T Cell Heterogeneity Shaped by Alternative Splicing Predicts Prognosis in Hepatocellular Carcinoma

Alternative splicing (AS) plays a key role in the regulation of immune function and cellular heterogeneity. However, its impact on T-cell behavior and clinical outcomes in hepatocellular carcinoma (HCC) remains poorly understood. Single-cell full-length transcriptome data were used to obtain comprehensive splicing site information. Variable AS events were identified in each cell using the SCASL algorithm, and AS-based profiles were constructed. Based on this, differential expression, functional enrichment, cell interaction, and RNA velocity analyses were used to reveal functional differences among distinct cell populations based on AS profiles. Finally, based on the functional differences among distinct T-cell subpopulations, an AS event-based survival prognosis model was constructed to assess patient outcomes. Using the SCASL algorithm, major cell subpopulations were identified and T cells were further divided into two AS-based subtypes distinct from gene expression clustering. Functional analysis revealed that these subtypes were enriched in NOD-like receptors, C-type lectin receptors, T-cell receptor signaling, antigen processing and presentation, and other immune-related pathways. Based on the definition of AS events, we constructed a prognostic model capable of distinguishing between patients with different risk levels, which was further validated using public datasets. Collectively, our findings highlight the crucial role of AS in shaping T-cell functional heterogeneity, and the AS-based prognostic model we established further underscores its clinical relevance, providing novel insights into tumor-immune interactions and uncovering potential splicing-derived biomarkers for immunotherapy.