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Updated: Nov 4, 2025

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis
Published on: April 12, 2024
KIF4A as a novel prognostic biomarker in cholangiocarcinoma
Deng Yong Zhang1, Shuo Shuo Ma1, Wan-Liang Sun1
1Department of General Surgery, The First Affiliated Hospital of Bengbu Medical College.
Abstract:
Cholangiocarcinoma (CCA) is one of the most common malignant tumors. Although gene-targeted therapies have significantly improved the outcome of many cancers, the results are still not satisfactory for patients with CCA. Owing to the lack of an effective biomarker for guiding clinical treatment and monitoring prognosis in patients with CCA, the purpose of this study was to identify a new biomarker that could help predict the outcome of patients with CCA using bioinformatics tools.Gene expression data were collected from three publicly available datasets, comprising 263 patients with CCA and 22 healthy controls. Differentially expressed genes were obtained using the limma package (FDR < 0.05, |Log2FC|>1), and the respective protein-protein interaction revealed five relevant genes in the STRING dataset (TOP2A, BUB1, RRM2, TYMS, and KIF4A). The immunohistochemistry and PCR were used to analyze the difference in KIF4A expression in CCA.Kinesin Family Member 4A (KIF4A) was the only gene significantly associated with overall patient survival (P .035), with higher KIF4A expression being associated with poor survival rates. Moreover, KIF4A was significantly correlated with the infiltration of activated memory T cells (P = .0198) and activated mast cells (P = .008) in the tumor microenvironment. Increase in KIF4A expression affected the infiltration degree of the immune cells, which may be involved in the regulation of immune tolerance by CCA cells. The results indicated that the expression of KIF4A in CCA was higher than that in paracancerous tissues.Taken together, these findings suggest that KIF4A could be a potential new biomarker in CCA for predicting the response of patients to targeted immunotherapies.

