Figure 16
Simulation of protein concentrations.
Figure 17
Figure 17
Steady-state analysis.
Figure 18
Figure 18
Sensitivity analysis.
Figure 19
Figure 19
Parameter sweep analysis.
Figure 20
Figure 20
Frequency analysis.
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