Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis
Breast cancer is a prevalent form of cancer worldwide, and the current standard screening method, mammography, often requires invasive biopsy procedures for further assessment. Recent research has explored microRNAs (miRNAs) in circulating blood as potential biomarkers for early breast cancer diagno...
| Main Authors: | , , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Online Access: | https://hdl.handle.net/10356/178545 |
| _version_ | 1811691543148363776 |
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| author | Zhang, Shuyan Wu, Steve Qing Yang Hum, Melissa Perumal, Jayakumar Tan, Ern Yu Lee, Ann Siew Gek Teng, Jinghua Dinish, U. S. Olivo, Malini |
| author2 | Lee Kong Chian School of Medicine (LKCMedicine) |
| author_facet | Lee Kong Chian School of Medicine (LKCMedicine) Zhang, Shuyan Wu, Steve Qing Yang Hum, Melissa Perumal, Jayakumar Tan, Ern Yu Lee, Ann Siew Gek Teng, Jinghua Dinish, U. S. Olivo, Malini |
| author_sort | Zhang, Shuyan |
| collection | NTU |
| description | Breast cancer is a prevalent form of cancer worldwide, and the current standard screening method, mammography, often requires invasive biopsy procedures for further assessment. Recent research has explored microRNAs (miRNAs) in circulating blood as potential biomarkers for early breast cancer diagnosis. In this study, we employed a multi-modal spectroscopy approach, combining attenuated total reflection Fourier transform infrared (ATR-FTIR) and surface-enhanced Raman scattering (SERS) to comprehensively characterize the full-spectrum fingerprints of RNA biomarkers in the blood serum of breast cancer patients. The sensitivity of conventional FTIR and Raman spectroscopy was enhanced by ATR-FTIR and SERS through the utilization of a diamond ATR crystal and silver-coated silicon nanopillars, respectively. Moreover, a wider measurement wavelength range was achieved with the multi-modal approach than with a single spectroscopic method alone. We have shown the results on 91 clinical samples, which comprised 44 malignant and 47 benign cases. Principal component analysis (PCA) was performed on the ATR-FTIR, SERS, and multi-modal data. From the peak analysis, we gained insights into biomolecular absorption and scattering-related features, which aid in the differentiation of malignant and benign samples. Applying 32 machine learning algorithms to the PCA results, we identified key molecular fingerprints and demonstrated that the multi-modal approach outperforms individual techniques, achieving higher average validation accuracy (95.1%), blind test accuracy (91.6%), specificity (94.7%), sensitivity (95.5%), and F-score (94.8%). The support vector machine (SVM) model showed the best area under the curve (AUC) characterization value of 0.9979, indicating excellent performance. These findings highlight the potential of the multi-modal spectroscopy approach as an accurate, reliable, and rapid method for distinguishing between malignant and benign breast tumors in women. Such a label-free approach holds promise for improving early breast cancer diagnosis and patient outcomes. |
| first_indexed | 2024-10-01T06:21:33Z |
| format | Journal Article |
| id | ntu-10356/178545 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:21:33Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1785452024-06-30T15:39:29Z Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis Zhang, Shuyan Wu, Steve Qing Yang Hum, Melissa Perumal, Jayakumar Tan, Ern Yu Lee, Ann Siew Gek Teng, Jinghua Dinish, U. S. Olivo, Malini Lee Kong Chian School of Medicine (LKCMedicine) Tan Tock Seng Hospital Medicine, Health and Life Sciences Breast cancer diagnosis Modal spectroscopy Breast cancer is a prevalent form of cancer worldwide, and the current standard screening method, mammography, often requires invasive biopsy procedures for further assessment. Recent research has explored microRNAs (miRNAs) in circulating blood as potential biomarkers for early breast cancer diagnosis. In this study, we employed a multi-modal spectroscopy approach, combining attenuated total reflection Fourier transform infrared (ATR-FTIR) and surface-enhanced Raman scattering (SERS) to comprehensively characterize the full-spectrum fingerprints of RNA biomarkers in the blood serum of breast cancer patients. The sensitivity of conventional FTIR and Raman spectroscopy was enhanced by ATR-FTIR and SERS through the utilization of a diamond ATR crystal and silver-coated silicon nanopillars, respectively. Moreover, a wider measurement wavelength range was achieved with the multi-modal approach than with a single spectroscopic method alone. We have shown the results on 91 clinical samples, which comprised 44 malignant and 47 benign cases. Principal component analysis (PCA) was performed on the ATR-FTIR, SERS, and multi-modal data. From the peak analysis, we gained insights into biomolecular absorption and scattering-related features, which aid in the differentiation of malignant and benign samples. Applying 32 machine learning algorithms to the PCA results, we identified key molecular fingerprints and demonstrated that the multi-modal approach outperforms individual techniques, achieving higher average validation accuracy (95.1%), blind test accuracy (91.6%), specificity (94.7%), sensitivity (95.5%), and F-score (94.8%). The support vector machine (SVM) model showed the best area under the curve (AUC) characterization value of 0.9979, indicating excellent performance. These findings highlight the potential of the multi-modal spectroscopy approach as an accurate, reliable, and rapid method for distinguishing between malignant and benign breast tumors in women. Such a label-free approach holds promise for improving early breast cancer diagnosis and patient outcomes. Agency for Science, Technology and Research (A*STAR) National Medical Research Council (NMRC) Published version The authors would like to acknowledge the funding support from: Agency for Science, Technology and Research, Singapore: IAF-PP Grant H19H6a0025 and BMRC UIBR Grant; National Medical Research Council, Singapore: NMRC/CBRG/0087/2015. 2024-06-25T08:22:27Z 2024-06-25T08:22:27Z 2024 Journal Article Zhang, S., Wu, S. Q. Y., Hum, M., Perumal, J., Tan, E. Y., Lee, A. S. G., Teng, J., Dinish, U. S. & Olivo, M. (2024). Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis. RSC Advances, 14(5), 3599-3610. https://dx.doi.org/10.1039/d3ra05723b 2046-2069 https://hdl.handle.net/10356/178545 10.1039/d3ra05723b 38264270 2-s2.0-85183833000 5 14 3599 3610 en H19H6a0025 NMRC/CBRG/0087/2015 RSC Advances © 2024 The Author(s). Published by the Royal Society of Chemistry. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. application/pdf |
| spellingShingle | Medicine, Health and Life Sciences Breast cancer diagnosis Modal spectroscopy Zhang, Shuyan Wu, Steve Qing Yang Hum, Melissa Perumal, Jayakumar Tan, Ern Yu Lee, Ann Siew Gek Teng, Jinghua Dinish, U. S. Olivo, Malini Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis |
| title | Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis |
| title_full | Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis |
| title_fullStr | Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis |
| title_full_unstemmed | Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis |
| title_short | Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis |
| title_sort | complete characterization of rna biomarker fingerprints using a multi modal atr ftir and sers approach for label free early breast cancer diagnosis |
| topic | Medicine, Health and Life Sciences Breast cancer diagnosis Modal spectroscopy |
| url | https://hdl.handle.net/10356/178545 |
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