Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease states
Abstract Prostate cancer is a disease which poses an interesting clinical question: Should it be treated? Only a small subset of prostate cancers are aggressive and require removal and treatment to prevent metastatic spread. However, conventional diagnostics remain challenged to risk-stratify such p...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-83708-6 |
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| author | Daniel West Susan Stepney Y. Hancock |
| author_facet | Daniel West Susan Stepney Y. Hancock |
| author_sort | Daniel West |
| collection | DOAJ |
| description | Abstract Prostate cancer is a disease which poses an interesting clinical question: Should it be treated? Only a small subset of prostate cancers are aggressive and require removal and treatment to prevent metastatic spread. However, conventional diagnostics remain challenged to risk-stratify such patients; hence, new methods of approach to biomolecularly sub-classify the disease are needed. Here we use an unsupervised self-organising map approach to analyse live-cell Raman spectroscopy data obtained from prostate cell-lines; our aim is to exemplify this method to sub-stratify, at the single-cell-level, the cancer disease state using high-dimensional datasets with minimal preprocessing. The results demonstrate a new sub-clustering of the prostate cancer cell-line into two groups—protein-rich and lipid-rich sub-cellular components—which we believe to be mechanistically linked. This finding shows the potential for unsupervised machine learning to discover distinct disease-state features for more accurate characterisation of highly heterogeneous prostate cancer. Applications may lead to more targeted diagnoses, prognoses and clinical treatment decisions via molecularly-informed stratification that would benefit patients. A method that could discover distinct disease-state features that are mechanistically linked could also assist in the development of more effective broad-spectrum treatments that simultaneously target linked disease-state processes. |
| format | Article |
| id | doaj-art-bcf16621b47243739d5b77b633caff42 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bcf16621b47243739d5b77b633caff422025-01-05T12:19:40ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-024-83708-6Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease statesDaniel West0Susan Stepney1Y. Hancock2Department of Computer Science, University of YorkDepartment of Computer Science, University of YorkSchool of Physics, Engineering and Technology, University of YorkAbstract Prostate cancer is a disease which poses an interesting clinical question: Should it be treated? Only a small subset of prostate cancers are aggressive and require removal and treatment to prevent metastatic spread. However, conventional diagnostics remain challenged to risk-stratify such patients; hence, new methods of approach to biomolecularly sub-classify the disease are needed. Here we use an unsupervised self-organising map approach to analyse live-cell Raman spectroscopy data obtained from prostate cell-lines; our aim is to exemplify this method to sub-stratify, at the single-cell-level, the cancer disease state using high-dimensional datasets with minimal preprocessing. The results demonstrate a new sub-clustering of the prostate cancer cell-line into two groups—protein-rich and lipid-rich sub-cellular components—which we believe to be mechanistically linked. This finding shows the potential for unsupervised machine learning to discover distinct disease-state features for more accurate characterisation of highly heterogeneous prostate cancer. Applications may lead to more targeted diagnoses, prognoses and clinical treatment decisions via molecularly-informed stratification that would benefit patients. A method that could discover distinct disease-state features that are mechanistically linked could also assist in the development of more effective broad-spectrum treatments that simultaneously target linked disease-state processes.https://doi.org/10.1038/s41598-024-83708-6 |
| spellingShingle | Daniel West Susan Stepney Y. Hancock Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease states Scientific Reports |
| title | Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease states |
| title_full | Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease states |
| title_fullStr | Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease states |
| title_full_unstemmed | Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease states |
| title_short | Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease states |
| title_sort | unsupervised self organising map classification of raman spectra from prostate cell lines uncovers substratified prostate cancer disease states |
| url | https://doi.org/10.1038/s41598-024-83708-6 |
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