PTPRO-related CD8+ T-cell signatures predict prognosis and immunotherapy response in patients with breast cancer

PTPRO 相关 CD8+ T 细胞特征可预测乳腺癌患者的预后和免疫治疗反应

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作者:Hongmei Dong, Chaoyu Xie, Zhimeng Yao, Ruijun Zhao, Yusheng Lin, Yichen Luo, Shuanglong Chen, Yanfang Qin, Yexi Chen, Hao Zhang

Background

Poor immunogenicity and extensive immunosuppressive T-cell infiltration in the tumor immune microenvironment (TIME) have been identified as potential barriers to immunotherapy success in "immune-cold" breast cancers. Thus, it is crucial to identify biomarkers that can predict immunotherapy efficacy. Protein tyrosine phosphatase receptor type O (PTPRO) regulates multiple kinases and pathways and has been implied to play a regulatory role in immune cell infiltration in various cancers.

Conclusion

PTPRO significantly impacts CD8+ T-cell infiltration in breast cancer, suggesting a potential role of immunomodulation. PTPRO-based PTS provides a new immune cell paradigm for prognosis, which is valuable for immunotherapy decisions in cancer patients.

Methods

ESTIMATE and single-sample gene set enrichment analysis (ssGSEA) were performed to uncover the TIME landscape. The correlation analysis of PTPRO and immune infiltration was performed to characterize the immune features of PTPRO. Univariate and multivariate Cox analyses were applied to determine the prognostic value of various variables and construct the PTPRO-related CD8+ T-cell signatures (PTSs). The Kaplan-Meier curve and the receiver operating characteristic (ROC) curve were used to estimate the performance of PTS in assessing prognosis and immunotherapy response in multiple validation datasets.

Results

High PTPRO expression was related to high infiltration levels of CD8+ T cells, as well as macrophages, activated dendritic cells (aDCs), tumor-infiltrating lymphocytes (TILs), and Th1 cells. Given the critical role of CD8+ T cells in the TIME, we focused on the impact of PTPRO expression on CD8+ T-cell infiltration. The prognostic PTS was then constructed using the TCGA training dataset. Further analysis showed that the PTS exhibited favorable prognostic performance in multiple validation datasets. Of note, the PTS could accurately predict the response to immune checkpoint inhibitors (ICIs).

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