The convergence of FTIR and EVs: Emergence strategy for non-invasive cancer markers discovery

Wong, Le-Wei and Mak, Siow-Hui and Goh, Bey-Hing and Lee, Wai-Leng (2023) The convergence of FTIR and EVs: Emergence strategy for non-invasive cancer markers discovery. Diagnostics, 13 (1). ISSN 2075-4418, DOI

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In conjunction with imaging analysis, pathology-based assessments of biopsied tissue are the gold standard for diagnosing solid tumors. However, the disadvantages of tissue biopsies, such as being invasive, time-consuming, and labor-intensive, have urged the development of an alternate method, liquid biopsy, that involves sampling and clinical assessment of various bodily fluids for cancer diagnosis. Meanwhile, extracellular vesicles (EVs) are circulating biomarkers that carry molecular profiles of their cell or tissue origins and have emerged as one of the most promising biomarkers for cancer. Owing to the biological information that can be obtained through EVs' membrane surface markers and their cargo loaded with biomolecules such as nucleic acids, proteins, and lipids, EVs have become useful in cancer diagnosis and therapeutic applications. Fourier-transform infrared spectroscopy (FTIR) allows rapid, non-destructive, label-free molecular profiling of EVs with minimal sample preparation. Since the heterogeneity of EV subpopulations may result in complicated FTIR spectra that are highly diverse, computational-assisted FTIR spectroscopy is employed in many studies to provide fingerprint spectra of malignant and non-malignant samples, allowing classification with high accuracy, specificity, and sensitivity. In view of this, FTIR-EV approach carries a great potential in cancer detection. The progression of FTIR-based biomarker identification in EV research, the rationale of the integration of a computationally assisted approach, along with the challenges of clinical translation are the focus of this review.

Item Type: Article
Funders: Monash University Malaysia School of Pharmacy's Pilot Research Grant (SOP/SRG-Pilot/02/2022), School of Science's Strategic Funding Scheme 2022 (STG-000125), European Union (EU) (HORIZON-MSCA-2021-SE-01-01)
Uncontrolled Keywords: extracellular vesicles; infrared spectroscopy; FTIR; biomarker; cancer detection; machine learning; chemometrics; automated diagnosis
Subjects: R Medicine > R Medicine (General)
Divisions: Faculty of Medicine > Medicine Department
Depositing User: Ms Zaharah Ramly
Date Deposited: 22 Nov 2023 07:35
Last Modified: 22 Nov 2023 07:35

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