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A Single-Cell-Derived CD8+ T-Cell State Plasticity Score for Prognostic Stratification and Characterization of Immune
Le Xing1,2, Dan He2, Dong-Mei Li2
1Department of Laboratory Medicine, Affiliated Qingyuan Hospital of Guangzhou Medical University (Qingyuan People's Hospital), Qingyuan, Guangdong, People's Republic of China.
Background:
T cells play a central role in antitumor immunity. Emerging evidence indicates that T-cell plasticity and differentiation states are associated with clinical outcomes and responses to cancer immunotherapy. CD8⁺ T cells that retain differentiation potential are thought to sustain effective antitumor immunity. However, robust quantitative metrics for assessing tumor-infiltrating CD8+ T-cell state plasticity and defining its clinical significance in hepatocellular carcinoma remain lacking.
Methods:
We integrated publicly available single-cell RNA-sequencing datasets from HCC cohorts to characterize CD8⁺ T-cell states using unsupervised pseudotime reconstruction and supervised ordinal transcriptional modeling. We used genes showing progressive changes along the inferred state axis and enriched in CD8⁺ T cells to develop a T-cell State Plasticity score using Cox proportional hazards and LASSO regression in the training cohort. The model was evaluated in independent bulk transcriptomic cohorts.
Results:
Supervised psupertime analysis validated the predefined Tcm-Tem-Tpex-Ttex ordinal axis in held-out cells, supporting the existence of an ordered CD8⁺ T-cell state continuum in HCC. The CD8+ T-cell State Plasticity score robustly stratified clinical outcomes across multiple large HCC cohorts. High-risk scores, indicative of reduced CD8+ T-cell state plasticity, were associated with adverse clinical outcomes and an immunosuppressive tumor immune microenvironment.
Conclusion:
CD8+ T-cell states are associated with tumor immune characteristics and clinical outcomes in HCC. This single-cell-derived T cell plasticity scoring system provides a quantitative framework for prognostic prediction and immune landscape evaluation, with potential implications for clinical decision-making.
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