A Novel Ferroptosis-Related 4-Gene Prognostic Signature for Cholangiocarcinoma and Photodynamic Therapy.

Abstract

Cholangiocarcinoma is the second most common malignant tumor in the hepatobiliary system. Compared with data on hepatocellular carcinoma, fewer public data and prognostic-related studies on cholangiocarcinoma are available, and effective prognostic prediction methods for cholangiocarcinoma are lacking. In recent years, ferroptosis has become an important subject of tumor research. Some studies have indicated that ferroptosis plays an important role in hepatobiliary cancers. However, the prediction of the prognostic effect of ferroptosis in patients with cholangiocarcinoma has not been reported. In addition, many reports have described the ability of photodynamic therapy (PDT), a potential therapy for cholangiocarcinoma, to regulate ferroptosis by generating reactive oxygen species (ROS). By constructing ferroptosis scores, the prognoses of patients with cholangiocarcinoma can be effectively predicted, and potential gene targets can be discovered to further enhance the efficacy of PDT. In this study, gene expression profiles and clinical information (TCGA, E-MTAB-6389, and GSE107943) of patients with cholangiocarcinoma were collected and divided into training sets and validation sets. Then, a model of the ferroptosis gene signature was constructed using least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis. Furthermore, through the analysis of RNA-seq data after PDT treatment of cholangiocarcinoma, PDT-sensitive genes were obtained and verified by immunohistochemistry staining and Western blot. The results of this study provide new insight for predicting the prognosis of cholangiocarcinoma and screening target genes that enhance the efficacy of PDT.

• Authors: Zi-Jian Zhang, Yun-Peng Huang, Xiao-Xue Li, Zhong-Tao Liu, Kai Liu, Xiao-Feng Deng, Li Xiong, Heng Zou, Yu Wen
• Journal: Frontiers in oncology (Front Oncol) Vol. 11 Pg. 747445 ( 2021) ISSN: 2234-943X [Print] Switzerland
• PMID: 34712611 (Publication Type: Journal Article)
• Copyright: Copyright © 2021 Zhang, Huang, Li, Liu, Liu, Deng, Xiong, Zou and Wen.