Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer
BackgroundBreast cancer is the most common malignancy in women globally, with significant heterogeneity affecting prognosis and treatment. RNA-binding proteins play vital roles in tumor progression, yet their prognostic potential remains unclear. This study introduces an Artificial Intelligence-Assi...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1583103/full |
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| author | Yunxia Zhao Li Li Shuqi Yuan Zixin Meng Jiayi Xu Zhaogen Cai Yijing Zhang Xiaonan Zhang Tao Wang |
| author_facet | Yunxia Zhao Li Li Shuqi Yuan Zixin Meng Jiayi Xu Zhaogen Cai Yijing Zhang Xiaonan Zhang Tao Wang |
| author_sort | Yunxia Zhao |
| collection | DOAJ |
| description | BackgroundBreast cancer is the most common malignancy in women globally, with significant heterogeneity affecting prognosis and treatment. RNA-binding proteins play vital roles in tumor progression, yet their prognostic potential remains unclear. This study introduces an Artificial Intelligence-Assisted RBP Signature (AIRS) model to improve prognostic accuracy and guide personalized treatment.MethodsData from 14 BC cohorts (9,000+ patients) were analyzed using 108 machine learning model combinations. The AIRS model, built on three key RBP genes (PGK1, MPHOSPH10, MAP2K6), stratified patients into high- and low-risk groups. Genomic alterations, single-cell transcriptomics, tumor microenvironment characteristics, and drug sensitivity were assessed to uncover AIRS-associated mechanisms.ResultsThe AIRS model demonstrated superior prognostic performance, surpassing 106 established signatures. High AIRS scores correlated with elevated tumor mutational burden, specific copy number alterations, and an immune-suppressive TME. Single-cell analysis revealed functional heterogeneity in epithelial cells, linking high AIRS scores to pathways like transcription factor binding. Regulatory network analysis identified key transcription factors such as MYC. Low AIRS scores predicted better responses to immune checkpoint inhibitors, while drug sensitivity analysis highlighted panobinostat and paclitaxel as potential therapies for high-risk patients.ConclusionsThe AIRS model offers a robust tool for BC prognosis and treatment stratification, integrating genomic, transcriptomic, and single-cell data. It provides actionable insights for personalized therapy, paving the way for improved clinical outcomes. Future studies should validate findings across diverse populations and expand functional analyses. |
| format | Article |
| id | doaj-art-080d938bb84045b481127728f5279f03 |
| institution | OA Journals |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-080d938bb84045b481127728f5279f032025-08-20T02:17:34ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-04-011610.3389/fimmu.2025.15831031583103Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancerYunxia Zhao0Li Li1Shuqi Yuan2Zixin Meng3Jiayi Xu4Zhaogen Cai5Yijing Zhang6Xiaonan Zhang7Tao Wang8Department of Pathophysiology, Bengbu Medical University, Longzihu, Bengbu, Anhui, ChinaDepartment of Pathology, Bengbu Medical University, Anqing 116 Hospital, Anqing, Anhui, ChinaDepartment of Rheumatology and Immunology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, ChinaSchool of Clinical Medicine, Bengbu Medical University, Longzihu, Bengbu, Anhui, ChinaSchool of Clinical Medicine, Bengbu Medical University, Longzihu, Bengbu, Anhui, ChinaDepartment of Pathology, Bengbu Medical University, Longzihu, Bengbu, Anhui, ChinaSchool of Clinical Medicine, Bengbu Medical University, Longzihu, Bengbu, Anhui, ChinaDepartment of Pathophysiology, Bengbu Medical University, Longzihu, Bengbu, Anhui, ChinaResearch Laboratory Center, Guizhou Provincial People’s Hospital, Nanming, Guiyang, Guizhou, ChinaBackgroundBreast cancer is the most common malignancy in women globally, with significant heterogeneity affecting prognosis and treatment. RNA-binding proteins play vital roles in tumor progression, yet their prognostic potential remains unclear. This study introduces an Artificial Intelligence-Assisted RBP Signature (AIRS) model to improve prognostic accuracy and guide personalized treatment.MethodsData from 14 BC cohorts (9,000+ patients) were analyzed using 108 machine learning model combinations. The AIRS model, built on three key RBP genes (PGK1, MPHOSPH10, MAP2K6), stratified patients into high- and low-risk groups. Genomic alterations, single-cell transcriptomics, tumor microenvironment characteristics, and drug sensitivity were assessed to uncover AIRS-associated mechanisms.ResultsThe AIRS model demonstrated superior prognostic performance, surpassing 106 established signatures. High AIRS scores correlated with elevated tumor mutational burden, specific copy number alterations, and an immune-suppressive TME. Single-cell analysis revealed functional heterogeneity in epithelial cells, linking high AIRS scores to pathways like transcription factor binding. Regulatory network analysis identified key transcription factors such as MYC. Low AIRS scores predicted better responses to immune checkpoint inhibitors, while drug sensitivity analysis highlighted panobinostat and paclitaxel as potential therapies for high-risk patients.ConclusionsThe AIRS model offers a robust tool for BC prognosis and treatment stratification, integrating genomic, transcriptomic, and single-cell data. It provides actionable insights for personalized therapy, paving the way for improved clinical outcomes. Future studies should validate findings across diverse populations and expand functional analyses.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1583103/fullbreast cancerRNA-binding proteinsprognostic modelpersonalized treatmentimmunotherapy |
| spellingShingle | Yunxia Zhao Li Li Shuqi Yuan Zixin Meng Jiayi Xu Zhaogen Cai Yijing Zhang Xiaonan Zhang Tao Wang Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer Frontiers in Immunology breast cancer RNA-binding proteins prognostic model personalized treatment immunotherapy |
| title | Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer |
| title_full | Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer |
| title_fullStr | Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer |
| title_full_unstemmed | Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer |
| title_short | Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer |
| title_sort | artificial intelligence assisted rna binding protein signature for prognostic stratification and therapeutic guidance in breast cancer |
| topic | breast cancer RNA-binding proteins prognostic model personalized treatment immunotherapy |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1583103/full |
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