ESCC Molecular Subtypes Based on Multidimensional Features of p53 and Their Clinical Significance

Overview

Collect 300 esophageal cancer tissue specimens, including cancer tissue and serum, from a biological sample bank. Collect clinical information on esophageal cancer, including gender, age, time of diagnosis, time of death, treatment information, TNM staging, tumor size, location, etc. DNA sequencing was performed on 300 collected esophageal cancer samples, divided into mutant and wild-type types. Perform immunohistochemistry on tissue slices and observe the expression of P53 mutant protein. ELISA was used to study the expression of serum anti-P53 autoantibodies. Process these data and use them to classify subtypes of esophageal cancer. Associate each subtype with clinical information, analyze the characteristics of each subtype, such as the multidimensional features of P53 and treatment correlation analysis, and the correlation between P53 multidimensional features and survival.

Specific research design 1. Collect information on all clinical cases of esophageal cancer in the study, including gender, age, time of diagnosis, time of death, treatment information, TNM staging, tumor size, location, etc. 2. Perform DNA sequencing on the collected esophageal cancer tissue samples, identify gene mutation sites, and group them according to different types of mutations. 3. Perform ELISA on the collected serum of esophageal cancer patients to distinguish between groups with negative and positive autoantibodies. 4. Perform immunohistochemistry on tumor tissue slices collected from esophageal cancer patients, observe the expression of mutant P53 protein, and obtain samples with positive immunohistochemistry for P53 protein. 5. Based on the presence or absence of P53 mutations, the expression of mutant P53 protein in immunohistochemistry, and the positive or negative autoantibodies in serum, further subtype classification criteria will be developed. 6. Associate different subtypes of esophageal cancer with clinical and pathological features, identify their respective characteristics, and provide guidance for clinical diagnosis, treatment, and prognosis. Univariate and multivariate survival analysis of clinical pathological parameters and the correlation between this subtype and DFS and OS in cancer patients; Kaplan Meier curve analysis of the relationship between this subtype and patient survival, as well as its expression for cancer risk assessment. Specific research design 1. Collect information on all clinical cases of esophageal cancer in the study, including gender, age, time of diagnosis, time of death, treatment information, TNM staging, tumor size, location, etc. 2. Perform DNA sequencing on the collected esophageal cancer tissue samples, identify gene mutation sites, and group them according to different types of mutations. 3. Perform ELISA on the collected serum of esophageal cancer patients to distinguish between groups with negative and positive autoantibodies. 4. Perform immunohistochemistry on tumor tissue slices collected from esophageal cancer patients, observe the expression of mutant P53 protein, and obtain samples with positive immunohistochemistry for P53 protein. 5. Based on the presence or absence of P53 mutations, the expression of mutant P53 protein in immunohistochemistry, and the positive or negative autoantibodies in serum, further subtype classification criteria will be developed. 6. Associate different subtypes of esophageal cancer with clinical and pathological features, identify their respective characteristics, and provide guidance for clinical diagnosis, treatment, and prognosis. Univariate and multivariate survival analysis of clinical pathological parameters and the correlation between this subtype and DFS and OS in cancer patients; Kaplan Meier curve analysis of the relationship between this subtype and patient survival, as well as its expression for cancer risk assessment.