J Cancer 2022; 13(2):496-507. doi:10.7150/jca.65646 This issue

Research Paper

Identifies Immune Feature Genes for Prediction of Chemotherapy Benefit in Cancer

Yuquan Bai*, Chuan Li*, Liang Xia, Fanyi Gan, Zhen Zeng, Chuanfen Zhang, Yulan Deng, Yuyang Xu, Chengwu Liu, Senyi Deng, Lunxu Liu

Department of Thoracic Surgery and Institute of Thoracic Oncology, West China Hospital, Sichuan University. Chengdu, 610041.
*These authors have contributed equally to this work

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Bai Y, Li C, Xia L, Gan F, Zeng Z, Zhang C, Deng Y, Xu Y, Liu C, Deng S, Liu L. Identifies Immune Feature Genes for Prediction of Chemotherapy Benefit in Cancer. J Cancer 2022; 13(2):496-507. doi:10.7150/jca.65646. Available from https://www.jcancer.org/v13p0496.htm

File import instruction


Graphic abstract

Chemotherapy is still the most fundamental treatment for advanced cancers so far. Previous studies have indicated that immune cell infiltration (ICI) index could serve as a biomarker to predict chemotherapy benefit in breast cancer and colorectal cancer. However, due to different responses of tumor infiltrating immune cells (TIICs) to chemotherapy, the prediction efficiency of ICI index is not fully confirmed by now. In our study, we first extended this conclusion in 7 cancers that high ICI index could certainly indicate chemotherapy benefit (P<0.05). But we also found the fraction of different TIICs and the interaction of TIICs were varies greatly from cancer to cancer. Therefore, we executed correlation and causal network analysis to identify chemotherapy associated immune feature genes, and fortunately identified six co-owned immune feature genes (CD48, GPR65, C3AR1, CD2, CD3E and ARHGAP9) in 10 cancers (BLCA, BRCA, COAD, LUAD, LUSC, OV, PAAD, SKCM, STAD and UCEC). Base on this, we developed a chemotherapy benefit prediction model within six co-owned immune feature genes through random forest classifying (AUC =0.83) in cancers mentioned above, and validated its efficiency in external datasets. In short, our work offers a novel model with a shrinking panel which has the potential to guide optimal chemotherapy in cancer.

Keywords: Chemotherapy benefit, Immune cell infiltration, Network analysis, prediction model