PIK3CA mutation-driven immune signature as a prognostic marker for evaluating the tumor immune microenvironment and therapeutic response in breast cancer
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Published:2024-03-11
Issue:3
Volume:150
Page:
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ISSN:1432-1335
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Container-title:Journal of Cancer Research and Clinical Oncology
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language:en
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Short-container-title:J Cancer Res Clin Oncol
Author:
Ren Xueting,Cui Hanxiao,Dai Luyao,Chang Lidan,Liu Dandan,Yan Wenyu,Zhao Xuyan,Kang Huafeng,Ma Xiaobin
Abstract
Abstract
Purpose
Gene mutations drive tumor immune microenvironment (TIME) heterogeneity, in turn affecting prognosis and immunotherapy efficacy. PIK3CA is the most frequently mutated gene in breast cancer (BC), yet its relevance to BC prognosis remains controversial. Herein, we sought to determine the impact of PIK3CA mutation-driven immune genes (PDIGs) on BC prognosis in relation to TIME heterogeneity.
Methods
PIK3CA mutation characteristics were compared and verified between the TCGA-BRCA dataset and a patient cohort from our hospital. PIK3CA mutation-driven differentially expressed genes were identified for consensus clustering and weighted gene co-expression network analysis to select the modules most relevant to the immune subtype. Thereafter, the two were intersected to obtain PDIGs. Univariate Cox, LASSO, and multivariate Cox regression analyses were sequentially performed on PDIGs to obtain a PIK3CA mutation-driven immune signature (PDIS), which was then validated using the Gene Expression Omnibus (GEO) database. Differences in functional enrichment, mutation landscape, immune infiltration, checkpoint gene expression, and drug response were compared between different risk groups.
Results
PIK3CA mutation frequencies in the TCGA and validation cohorts were 34.49% and 40.83%, respectively. PIK3CA mutants were significantly associated with ER, PR, and molecular BC subtypes in our hospital cohort. The PDIS allowed for effective risk stratification and exhibited prognostic power in TCGA and GEO sets. The low-risk patients exhibited greater immune infiltration, higher expression of common immune checkpoint factors, and lower scores for tumor immune dysfunction and exclusion.
Conclusion
The PDIS can be used as an effective prognostic model for predicting immunotherapy response to guide clinical decision-making.
Funder
Key Research and Development Plan of Shaanxi Provincial Department of Science and Technology International Science and Technology Cooperation Program Project of Shaanxi Province, China
Publisher
Springer Science and Business Media LLC
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