Identification of Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related Genes
Objectives: The TGF-β signaling pathway is widely acknowledged for its role in various aspects of cancer progression, including cellular invasion, epithelial-mesenchymal transition, and immunosuppression. Immune checkpoint inhibitors (ICIs) and pharmacological agents that target TGF-β offer signific...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-02-01
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| Series: | Cancer Informatics |
| Online Access: | https://doi.org/10.1177/11769351251316398 |
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| author | Jia Qu Mei-Huan Wang Yue-Hua Gao Hua-Wei Zhang |
| author_facet | Jia Qu Mei-Huan Wang Yue-Hua Gao Hua-Wei Zhang |
| author_sort | Jia Qu |
| collection | DOAJ |
| description | Objectives: The TGF-β signaling pathway is widely acknowledged for its role in various aspects of cancer progression, including cellular invasion, epithelial-mesenchymal transition, and immunosuppression. Immune checkpoint inhibitors (ICIs) and pharmacological agents that target TGF-β offer significant potential as therapeutic options for cancer. However, the specific role of TGF-β in prognostic assessment and treatment strategies for breast cancer (BC) remains unclear. Methods: The Cancer Genome Atlas (TCGA) database was utilized to develop a predictive model incorporating five TGF-β signaling-related genes (TSRGs). The GSE161529 dataset from the Gene Expression Omnibus was employed to conduct single-cell analyses aimed at further elucidating the characteristics of these TSRGs. Additionally, an unsupervised clustering algorithm was applied to categorize BC patients into two distinct groups based on the five TSRGs, with a focus on immune response and overall survival (OS). Further investigations were conducted to explore variations in pharmacotherapy and the tumor microenvironment across different patient cohorts and clusters. Results: The predictive model for BC identified five TSRGs: FUT8, IFNG, ID3, KLF10, and PARD6A. Single-cell analysis revealed that IFNG is predominantly expressed in CD8+ T cells. Consensus clustering effectively categorized BC patients into two distinct clusters, with cluster B demonstrating a longer OS and a more favorable prognosis. Immunological assessments indicated a higher presence of immune checkpoints and immune cells in cluster B, suggesting a greater likelihood of responsiveness to ICIs. Conclusion: The findings of this study highlight the potential of the TGF-β signaling pathway for prognostic classification and the development of personalized treatment strategies for BC patients, thereby enhancing our understanding of its significance in BC prognosis. |
| format | Article |
| id | doaj-art-9c6d2bc9deeb4d93933aa4f26694e68c |
| institution | Kabale University |
| issn | 1176-9351 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj-art-9c6d2bc9deeb4d93933aa4f26694e68c2025-02-03T10:03:27ZengSAGE PublishingCancer Informatics1176-93512025-02-012410.1177/11769351251316398Identification of Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related GenesJia QuMei-Huan WangYue-Hua GaoHua-Wei ZhangObjectives: The TGF-β signaling pathway is widely acknowledged for its role in various aspects of cancer progression, including cellular invasion, epithelial-mesenchymal transition, and immunosuppression. Immune checkpoint inhibitors (ICIs) and pharmacological agents that target TGF-β offer significant potential as therapeutic options for cancer. However, the specific role of TGF-β in prognostic assessment and treatment strategies for breast cancer (BC) remains unclear. Methods: The Cancer Genome Atlas (TCGA) database was utilized to develop a predictive model incorporating five TGF-β signaling-related genes (TSRGs). The GSE161529 dataset from the Gene Expression Omnibus was employed to conduct single-cell analyses aimed at further elucidating the characteristics of these TSRGs. Additionally, an unsupervised clustering algorithm was applied to categorize BC patients into two distinct groups based on the five TSRGs, with a focus on immune response and overall survival (OS). Further investigations were conducted to explore variations in pharmacotherapy and the tumor microenvironment across different patient cohorts and clusters. Results: The predictive model for BC identified five TSRGs: FUT8, IFNG, ID3, KLF10, and PARD6A. Single-cell analysis revealed that IFNG is predominantly expressed in CD8+ T cells. Consensus clustering effectively categorized BC patients into two distinct clusters, with cluster B demonstrating a longer OS and a more favorable prognosis. Immunological assessments indicated a higher presence of immune checkpoints and immune cells in cluster B, suggesting a greater likelihood of responsiveness to ICIs. Conclusion: The findings of this study highlight the potential of the TGF-β signaling pathway for prognostic classification and the development of personalized treatment strategies for BC patients, thereby enhancing our understanding of its significance in BC prognosis.https://doi.org/10.1177/11769351251316398 |
| spellingShingle | Jia Qu Mei-Huan Wang Yue-Hua Gao Hua-Wei Zhang Identification of Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related Genes Cancer Informatics |
| title | Identification of Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related Genes |
| title_full | Identification of Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related Genes |
| title_fullStr | Identification of Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related Genes |
| title_full_unstemmed | Identification of Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related Genes |
| title_short | Identification of Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related Genes |
| title_sort | identification of molecular subtypes and prognostic features of breast cancer based on tgf β signaling related genes |
| url | https://doi.org/10.1177/11769351251316398 |
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