Proteomic Profiling and Biomarker Discovery for Predicting Response to PD-1 Inhibitor Immunotherapy in Gastric Cancer Patients
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This repository provides R code for reproducing proteomics-based analysis. The code was verified using R 4.2.
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Our proteomics-based analysis identified revealed that low activity in the complement and coagulation cascades pathway (CCCP) and a high abundance of activated CD8 T cells are positive signals corresponding to ICIs. Finally, utilizing machine learning, we successfully identified a set of 10 protein biomarkers, and the constructed model demonstrated excellent performance in predicting response on an independent validation set (N = 14, area under the curve (AUC) = 0.959).
The MS raw data generated in this study have been deposited in the ProteomeXchange Consortium (dataset identifier: PXD050228) via the iProX partner repository under accession code IPX0008285000. The original contributions presented in the study are included in the article/Supplementary Information and Table. Further inquiries can be directed to the corresponding authors.
If you use the code or data from this repository in your research, please consider citing our paper as follows:
J. Sun, X. Li, Q. Wang, P. Chen, L. Zhao, Y. Gao, Proteomic profiling and biomarker discovery for predicting the response to PD-1 inhibitor immunotherapy in gastric cancer patients, Front. Pharmacol. 15 (2024). https://doi.org/10.3389/fphar.2024.1349459.