Basic Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Mar 21, 2024; 30(11): 1609-1620
Published online Mar 21, 2024. doi: 10.3748/wjg.v30.i11.1609
Identification of an immune-related gene signature for predicting prognosis and immunotherapy efficacy in liver cancer via cell-cell communication
Jun-Tao Li, Hong-Mei Zhang, Wei Wang, Dong-Qing Wei
Jun-Tao Li, Hong-Mei Zhang, College of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, Henan Province, China
Wei Wang, College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, Henan Province, China
Dong-Qing Wei, State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
Co-corresponding authors: Hong-Mei Zhang and Dong-Qing Wei.
Author contributions: Zhang HM and Wei DQ contributed equally to this work and should be considered co-corresponding authors. Li JT and Zhang HM conceived this study and implemented the experiments; Li JT and Wang W collected and preprocessed the data; Zhang HM and Wei DQ drafted and revised the manuscript.
Supported by Scientific and Technological Project of Henan Province, No. 212102210140.
Institutional review board statement: This study doesn’t involve any human subjects.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Hong-Mei Zhang, MPhil, Master’s Student, College of Mathematics and Information Science, Henan Normal University, Jianshe East Road, Xinxiang 453007, Henan Province, China. zhanghmmail@163.com
Received: December 13, 2023
Peer-review started: December 13, 2023
First decision: December 27, 2023
Revised: January 9, 2024
Accepted: March 4, 2024
Article in press: March 4, 2024
Published online: March 21, 2024
ARTICLE HIGHLIGHTS
Research background

Immunotherapy has provided hope to patients with advanced liver cancer, but only a small fraction of patients benefit from this treatment due to individual differences. Although several methods have been developed to predict the prognosis and immunotherapeutic efficacy in patients with liver cancer, the impact of cell-cell interactions in the tumor microenvironment has not been adequately considered.

Research motivation

Recent research has demonstrated the crucial role of cell-cell interactions in shaping the immune landscape of liver cancer.

Research objectives

This study aims to identify immune-related gene signatures through cell-cell interactions to predict prognosis and immunotherapeutic efficacy in liver cancer.

Research methods

In this study, CellChat was employed to infer cell-cell communication, thereby selecting highly active cell groups in immune-related pathways on single-cell RNA-sequencing (scRNA-seq) data. Highly active immune cells were identified by intersecting these groups with B and T cells. Subsequently, significantly differentially expressed genes between highly active immune cells and the remaining cells were incorporated into the Lasso regression model. Ultimately, incorporating genes selected more than 5 times in 10 Lasso regression experiments into a multivariable Cox regression model, 3 genes (stathmin 1, cofilin 1, and C-C chemokine ligand 5) significantly associated with survival were identified to construct a gene signature.

Research results

The immune-related gene signature composed of stathmin 1, cofilin 1, and C-C chemokine ligand 5 was identified through cell-cell communication. The identified gene signature has been validated to be superior to the other two methods through immunotherapy response prediction, tumor mutation burden analysis, and immune cell infiltration analysis, enabling better prediction of prognosis and immune therapy efficacy in liver cancer.

Research conclusions

This study suggest that the identified gene signature may contribute to a deeper understanding of the activity patterns of immune cells in the liver tumor microenvironment, providing insights for personalized treatment strategies.

Research perspectives

This article utilized cell-cell communication information and machine learning method, combined with Cox regression, to comprehensively analyze bulk and scRNA-seq data, identifying clinically and therapeutically relevant immune-related gene signature.