SERS liquid biopsy in breast cancer. What can we learn from SERS on serum and urine?

Iancu, Ștefania D.; Cozan, Ramona G.; Ştefancu, Andrei; David, Maria Manuela; Moisoiu, Tudor; Moroz-Dubenco, Cristiana; Bajcsi, Adel; Chira, Camelia; Andreica, Anca; Leopold, Loredana; Eniu, D.; Staicu, Adelina; Goidescu, Iulian; Socaciu, Carmen; Eniu, Dan Tudor; Dioşan, Laura; Leopold, Nicolae

doi:10.1016/j.saa.2022.120992

Cited by 22 publications

(27 citation statements)

References 46 publications

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“…The most prominent spectral differences between the two groups regarded SERS bands attributed to purine metabolites (mainly uric acid and hypoxanthine) and creatinine (Additional file 1: Table S8). Higher intensities of the SERS bands at 644, 725, 1350 cm −1 (assigned to uric acid and hypoxanthine) in urine samples from cancer patients, compared to controls, were reported earlier (Phyo et al 2021;Mistro et al 2015;Iancu et al 2022). Indeed, higher levels of uric acid and hypoxanthine in samples from cancer patients, compared to controls were expected since cancer is associated with a rise in cellular turnover rate (Ridi and Tallima 2017) and xanthine oxidoreductase, which converts hypoxanthine to xanthine, is downregulated in cancer (Linder et al 2005).…”

Section: Combined Urine Mirna and Sers Profiling Can Accurately Disti...mentioning

confidence: 50%

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

Moisoiu

Dragomir

Iancu

et al. 2022

Mol Med

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Background Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine. Methods Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types. Results Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 ± 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 ± 0.03) or SERS data (AUC = 0.84 ± 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 ± 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 ± 0.04) or SERS (AUC = 0.92 ± 0.05) individually, although SERS alone performed better in terms of classification accuracy. Conclusion miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged.

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Section: Combined Urine Mirna and Sers Profiling Can Accurately Disti...mentioning

confidence: 50%

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

Moisoiu

Dragomir

Iancu

et al. 2022

Mol Med

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“…This was attributed to a mutation in the colon cancer group that reduced the activity of enzymes such as phenylalanine hydrogenase, which decreased the rate of protein degradation, allowing phenylalanine and its metabolites to accumulate in the body and cause disease, as has been successfully demonstrated in cancer research. Conversely, SERS bands at 494, 595, 810, 889, 1012, 1135 and 1208 cm −1 were less intense in the colon cancer group than in the normal group, mainly explained by abnormal base metabolism in colon cancer patients, resulting in reduced amino acid and sugar content [ 30 , 31 , 32 , 33 ].…”

Section: Resultsmentioning

confidence: 99%

“…It is concluded that our model has an overall prediction accuracy of 84.1%, with a model specificity of 89.3% and a sensitivity of 71.4%; the confusion matrix of prediction results in Figure 6 F. This is to say that 8 of the 28 spectra from the serum samples in the colon cancer groups were misclassified as controls and only 3 of the 28 spectra from the serum samples in the normal groups were misclassified as colon cancer. Thus, the differences in the SERS spectra of the colon cancer and normal groups and the misclassification of the PCA-LDA model indicated abnormal metabolic profiles and changes in the composition of serum proteins in the colon cancer groups [ 33 , 35 ].…”

Section: Resultsmentioning

confidence: 99%

Label-Free SERS Analysis of Serum Using Ag NPs/Cellulose Nanocrystal/Graphene Oxide Nanocomposite Film Substrate in Screening Colon Cancer

She

Wang

et al. 2023

Nanomaterials

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Label-free surface-enhanced Raman scattering (SERS) analysis shows tremendous potential for the early diagnosis and screening of colon cancer, owing to the advantage of being noninvasive and sensitive. As a clinical diagnostic tool, however, the reproducibility of analytical methods is a priority. Herein, we successfully fabricated Ag NPs/cellulose nanocrystals/graphene oxide (Ag NPs/CNC/GO) nanocomposite film as a uniform SERS active substrate for label-free SERS analysis of clinical serum. The Ag NPs/CNC/GO suspensions by self-assembling GO into CNC solution through in-situ reduction method. Furthermore, we spin-coated the prepared suspensions on the bacterial cellulose membrane (BCM) to form Ag NPs/CNC/GO nanocomposite film. The nanofilm showed excellent sensitivity (LOD = 30 nM) and uniformity (RSD = 14.2%) for Nile Blue A detection. With a proof-of-concept demonstration for the label-free analysis of serum, the nanofilm combined with the principal component analysis-linear discriminant analysis (PCA-LDA) model can be effectively employed for colon cancer screening. The results showed that our model had an overall prediction accuracy of 84.1% for colon cancer (n = 28) and the normal (n = 28), and the specificity and sensitivity were 89.3% and 71.4%, respectively. This study indicated that label-free serum SERS analysis based on Ag NPs/CNC/GO nanocomposite film combined with machine learning holds promise for the early diagnosis of colon cancer.

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“…PLS-LDA was also used by Zheng et al for the identification of breast-cancer biomarkers, using HAp [ 165 ]. PLS-SVM, PLS-LDA, and PCA-LDA were used for the classification of breast cancer and the normal group [ 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 ]. An exosome-based CNN model was developed for the classification of breast cancer and normal samples, with an accuracy of 95% [ 177 ].…”

Section: Sers For Disease Diagnosismentioning

confidence: 99%

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

2023

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Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS’s full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

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SERS liquid biopsy in breast cancer. What can we learn from SERS on serum and urine?

Cited by 22 publications

References 46 publications

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

Label-Free SERS Analysis of Serum Using Ag NPs/Cellulose Nanocrystal/Graphene Oxide Nanocomposite Film Substrate in Screening Colon Cancer

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

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