Background: Exercise exerts tumor-suppressive effects across multiple malignancies, partly through exerkines—exercise-induced secreted factors with immunomodulatory and metabolic functions. However, the prognostic relevance of exerkines across cancer types remains unclear, and the molecular determinants of exercise responsiveness are poorly defined.
Methods: We systematically profiled 183 curated exerkine-related genes across 33 cancer types from The Cancer Genome Atlas (TCGA) using non-negative matrix factorization (NMF) to define molecular subtypes. Prognostic significance was evaluated via Kaplan-Meier analysis. For five cancers with consistent survival divergence (LGG, KIRC, LUAD, PAAD, ACC), we developed an Exerkine Prognostic Index (EPI) using LASSO Cox regression and validated its predictive performance through time-dependent ROC analysis. Immune cell infiltration (CIBERSORT), stromal/immune scores (ESTIMATE), and immune checkpoint expression were assessed to characterize immune landscape differences between EPI subgroups.
Results: Exerkine- based NMF clustering identified prognostically distinct subtypes in 25 cancers. The EPI robustly stratified patients into high- and low-risk groups with significant differences in overall survival (p < 0.001). High-EPI subgroups were associated with elevated infiltration of immunosuppressive cells (e.g., Tregs, M0 macrophages), altered immune/stromal scores, and differential expression of immune checkpoints such as PD-L1 and CTLA4 in a cancer-type-specific manner.
Discussion: Our findings reveal that exerkine expression patterns capture biologically and clinically relevant heterogeneity across cancers. The EPI provides a robust molecular tool to stratify patients by prognosis and immune contexture, offering insights into differential exercise responsiveness.
Conclusions: Exerkines represent promising biomarkers for risk stratification and precision-guided exercise interventions in oncology.
CRediT authorship contribution statement
Jiawei Du: Writing - original draft, Visualization, Methodology, Formal analysis, Data curation, Conceptualization. Jinghua Hou: Writing - review & editing, Supervision.
Data statement
All data used in our study are publicly available. RNA-seq data and corresponding clinical annotations for 33 cancer types were retrieved from the TCGA database via UCSC Xena (
https://xena.ucsc.edu/).
Funding
This study was supported by Beijing Sport University Graduate Innovation Programme (2024013).
Declaration of competing interest
The authors declare that they have no competing interests.
Appendix A. Supplementary data
Supplementary data to this article can be found online at
https://doi.org/10.1016/j.smhs.2025.05.002.
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