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Introduction: Early-stage diagnosis is crucial for the best prognosis in breast cancer. Here, our objective was to compare serum tumor protein p53 (TP53) levels in an Egyptian population with benign breast disease vs breast cancer to investigate the protein's utility in early detection and intervention, alongside existing tumor-related markers, including carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), and CA15.3.
Methods: We enrolled 231 patients in this study: 146 with breast cancer, 50 with benign conditions, and 35 without breast pathology (healthy control individuals). The serum level of TP53, CEA, CA125, and CA15.3 was quantitated using an enzyme-linked immunosorbent assay. Data were examined using the t test, Spearman correlation, and receiver operating characteristic curve analysis.
Results: Patients with breast cancer showed clinically significant higher serum levels of TP53 than did individuals with benign conditions and healthy control individuals. Elevated TP53 levels were correlated with advanced tumor stage (P <.001) and high-tumor grade (P <.001). TP53 effectively distinguished between benign disease and early-stage breast cancer, with an area under the curve of 0.88, 73.6% sensitivity, and 78.0% specificity. Multinomial logistic regression analysis showed that combining TP53 with other tumor-related markers improved early breast cancer detection. Both TP53 and CA125 remained significant predictors in this analysis. A new function, the PCA-Index, which combines concentrations of TP53 and CA125, was developed to enhance early detection in early stages (T1-T2) and low grades (1-2) with notable results: areas under the curve of 0.96 and 0.98, a specificity of 90.0%, and sensitivities of 85.7% and 91.7%, respectively.
Discussion: The combined detection of TP53 and CA125, the PCA-Index, has important clinical value for early detection of breast cancer with a high degree of sensitivity and specificity.
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http://dx.doi.org/10.1093/labmed/lmaf032 | DOI Listing |
EBioMedicine
September 2025
Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, PR China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, PR China. Electronic address:
JMIR Cancer
September 2025
Cancer Patients Europe, Rue de l'Industrie 24, Brussels, 1000, Belgium.
Background: Breast cancer is the most common cancer among women and a leading cause of mortality in Europe. Early detection through screening reduces mortality, yet participation in mammography-based programs remains suboptimal due to discomfort, radiation exposure, and accessibility issues. Thermography, particularly when driven by artificial intelligence (AI), is being explored as a noninvasive, radiation-free alternative.
View Article and Find Full Text PDFEpidemiol Serv Saude
September 2025
Universidade Estadual do Norte do Paraná, Programa de Pós-Graduação em Enfermagem em Atenção Primária à Saúde Bandeirantes, PR, Brazil.
Objectives: To analyze the temporal trend and identify spatial clusters of breast cancer mortality in Paraná state between 2012 and 2021.
Methods: This was a time series study, with spatial analysis of breast cancer mortality rates in the 399 municipalities of Paraná. Data were selected from the Mortality Information System.
Cien Saude Colet
August 2025
Faculdade de Medicina da Universidade Federal de Pelotas. Pelotas RS Brasil.
The objective of this study was to analyze the characteristics of avoidable mortality in the population aged five to 69 years living in the city of Pelotas/RS, comparing it with the rest of the state of Rio Grande do Sul, from 2000 to 2021. An ecological study was conducted analyzing avoidable mortality coefficients according to sex and age, from 2000 to 2021. The data source was the Mortality Information System, and the trend analysis was performed using Prais-Winsten regression, with standardization of coefficients.
View Article and Find Full Text PDFCien Saude Colet
August 2025
Programa de Pós-Graduação em Nutrição e Saúde, Universidade Estadual do Ceará. R. Betel 1958, Itaperi. 60714-230 Fortaleza CE Brasil.
This study aimed to evaluate mortality due to female breast cancer attributable to overweight and obesity and to estimate the number of preventable deaths with a reduction in the Body Mass Index in Brazil. An ecological study was carried out with investigation of information on overweight, obesity, sociodemographic characteristics based on a national survey carried out in 2013-14; breast cancer mortality rate in 2019 using the Online Atlas of Mortality and Relative Risk Meta-Analyses. The Potential Impact Fraction analysis was carried out, considering the following counterfactual scenarios related to the reduction in BMI: Scenario A - population contingent of women that make up the prevalence of overweight and obesity now composes the prevalence of eutrophy; Scenario B - population contingent of women that make up the prevalence of overweight starts to make up the prevalence of eutrophy; Scenario C - population contingent of women that make up the prevalence of obesity becomes part of the prevalence of overweight.
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