Machine learning–assisted internal standard calibration label-free SERS strategy for colon cancer detection

Peng, Shirun; Lu, Dechan; Zhang, Bohan; You, Ruiyun; Xu, Huiyao; Lu, Yue

doi:10.1007/s00216-023-04566-1

Cited by 12 publications

(9 citation statements)

References 26 publications

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“…Main vibration peaks for the SERS of serum samples and their assignments using a 785 nm laser excitation line and the solid substrate. − [28] and ring vibration of L-arginine [29,30], guanine [30,31], ergothioneine [32], DNA [33] 480 Ergothioneine 496 Uric acid 593 589-592 amide-VI [30], glycerol [31] uric acid [20] Uric acid 640 637-650 ν(C-S) of tyrosine [28,30,31,[33][34][35] τ(C-C) of tyrosine [29] and phenylalanine [36], uric acid [20,22] Uric acid 727 720-725 Hypoxanthine [20,22,29,31,37,38], δ(CH) of adenine [30,36] Hypoxanthine 765 755-757 Tryptophan [34,35] Uric Acid 812 813-818 ν(C-C-O) of L-serine [28,30,31], ν(C-C) of collagen [34], gluthatione [30], uric acid [20,22] Uric acid 889 885-890 ν(C-O-H) of D-galactosamine [28,29,31,33,34,37], glutathione [30,…”

Section: Multivariate Analysismentioning

confidence: 99%

High-Accuracy Renal Cell Carcinoma Discrimination through Label-Free SERS of Blood Serum and Multivariate Analysis

et al. 2023

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Renal cell carcinoma (RCC) represents the sixth most frequently diagnosed cancer in men and is asymptomatic, being detected mostly incidentally. The apparition of symptoms correlates with advanced disease, aggressive histology, and poor outcomes. The development of the Surface-Enhanced Raman Scattering (SERS) technique opened the way for investigating and detecting small molecules, especially in biological liquids such as serum or blood plasma, urine, saliva, and tears, and was proposed as a simple technique for the diagnosis of various diseases, including cancer. In this study, we investigated the use of serum label-free SERS combined with two multivariate analysis tests: Principal Component Analysis combined with Linear Discriminate Analysis (PCA-LDA) and Supported Vector Machine (SVM) for the discrimination of 50 RCC cancer patients from 45 apparently healthy donors. In the case of LDA-PCA, we obtained a discrimination accuracy of 100% using 12 principal components and a quadratic discrimination function. The accuracy of discrimination between RCC stages was 88%. In the case of the SVM approach, we obtained a training accuracy of 100%, a validation accuracy of 92% for the discrimination between RCC and controls, and an accuracy of 81% for the discrimination between stages. We also performed standard statistical tests aimed at improving the assignment of the SERS vibration bands, which, according to our data, are mainly due to purinic metabolites (uric acid and hypoxanthine). Moreover, our results using these assignments and Student’s t-test suggest that the main differences in the SERS spectra of RCC patients are due to an increase in the uric acid concentration (a conclusion in agreement with recent literature), while the hypoxanthine concentration is not statistically different between the two groups. Our results demonstrate that label-free SERS combined with chemometrics holds great promise for non-invasive and early detection of RCC. However, more studies are needed to validate this approach, especially when combined with other urological diseases.

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Section: Multivariate Analysismentioning

confidence: 99%

High-Accuracy Renal Cell Carcinoma Discrimination through Label-Free SERS of Blood Serum and Multivariate Analysis

et al. 2023

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“…Raman spectroscopy and micro spectroscopy are widely used for real-time cancer diagnostics to identify cancer tissues in the surgical room . The relative intensities of various body fluids such as serum, urine, and saliva were compared to reveal specific biomolecules related to DNAs (DNAs)/ribonucleic acids (RNAs), proteins, sugars, and lipids to diagnose cancer with label-free SERS substrates. , Numerous studies have reported the use of label-free SERS-based biosensors to diagnose breast, ,− prostate, , lung, , and other cancer. ,,,, The rate of protein breakdown is reduced in cancer patients, accumulating phenylalanine and its metabolites in the body. Therefore, bands associated with enzyme activity have a higher intensity than those of the normal group, while the bands with an abnormality in base metabolism resulting in reduced amino acid and sugar contents have lower intensity than those of the normal group. , l -tyrosine at ∼639 cm –1 is an important biomarker to distinguish cancer from healthy controls.…”

Section: Label-free Sers Detectionmentioning

confidence: 99%

“…Various case studies and approaches provide a comprehensive understanding of multifaceted approaches to improving the sensitivity of label-free SERS substrates. We explored strategies aimed at enhancing the sensitivity of label-free SERS substrates ,,− and presented a detailed discussion of specific algorithms and the role of AI in early disease diagnosis based on SERS spectra which have bioinformation. − The insights into sensitivity enhancement techniques and AI integration presented in this review would contribute to the advancement of label-free SERS-based biosensors in biomedical diagnostics.…”

Section: Introductionmentioning

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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“…A label-free SERS, in combination with different machine learning algorithms, such as random forest, PCA-LDA, and decision trees, was used for the identification of colon cancer using serum samples. It was found that the random forest model outperformed the other two models in terms of accuracy and specificity [ 383 ]. SERS combined with ANN was used for the identification of different pollen samples despite many spectral contributions using Au NPs [ 384 ].…”

Section: Machine Learning In Sers-based Biosensingmentioning

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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Machine learning–assisted internal standard calibration label-free SERS strategy for colon cancer detection

Cited by 12 publications

References 26 publications

High-Accuracy Renal Cell Carcinoma Discrimination through Label-Free SERS of Blood Serum and Multivariate Analysis

High-Accuracy Renal Cell Carcinoma Discrimination through Label-Free SERS of Blood Serum and Multivariate Analysis

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

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

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