A comparative study based on serum SERS spectra in and on the coffee ring for high precision breast cancer detection

Lin, Xueliang; Jia, Xianggang; Lin, Jin; Wu, Ping Hui; Weng, Youliang; Feng, Shouhua

doi:10.1002/jrs.6382

Cited by 13 publications

(8 citation statements)

References 35 publications

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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%

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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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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“…The 19 eligible articles [ 4,6–23 ] were contained in this meta‐analysis with 1299 patients in total. These articles were published from 2015 to 2022.…”

Section: Resultsmentioning

confidence: 99%

“…As a new way to diagnose breast cancer, many studies have shown that SERS was an accurate and effective diagnostic method. [ 4,6–23 ] However, there was no comprehensive review conducted to assess the accuracy of the diagnosis. The meta‐analysis was the first study to evaluate the diagnostic value of SERS in the diagnosis of breast cancer.…”

Section: Discussionmentioning

confidence: 99%

Surface‐enhanced Raman spectroscopy is an accurate technique in diagnosis of breast cancer: A meta‐analysis

Yue

Wang

Guo

et al. 2022

J Raman Spectroscopy

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The meta-analysis was prepared to evaluate the diagnostic value of surface-enhanced Raman spectroscopy for patients with breast cancer and to provide some statistically significant reference for the detection process. PubMed, Web of Science, Cochrane library, and Embase databases were retrieved for randomized clinical trials by two independent reviewers. A comprehensive search was performed on July 15, 2022. Meta-Disc 1.4 was used to calculate Spearman correlation coefficient. STATA 14.1 software was used to evaluate the diagnostic effect and the performance of subgroup analysis, which could be conducted to explore the potential sources of heterogeneity. Review manager 5.3 software was performed for quality assessment in our meta-analysis. The results displayed 19 studies with 1299 patients were included according to the inclusion and exclusion criteria. Meta-analysis displayed the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and the area under the curve of using surface enhanced Raman spectroscopy for diagnosing the patients with breast cancer were 0.91 (95% CI 0.85-0.94), 0.94 (95% CI 0.91-0.96), 14.06 (95% CI 9.25-21.35), 0.10 (95% CI 0.06-0.17), ), and 0.97 (95% CI 0.95-0.98), respectively. The source of specificity heterogeneity was from different sample volume mixed with nanoparticle. Surface-enhanced Raman spectroscopy had high sensitivity and specificity in the diagnosis of breast cancer. It was an accurate and effective technique for patients with breast 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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A comparative study based on serum SERS spectra in and on the coffee ring for high precision breast cancer detection

Cited by 13 publications

References 35 publications

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

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

Surface‐enhanced Raman spectroscopy is an accurate technique in diagnosis of breast cancer: A meta‐analysis

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

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