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

doi:10.1039/d3ra05723b

Cited by 4 publications

(2 citation statements)

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“…A support vector machine (SVM) model, an effective classification algorithm, was utilized for disease diagnosis such as AD, , cancer, ,− and other diseases ,, based on SERS spectra. Andrea et al used the t -distributed stochastic neighbor embedding algorithm to visualize the SERS spectra of the end products of the 2D seed amplification assay .…”

Section: Artificial Intelligence-based Detectionmentioning

confidence: 99%

Recent Trends in Surface-Enhanced Raman Spectroscopy-Based Biosensors: Label-Free Early Disease Diagnosis

Jin,

Lednev,

Jung

2024

J. Phys. Chem. C

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The recent advances in surface-enhanced Raman spectroscopy (SERS) have developed label-free biosensors for early diagnosis of diseases. This review explores the recent trends of SERS-based biosensors, focusing on their potential applications for the rapid and accurate detection of diseases without labeling agents. The integration of SERS with biosensor platforms provides a powerful approach to enhance sensitivity and selectivity in the detection of biomolecules associated with diseases. The integration of artificial intelligence to optimize data analysis and improve diagnostic accuracy is discussed. This study provides valuable insight into the current landscape of SERS-based biosensors for label-free early diagnosis of diseases by summarizing key studies and innovations.

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Section: Artificial Intelligence-based Detectionmentioning

confidence: 99%

Recent Trends in Surface-Enhanced Raman Spectroscopy-Based Biosensors: Label-Free Early Disease Diagnosis

Jin,

Lednev,

Jung

2024

J. Phys. Chem. C

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“…Furthermore, Zhang ( 124 ) et al. presented an innovative multimodal spectral approach for early diagnosis of breast cancer using attenuated total reflection Fourier Transform infrared spectroscopy (ATR-FTIR) and SERS data.…”

Section: Sers Detection Of Tumor Biomarkersmentioning

confidence: 99%

Recent progress in Surface-Enhanced Raman Spectroscopy detection of biomarkers in liquid biopsy for breast cancer

Liu,

Jia,

Zheng

2024

Front. Oncol.

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Breast cancer is the most commonly diagnosed cancer in women globally and a leading cause of cancer-related mortality. However, current detection methods, such as X-rays, ultrasound, CT scans, MRI, and mammography, have their limitations. Recently, with the advancements in precision medicine and technologies like artificial intelligence, liquid biopsy, specifically utilizing Surface-Enhanced Raman Spectroscopy (SERS), has emerged as a promising approach to detect breast cancer. Liquid biopsy, as a minimally invasive technique, can provide a temporal reflection of breast cancer occurrence and progression, along with a spatial representation of overall tumor information. SERS has been extensively employed for biomarker detection, owing to its numerous advantages such as high sensitivity, minimal sample requirements, strong multi-detection ability, and controllable background interference. This paper presents a comprehensive review of the latest research on the application of SERS in the detection of breast cancer biomarkers, including exosomes, circulating tumor cells (CTCs), miRNA, proteins and others. The aim of this review is to provide valuable insights into the potential of SERS technology for early breast cancer diagnosis.

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Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections

Delrue,

Hofmans,

Van Dorpe

et al. 2024

Commun Biol

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Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis

Abstract: With the multi-modal approach combining ATR-FTIR and SERS, we achieved an extended spectral range for molecular fingerprint detection of RNA biomarkers. Machine learning results shows 91.6% blind test accuracy for label-free breast cancer diagnosis.

Cited by 4 publications

References 50 publications

Recent Trends in Surface-Enhanced Raman Spectroscopy-Based Biosensors: Label-Free Early Disease Diagnosis

Recent Trends in Surface-Enhanced Raman Spectroscopy-Based Biosensors: Label-Free Early Disease Diagnosis

Recent progress in Surface-Enhanced Raman Spectroscopy detection of biomarkers in liquid biopsy for breast cancer

Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections

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