RETRACTED ARTICLE: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6
Abstract Background Breast cancer is a significant public health issue worldwide, being the most prevalent cancer among women and a leading cause of death related to this disease. The molecular processes that propel breast cancer progression are not fully elucidated, highlighting the intricate natur...
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| Format: | Article |
| Language: | English |
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Springer
2024-10-01
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| Series: | Journal of Cancer Research and Clinical Oncology |
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| Online Access: | https://doi.org/10.1007/s00432-024-05995-w |
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| author | Lizhe Wang Yu Wang Yueyang Li Li Zhou Sihan Liu Yongyi Cao Yuzhi Li Shenting Liu Jiahui Du Jin Wang Ting Zhu |
| author_facet | Lizhe Wang Yu Wang Yueyang Li Li Zhou Sihan Liu Yongyi Cao Yuzhi Li Shenting Liu Jiahui Du Jin Wang Ting Zhu |
| author_sort | Lizhe Wang |
| collection | DOAJ |
| description | Abstract Background Breast cancer is a significant public health issue worldwide, being the most prevalent cancer among women and a leading cause of death related to this disease. The molecular processes that propel breast cancer progression are not fully elucidated, highlighting the intricate nature of the underlying biology and its crucial impact on global health. The objective of this research was to perform bioinformatics analyses on breast cancer-related datasets to gain a comprehensive understanding of the molecular mechanisms at play and to identify key genes associated with the disease. Methods The toolkit analyses involve techniques such as differential gene expression analysis, Gene Set Enrichment Analysis (GSEA), Weighted Co-Expression Network Analysis (WGCNA), and Machine Learning algorithms. Furthermore, in vitro cell experiments have demonstrated the impact of HSPB6 on cell migration, proliferation, and apoptosis. Results The study identified multiple genes that displayed differential expression in breast cancer, notably FHL1 and HSPB6. A machine learning model was developed in this study and specifically trained for breast cancer diagnosis using these genes, achieving high precision. Furthermore, analysis of immune cell infiltration revealed an enrichment of Tregs and M2 macrophages in the treated group, showcasing its significant impact on the tumor’s immunological context. A temporal analysis of breast cancer cells using single-cell RNA sequencing provided insights into cellular developmental trajectories and highlighted changes in expression patterns across key genes during disease progression. The upregulation of HSPB6 in MCF7 cells significantly inhibited both cell migration and proliferation abilities, suggesting that promoting HSPB6 expression could induce ferroptosis in breast cancer cells. Conclusion Our findings have identified compelling molecular targets and distinctive diagnostic markers for the clinical management of breast cancer. This data will serve as crucial guidance for further research in the field. |
| format | Article |
| id | doaj-art-7bdfe61d69dc43529acf57639ac2a525 |
| institution | Kabale University |
| issn | 1432-1335 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of Cancer Research and Clinical Oncology |
| spelling | doaj-art-7bdfe61d69dc43529acf57639ac2a5252025-02-09T12:10:03ZengSpringerJournal of Cancer Research and Clinical Oncology1432-13352024-10-011501011610.1007/s00432-024-05995-wRETRACTED ARTICLE: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6Lizhe Wang0Yu Wang1Yueyang Li2Li Zhou3Sihan Liu4Yongyi Cao5Yuzhi Li6Shenting Liu7Jiahui Du8Jin Wang9Ting Zhu10Department of Oncology, The Third Affiliated Hospital of Anhui Medical University, Hefei First People’s HospitalDepartment of Oncology, The Third Affiliated Hospital of Anhui Medical University, Hefei First People’s HospitalDepartment of Oncology, The Third Affiliated Hospital of Anhui Medical University, Hefei First People’s HospitalDepartment of Oncology, The Third Affiliated Hospital of Anhui Medical University, Hefei First People’s HospitalDepartment of Oncology, The Third Affiliated Hospital of Anhui Medical University, Hefei First People’s HospitalDepartment of Oncology, The Third Affiliated Hospital of Anhui Medical University, Hefei First People’s HospitalDepartment of Oncology, The Third Affiliated Hospital of Anhui Medical University, Hefei First People’s HospitalDepartment of Oncology, Wuhu Second People’s HospitalDepartment of Oncology, The Third Affiliated Hospital of Anhui Medical University, Hefei First People’s HospitalDepartment of Oncology, The Third Affiliated Hospital of Anhui Medical University, Hefei First People’s HospitalDepartment of Oncology, The Third Affiliated Hospital of Anhui Medical University, Hefei First People’s HospitalAbstract Background Breast cancer is a significant public health issue worldwide, being the most prevalent cancer among women and a leading cause of death related to this disease. The molecular processes that propel breast cancer progression are not fully elucidated, highlighting the intricate nature of the underlying biology and its crucial impact on global health. The objective of this research was to perform bioinformatics analyses on breast cancer-related datasets to gain a comprehensive understanding of the molecular mechanisms at play and to identify key genes associated with the disease. Methods The toolkit analyses involve techniques such as differential gene expression analysis, Gene Set Enrichment Analysis (GSEA), Weighted Co-Expression Network Analysis (WGCNA), and Machine Learning algorithms. Furthermore, in vitro cell experiments have demonstrated the impact of HSPB6 on cell migration, proliferation, and apoptosis. Results The study identified multiple genes that displayed differential expression in breast cancer, notably FHL1 and HSPB6. A machine learning model was developed in this study and specifically trained for breast cancer diagnosis using these genes, achieving high precision. Furthermore, analysis of immune cell infiltration revealed an enrichment of Tregs and M2 macrophages in the treated group, showcasing its significant impact on the tumor’s immunological context. A temporal analysis of breast cancer cells using single-cell RNA sequencing provided insights into cellular developmental trajectories and highlighted changes in expression patterns across key genes during disease progression. The upregulation of HSPB6 in MCF7 cells significantly inhibited both cell migration and proliferation abilities, suggesting that promoting HSPB6 expression could induce ferroptosis in breast cancer cells. Conclusion Our findings have identified compelling molecular targets and distinctive diagnostic markers for the clinical management of breast cancer. This data will serve as crucial guidance for further research in the field.https://doi.org/10.1007/s00432-024-05995-wBreast cancerThe immunocyte-infiltrating feature of gene expression differencesWeighted gene co-expression network analysisMachine learning model |
| spellingShingle | Lizhe Wang Yu Wang Yueyang Li Li Zhou Sihan Liu Yongyi Cao Yuzhi Li Shenting Liu Jiahui Du Jin Wang Ting Zhu RETRACTED ARTICLE: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6 Journal of Cancer Research and Clinical Oncology Breast cancer The immunocyte-infiltrating feature of gene expression differences Weighted gene co-expression network analysis Machine learning model |
| title | RETRACTED ARTICLE: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6 |
| title_full | RETRACTED ARTICLE: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6 |
| title_fullStr | RETRACTED ARTICLE: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6 |
| title_full_unstemmed | RETRACTED ARTICLE: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6 |
| title_short | RETRACTED ARTICLE: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6 |
| title_sort | retracted article a prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in hspb6 |
| topic | Breast cancer The immunocyte-infiltrating feature of gene expression differences Weighted gene co-expression network analysis Machine learning model |
| url | https://doi.org/10.1007/s00432-024-05995-w |
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