Analysis of p53 and vascular endothelial growth factor expression in human gallbladder carcinoma for the determination of tumor vascularity

AIM: To examine the expression of p53 and vascular endothelial growth factor (VEGF) as well as microvessel count (MVC) and to investigate the role of VEGF as an angiogenic marker and the possible role of p53 in the regulation of angiogenesis in human gallbladder carcinoma.

METHODS: Surgically resected specimens of 49 gallbladder carcinomas were studied by immunohistochemical staining for p53 protein, VEGF, and factor VIII-related antigen. VEGF expression and mutant p53 expression were then correlated with Nevin stage, differentiation grade, MVC, and lymph node metastasis.

RESULTS: Positive p53 protein and VEGF expressions were found in 61.2% and 63.3% of tumors, respectively. p53 and VEGF staining status was identical in 55.1% of tumors. The Nevin staging of p53- or VEGF-positive tumors was significantly later than that of negative tumors. The MVC in p53- or VEGF-positive tumors was significantly higher than that in negative tumors, and MVC in both p53- and VEGF-negative tumors was significantly lower than that in the other subgroups.

CONCLUSION: Our findings suggest that p53-VEGF pathway can regulate tumor angiogenesis in human gallbladder carcinoma. Combined analysis of p53 and VEGF expression might be useful for predicting the tumor vascularity of gallbladder cancer.

DNA damage response in peripheral mouse blood leukocytes in vivo after variable, low-dose rate exposure

Environmental contamination and ingestion of the radionuclide Cesium-137 (137Cs) is a large concern in fallout from a nuclear reactor accident or improvised nuclear device, and highlights the need to develop biological assays for low-dose rate, internal emitter radiation. To mimic low-dose rates attributable to fallout, we have developed a VAriable Dose-rate External 137Cs irradiatoR (VADER), which can provide arbitrarily varying and progressive low-dose rate irradiations in the range of 0.1-1.2 Gy/day, while circumventing the complexities of dealing with radioactively contaminated biomaterials. We investigated the kinetics of mouse peripheral leukocytes DNA damage response in vivo after variable, low-dose rate 137Cs exposure. C57BL/6 mice were placed in the VADER over 7 days with total accumulated dose up to 2.7 Gy. Peripheral blood response including the leukocyte depletion, apoptosis as well as its signal protein p53 and DNA repair biomarker γ-H2AX was measured. The results illustrated that blood leukocyte numbers had significantly dropped by day 7. P53 levels peaked at day 2 (total dose = 0.91 Gy) and then declined; whereas, γ-H2AX fluorescence intensity (MFI) and foci number generally increased with accumulated dose and peaked at day 5 (total dose = 2.08 Gy). ROC curve analysis for γ-H2AX provided a good discrimination of accumulated dose < 2 Gy and ≥ 2 Gy, highlighting the potential of γ-H2AX MFI as a biomarker for dosimetry in a protracted, environmental exposure scenario.

Role of artificial intelligence in cancer detection using protein p53: A Review

Normal cell development and prevention of tumor formation rely on the tumor-suppressor protein p53. This crucial protein is produced from the Tp53 gene, which encodes the p53 protein. The p53 protein plays a vital role in regulating cell growth, DNA repair, and apoptosis (programmed cell death), thereby maintaining the integrity of the genome and preventing the formation of tumors. Since p53 was discovered 43 years ago, many researchers have clarified its functions in the development of tumors. With the support of the protein p53 and targeted artificial intelligence modeling, it will be possible to detect cancer and tumor activity at an early stage. This will open up new research opportunities. In this review article, a comprehensive analysis was conducted on different machine learning techniques utilized in conjunction with the protein p53 to predict and speculate cancer. The study examined the types of data incorporated and evaluated the performance of these techniques. The aim was to provide a thorough understanding of the effectiveness of machine learning in predicting and speculating cancer using the protein p53.