Translational cancer research · 2026
Single-cell analysis identifies a tumor-specific T-cell metabolic signature: prognostic model and association with immunosuppressive microenvironment in ovarian cancer.
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Tian Huadong, Liu Xiaotong, Wang Guojiao, Wang Zimo, Luo Yuexi
Аннотация
BACKGROUND: Ovarian cancer (OC) remains the most lethal gynecological malignancy, with profound tumor microenvironment (TME) heterogeneity contributing to the suboptimal clinical response to immune checkpoint inhibitors (ICIs). Therefore, delineating the metabolic heterogeneity of T cells within the TME is imperative for identifying precise prognostic biomarkers and refining individualized immunotherapeutic strategies.
METHODS: We integrated single-cell RNA sequencing (scRNA-seq) data (GSE184880) with multi-omics profiles from The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) cohort. The Seurat and Harmony algorithms were employed to identify tumor-specific T-cell subpopulations (CA-T cells). Weighted gene co-expression network analysis (WGCNA) and machine learning frameworks-including the least absolute shrinkage and selection operator (LASSO) regression and Random Survival Forest-were applied to screen core prognostic genes and construct a multi-gene risk scoring model (riskScore) for predicting overall survival (OS). The model was validated through Kaplan-Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, and independent prognostic assessment. Finally, ESTIMATE, CIBERSORT, and TIDE algorithms were applied to assess the associations between riskScore, TME immune landscapes, and ICI efficacy.
RESULTS: We identified a distinct tumor-specific T-cell subset (CA-T cells) significantly enriched in malignant tissues. This subset exhibited a functional signature characterized by mitochondrial metabolic reprogramming, specifically oxidative phosphorylation and electron transport chain activity. An 8-gene riskScore model-comprising SLAMF1, CXCR4, SFT2D1, SH3KBP1, SPOCK2, CDKN1B, GNPTAB, and SNRPA1-demonstrated superior independent prognostic value, with area under the curve (AUC) values of 0.707, 0.698, and 0.724 for 1-, 3-, and 5-year survival predictions, respectively. Immunological profiling revealed that high-risk patients exhibited a prototypical 'cold' tumor phenotype, marked by significantly diminished infiltration of stromal and effector immune cells (e.g., CD8+ T cells) and a concomitant enrichment of immunosuppressive populations, including myeloid-derived suppressor cells (MDSCs) and M2-type tumor-associated macrophages (M2-TAMs). Furthermore, high-risk tumors displayed elevated immune dysfunction and exclusion scores alongside reduced IFN-γ and PD-L1 expression, suggesting limited sensitivity to ICI therapy.
CONCLUSIONS: This study established a robust prognostic framework based on single-cell resolved T-cell metabolic signatures. Our findings provide a reliable independent prognostic tool for OC and elucidate the intricate interplay between metabolic reprogramming and the immunosuppressive microenvironment. These results offer critical mechanistic insights for prognostic stratification and the development of personalized combinatorial immunotherapies in OC.
METHODS: We integrated single-cell RNA sequencing (scRNA-seq) data (GSE184880) with multi-omics profiles from The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) cohort. The Seurat and Harmony algorithms were employed to identify tumor-specific T-cell subpopulations (CA-T cells). Weighted gene co-expression network analysis (WGCNA) and machine learning frameworks-including the least absolute shrinkage and selection operator (LASSO) regression and Random Survival Forest-were applied to screen core prognostic genes and construct a multi-gene risk scoring model (riskScore) for predicting overall survival (OS). The model was validated through Kaplan-Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, and independent prognostic assessment. Finally, ESTIMATE, CIBERSORT, and TIDE algorithms were applied to assess the associations between riskScore, TME immune landscapes, and ICI efficacy.
RESULTS: We identified a distinct tumor-specific T-cell subset (CA-T cells) significantly enriched in malignant tissues. This subset exhibited a functional signature characterized by mitochondrial metabolic reprogramming, specifically oxidative phosphorylation and electron transport chain activity. An 8-gene riskScore model-comprising SLAMF1, CXCR4, SFT2D1, SH3KBP1, SPOCK2, CDKN1B, GNPTAB, and SNRPA1-demonstrated superior independent prognostic value, with area under the curve (AUC) values of 0.707, 0.698, and 0.724 for 1-, 3-, and 5-year survival predictions, respectively. Immunological profiling revealed that high-risk patients exhibited a prototypical 'cold' tumor phenotype, marked by significantly diminished infiltration of stromal and effector immune cells (e.g., CD8+ T cells) and a concomitant enrichment of immunosuppressive populations, including myeloid-derived suppressor cells (MDSCs) and M2-type tumor-associated macrophages (M2-TAMs). Furthermore, high-risk tumors displayed elevated immune dysfunction and exclusion scores alongside reduced IFN-γ and PD-L1 expression, suggesting limited sensitivity to ICI therapy.
CONCLUSIONS: This study established a robust prognostic framework based on single-cell resolved T-cell metabolic signatures. Our findings provide a reliable independent prognostic tool for OC and elucidate the intricate interplay between metabolic reprogramming and the immunosuppressive microenvironment. These results offer critical mechanistic insights for prognostic stratification and the development of personalized combinatorial immunotherapies in OC.