Dihydropyrimidine Dehydrogenase (DPD) as a Bridge between the Immune Microenvironment of Colon Cancers and 5-FU Resistance.

Background: The purpose of the present study was to investigate the role of the 5-Fluorouracil (5-FU) resistance-related factor dihydropyrimidine dehydrogenase (DPD) in tumor immunity and prognosis and to study the relationship between drug resistance and the immune microenvironment of colon cancer.

Methods: Bioinformatics methods were used to analyze the expression of DPD associated with prognosis, immunity, microsatellite instability, and tumor mutational burden in colon cancer. Immunohistochemistry (IHC) was used to detect DPD, MLH1, MSH2, MSH6, and PMS2 in 219 colon cancer tissue samples.

Glutathione peroxidase 2: A key factor in the development of microsatellite instability in colon cancer.

Background: Much research has focused on detecting microsatellite instability (MSI), which is frequently employed in the diagnosis and treatment of patients with colon cancer. However, the causes and progression of MSI in colon cancer have not yet been thoroughly elucidated. In this study, we screened and validated the genes associated with MSI in colorectal adenocarcinoma (COAD) using bioinformatics analysis.

A common tumor in an uncommon site: epithelioid Leiomyoma arising from the seminal vesicle-a case report.

Background: Leiomyoma of the seminal vesicle is a rare leiomyoma characterized by the formation of benign leiomyomatous tissue within the seminal vesicle. Although histologically benign, excessive size can lead to urinary system disease if left untreated. Herein, we report a case of a seminal vesicle epithelioid leiomyoma.

Key Candidate Genes - VSIG2 of Colon Cancer Identified by Weighted Gene Co-Expression Network Analysis.

Background: Colon adenocarcinoma (COAD) is one of the most common malignancies. To identify candidate genes that may be involved in colon adenocarcinoma development and progression, weighted gene co-expression network analysis (WGCNA) was used to construct gene co-expression networks to explore associations between gene sets and clinical features and to identify candidate biomarkers. Moreover, we intend to make a preliminary exploration on it.