单细胞和批量转录组分析揭示氨基酸代谢升高促进结直肠癌中肿瘤导向的免疫逃逸

We analyzed 46,374 epithelial cells from 17 CRC patients treated with PD-1 blockade to develop an amino acid (AA) metabolism score using the AUCell algorithm. This score was applied to a separate single-cell RNA sequencing (scRNA-seq) dataset from 23 CRC patients to investigate cell-cell interactions and functions of tumor-infiltrating immune cells, revealing distinct immune TME landscapes shaped by tumor metabolism. An in vitro co-culture assay of CRC cells and CD8 + T cells was performed to validate the findings. Additionally, LASSO and Cox regression analyses were conducted to construct an AA metabolism-related risk score for predicting prognosis and drug sensitivity across multiple bulk transcriptome cohorts.

This study identified a link between elevated amino acid metabolism in CRC epithelial cells and resistance to PD-1 blockade therapy. A 31-gene AA score was developed by intersecting differentially expressed genes between responders and non-responders to PD-1 blockade with amino acid metabolism-related genes from the Molecular Signature Database (MSigDB). Using this score, 23 additional CRC samples were classified into high and low AA score groups. Comparative analysis revealed that the low AA group exhibited a more robust immune response, characterized by a greater number and stronger cell-cell interactions. Tumor-infiltrating immune cells in this group demonstrated enhanced activation and anti-tumor functions. Furthermore, CD8 + T cells showed increased Granzyme B levels when co-cultured with CRC cells in which Psat1 or Shmt2 was knocked down. Finally, a machine learning-derived risk score based on six genes was established to translate single-cell findings to bulk transcriptomes. This risk score was found to correlate with immune checkpoint expression and immune cell infiltration, with potential implications for predicting prognosis and drug sensitivity.

Our findings highlight the role of elevated epithelial amino acid metabolism in shaping an immune-suppressive microenvironment, offering insights for patient stratification and therapeutic decision-making.