RETRACTED ARTICLE: Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients
Abstract Background Breast cancer remains the leading malignant neoplasm among women globally, posing significant challenges in terms of treatment and prognostic evaluation. The metabolic pathway of polyamines is crucial in breast cancer progression, with a strong association to the increased capabi...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-10-01
|
| Series: | Journal of Cancer Research and Clinical Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s00432-024-06001-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823863415518527488 |
|---|---|
| author | Xiliang Zhang Hanjie Guo Xiaolong Li Wei Tao Xiaoqing Ma Yuxing Zhang Weidong Xiao |
| author_facet | Xiliang Zhang Hanjie Guo Xiaolong Li Wei Tao Xiaoqing Ma Yuxing Zhang Weidong Xiao |
| author_sort | Xiliang Zhang |
| collection | DOAJ |
| description | Abstract Background Breast cancer remains the leading malignant neoplasm among women globally, posing significant challenges in terms of treatment and prognostic evaluation. The metabolic pathway of polyamines is crucial in breast cancer progression, with a strong association to the increased capabilities of tumor cells for proliferation, invasion, and metastasis. Methods We used a multi-omics approach combining bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) to study polyamine metabolism. Data from The Cancer Genome Atlas, Gene Expression Omnibus, and Genotype-Tissue Expression identified 286 differentially expressed genes linked to polyamine pathways in breast cancer. These genes were analyzed using univariate COX and machine learning algorithms to develop a prognostic scoring algorithm. Single-cell RNA sequencing validated the model by examining gene expression heterogeneity at the cellular level. Results Our single-cell analyses revealed distinct subpopulations with different expressions of genes related to polyamine metabolism, highlighting the heterogeneity of the tumor microenvironment. The SuperPC model (a constructed risk score) demonstrated high accuracy when predicting patient outcomes. The immune profiling and functional enrichment analyses revealed that the genes identified play a crucial role in cell cycle control and immune modulation. Single-cell validation confirmed that polyamine metabolism genes were present in specific cell clusters. This highlights their potential as therapeutic targets. Conclusions This study integrates single cell omics with machine-learning to develop a robust scoring model for breast cancer based on polyamine metabolic pathways. Our findings offer new insights into tumor heterogeneity, and a novel framework to personalize prognosis. Single-cell technologies are being used in this context to enhance our understanding of the complex molecular terrain of breast cancer and support more effective clinical management. |
| format | Article |
| id | doaj-art-2fb797c72fdf47a7821634ea1f4329aa |
| 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-2fb797c72fdf47a7821634ea1f4329aa2025-02-09T12:10:04ZengSpringerJournal of Cancer Research and Clinical Oncology1432-13352024-10-011501012010.1007/s00432-024-06001-zRETRACTED ARTICLE: Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patientsXiliang Zhang0Hanjie Guo1Xiaolong Li2Wei Tao3Xiaoqing Ma4Yuxing Zhang5Weidong Xiao6Department of General Surgery, Xinqiao Hospital, Army Medical UniversityDepartment of General Surgery, School of Medicine, South China University of TechnologyDepartment of General Surgery, Xinqiao Hospital, Army Medical UniversityDepartment of General Surgery, Xinqiao Hospital, Army Medical UniversityDepartment of General Surgery, School of Medicine, South China University of TechnologyDepartment of General Surgery, The Sixth Medical Center of PLA General HospitalDepartment of General Surgery, Xinqiao Hospital, Army Medical UniversityAbstract Background Breast cancer remains the leading malignant neoplasm among women globally, posing significant challenges in terms of treatment and prognostic evaluation. The metabolic pathway of polyamines is crucial in breast cancer progression, with a strong association to the increased capabilities of tumor cells for proliferation, invasion, and metastasis. Methods We used a multi-omics approach combining bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) to study polyamine metabolism. Data from The Cancer Genome Atlas, Gene Expression Omnibus, and Genotype-Tissue Expression identified 286 differentially expressed genes linked to polyamine pathways in breast cancer. These genes were analyzed using univariate COX and machine learning algorithms to develop a prognostic scoring algorithm. Single-cell RNA sequencing validated the model by examining gene expression heterogeneity at the cellular level. Results Our single-cell analyses revealed distinct subpopulations with different expressions of genes related to polyamine metabolism, highlighting the heterogeneity of the tumor microenvironment. The SuperPC model (a constructed risk score) demonstrated high accuracy when predicting patient outcomes. The immune profiling and functional enrichment analyses revealed that the genes identified play a crucial role in cell cycle control and immune modulation. Single-cell validation confirmed that polyamine metabolism genes were present in specific cell clusters. This highlights their potential as therapeutic targets. Conclusions This study integrates single cell omics with machine-learning to develop a robust scoring model for breast cancer based on polyamine metabolic pathways. Our findings offer new insights into tumor heterogeneity, and a novel framework to personalize prognosis. Single-cell technologies are being used in this context to enhance our understanding of the complex molecular terrain of breast cancer and support more effective clinical management.https://doi.org/10.1007/s00432-024-06001-zBreast cancerPolyamine metabolismRisk scoring modelMachine learning |
| spellingShingle | Xiliang Zhang Hanjie Guo Xiaolong Li Wei Tao Xiaoqing Ma Yuxing Zhang Weidong Xiao RETRACTED ARTICLE: Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients Journal of Cancer Research and Clinical Oncology Breast cancer Polyamine metabolism Risk scoring model Machine learning |
| title | RETRACTED ARTICLE: Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients |
| title_full | RETRACTED ARTICLE: Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients |
| title_fullStr | RETRACTED ARTICLE: Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients |
| title_full_unstemmed | RETRACTED ARTICLE: Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients |
| title_short | RETRACTED ARTICLE: Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients |
| title_sort | retracted article single cell omics and machine learning integration to develop a polyamine metabolism based risk score model in breast cancer patients |
| topic | Breast cancer Polyamine metabolism Risk scoring model Machine learning |
| url | https://doi.org/10.1007/s00432-024-06001-z |
| work_keys_str_mv | AT xiliangzhang retractedarticlesinglecellomicsandmachinelearningintegrationtodevelopapolyaminemetabolismbasedriskscoremodelinbreastcancerpatients AT hanjieguo retractedarticlesinglecellomicsandmachinelearningintegrationtodevelopapolyaminemetabolismbasedriskscoremodelinbreastcancerpatients AT xiaolongli retractedarticlesinglecellomicsandmachinelearningintegrationtodevelopapolyaminemetabolismbasedriskscoremodelinbreastcancerpatients AT weitao retractedarticlesinglecellomicsandmachinelearningintegrationtodevelopapolyaminemetabolismbasedriskscoremodelinbreastcancerpatients AT xiaoqingma retractedarticlesinglecellomicsandmachinelearningintegrationtodevelopapolyaminemetabolismbasedriskscoremodelinbreastcancerpatients AT yuxingzhang retractedarticlesinglecellomicsandmachinelearningintegrationtodevelopapolyaminemetabolismbasedriskscoremodelinbreastcancerpatients AT weidongxiao retractedarticlesinglecellomicsandmachinelearningintegrationtodevelopapolyaminemetabolismbasedriskscoremodelinbreastcancerpatients |