Label-free detection of bladder cancer and kidney cancer plasma based on SERS and multivariate statistical algorithm

Bai, Xin; Lin, Juqiang; Wu, Xiang; Lin, Yamin; Zhao, Xin; Du, Weiwei; Gao, Jian; Hu, Zeqin; Xu, Qingjiang; Li, Tao; Yu, Yun

doi:10.1016/j.saa.2022.121336

Cited by 19 publications

(11 citation statements)

References 42 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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“…ResNet, [ 115] ANN, [ 116] CNN, [117][118][119][120][121] PCA [ 122] Quantify the abundance of certain molecules RF, [44] PCA+LR, [123] ANN, [ 47,[123][124][125][126] CNN, [127][128][129][130] PCA+SVM, [ 131] PLS, [124,125] SVR, [124] PLS+GA, [132] SVM [ 133] Discover the multiplexed variation in the whole profile PCA, [ 134] Autoencoder, [ 135] CNN, [ 136,137] PCA+LDA [138] Early disease diagnosis ResNet, [ 115] RF, [139] KNN, [ 139] naïve Bayes, [ 139] PLS+SVM [140] SERS spectrum with microRNAs Early disease diagnosis RF, [ 141] LR, [141] naïve Bayes [ 141] Covariance matrices of SERS spectrum…”

Section: Molecular Graphmentioning

confidence: 99%

“…RF, naïve Bayes, and other explainable algorithms have been applied to distinguish diseased cases from the controls, combining the screening of important spectral features for the excavation of potential biomarkers (e.g., uric acid, hypoxanthine, arginine, etc.). [139][140][141] Even pan-cancer diagnosis can be achieved with only single SERS test of the serum samples that an integrative method was established called SERS and Artificial Intelligence for Cancer Screening (SERS-AICS) diagnosis, using SVM for both multi-cancer diagnosis and biomarker discovery based on the SERS bands screened with the importance from classification [142] (Figure 6d). However, the utility of such models relies on the training dataset which is required to cover a wide range of populations, since label-free detection of the biofluids is quite vulnerable to the individual differences including environmental stimuli, genetic factors among the others.…”

Section: Ai For Sers-based Applicationsmentioning

confidence: 99%

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

Bi,

Lin,

Chen

et al. 2023

Small Methods

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Surface‐enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever‐sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high‐level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above‐mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

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“…[ 27 ] Machine learning approaches are powerful tools to classify different groups by analyzing large SERS data sets by finding minimum variation within the groups while exploring maximum variation between the groups. [ 28–31 ]…”

Section: Introductionmentioning

confidence: 99%

Characterization and discrimination of spike protein in SARS‐CoV‐2 virus‐like particles via surface‐enhanced Raman spectroscopy

Akdeniz,

Al‐Shaebi,

Altunbek

et al. 2023

Biotechnology Journal

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Non‐infectious virus‐like particles (VLPs) are excellent structures for development of many biomedical applications such as drug delivery systems, vaccine production platforms, and detection techniques for infectious diseases including SARS‐CoV‐2 VLPs. The characterization of biochemical and biophysical properties of purified VLPs is crucial for development of detection methods and therapeutics. The presence of spike (S) protein in their structure is especially important since S protein induces immunological response. In this study, development of a rapid, low‐cost, and easy‐to‐use technique for both characterization and detection of S protein in the two VLPs, which are SARS‐CoV‐2 VLPs and HIV‐based VLPs was achieved using surface‐enhanced Raman spectroscopy (SERS). To analyze and classify datasets of SERS spectra obtained from the VLP groups, machine learning classification techniques including support vector machine (SVM), k‐nearest neighbors (kNN), and random forest (RF) were utilized. Among them, the SVM classification algorithm demonstrated the best classification performance for SARS‐CoV‐2 VLPs and HIV‐based VLPs groups with 87.5% and 92.5% accuracy, respectively. This study could be valuable for the rapid characterization of VLPs for the development of novel therapeutics or detection of structural proteins of viruses leading to a variety of infectious diseases.

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Label-free detection of bladder cancer and kidney cancer plasma based on SERS and multivariate statistical algorithm

Cited by 19 publications

References 42 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

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

Characterization and discrimination of spike protein in SARS‐CoV‐2 virus‐like particles via surface‐enhanced Raman spectroscopy

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