Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms.

Early detection of breast cancer and its molecular subtyping is crucial for guiding clinical treatment and improving survival rate. Current diagnostic methods for breast cancer are invasive, time consuming and complicated. In this work, an optical detection method integrating surface-enhanced Raman spectroscopy (SERS) technology with feature selection and deep learning algorithm was developed for identifying serum components and building diagnostic model, with the aim of efficient and accurate noninvasive screening of breast cancer. First, the high quality of serum SERS spectra from breast cancer (BC), breast benign disease (BBD) patients and healthy controls (HC) were obtained. Chi-square tests were conducted to exclude confounding factors, enhancing the reliability of the study. Then, LightGBM (LGB) algorithm was used as the base model to retain useful features to significantly improve classification performance. The DNN algorithm was trained through backpropagation, adjusting the weights and biases between neurons to improve the network's predictive ability. In comparison to traditional machine learning algorithms, this method provided more accurate information for breast cancer classification, with classification accuracies of 91.38 % for BC and BBD, and 96.40 % for BC, BBD, and HC. Furthermore, the accuracies of 90.11 % for HR+/HR- and 88.89 % for HER2+/HER2- can be reached when evaluating BC patients' molecular subtypes. These results demonstrate that serum SERS combined with powerful LGB-DNN algorithm would provide a supplementary method for clinical breast cancer screening.

Highly Efficient Blood Protein Analysis Using Membrane Purification Technique and Super-Hydrophobic SERS Platform for Precise Screening and Staging of Nasopharyngeal Carcinoma.

Early screening and precise staging are crucial for reducing mortality in patients with nasopharyngeal carcinoma (NPC). This study aimed to assess the performance of blood protein surface-enhanced Raman scattering (SERS) spectroscopy, combined with deep learning, for the precise detection of NPC. A highly efficient protein SERS analysis, based on a membrane purification technique and super-hydrophobic platform, was developed and applied to blood samples from 1164 subjects, including 225 healthy volunteers, 120 stage I, 249 stage II, 291 stage III, and 279 stage IV NPC patients. The proteins were rapidly purified from only 10 µL of blood plasma using the membrane purification technique. Then, the super-hydrophobic platform was prepared to pre-concentrate tiny amounts of proteins by forming a uniform deposition to provide repeatable SERS spectra. A total of 1164 high-quality protein SERS spectra were rapidly collected using a self-developed macro-Raman system. A convolutional neural network-based deep-learning algorithm was used to classify the spectra. An accuracy of 100% was achieved for distinguishing between the healthy and NPC groups, and accuracies of 96%, 96%, 100%, and 100% were found for the differential classification among the four NPC stages. This study demonstrated the great promise of SERS- and deep-learning-based blood protein testing for rapid, non-invasive, and precise screening and staging of NPC.

Rapid and Label-Free Prenatal Detection of Down's Syndrome Using Body Fluid Surface Enhanced Raman Spectroscopy.

Down's syndrome (DS) is the leading genetic cause of intellectual disability. In this work, the surface enhanced Raman spectroscopy (SERS) was used for the detection of amniotic fluid and plasma from pregnant women with DS fetus for the first time. High-quality and characteristic spectral features of amniotic fluid and plasma samples from DS groups can be obtained in comparison to normal group. Moreover, principal component analysis with linear discriminant analysis was applied to generate the efficient diagnostic model, achieving accuracies of 94.3% and 88.5% for the DS detection with amniotic fluid and plasma samples, respectively. This preliminary study would provide a novel, convenient and accurate prenatal test based on blood SERS technology for clinical DS screening.

Optical biosensor based on SERS with signal calibration function for quantitative detection of carcinoembryonic antigen.

Monitoring the levels of cancer biomarkers is essential for cancer diagnosis and evaluation. In this study, a novel sandwich type sensing platform based on surface-enhanced Raman scattering (SERS) technology was developed for the detection of carcinoembryonic antigen (CEA), with a limit of detection (LOD) of 0.258 ng/mL. In order to achieve sensitive detection of CEA in complex samples, gold nanoparticle monolayer modified with CEA antibodies and with aptamer-functionalized probes was fabricated to target CEA. Two gold layers were integrated into the SERS platform, which greatly enhanced the signal of the probe by generating tremendous "hot spots". Meanwhile, the intensity ratio of Raman probes and the second-order peak of the silicon wafer was used to achieve dynamic calibration of the Raman probe signal. Excitingly, this sensing platform was capable of distinguishing cancer patients from healthy individuals via CEA concentrations in blood samples with the accuracy of 100%. This sandwich structure SERS sensing platform presented promising potential to be an alternative tool for clinical biomarker detection in the field of cancer diagnosis.

Reusable 3D silver superposed silica SERS substrate based on the Griess reaction for the ratiometric detection of nitrite.

When nitrite is ingested and absorbed by the body, it can be converted into highly toxic nitrosamines (carcinogens, teratogens, and mutagens), posing health risks to the general population. Therefore, it calls for establishing a method for determination of nitrite. In this paper, the glass-SiO2-Ag surface-enhanced Raman scattering (SERS) substrate with a large number of "hot spots" were prepared by two kinds of silane coupling agents. The SERS substrate had high sensitivity and repeatability. Silicon dioxide supported the silver nanoparticles (Ag NPs), which increased surface roughness of the substrate, generated a great quantity of hot spots and enhanced the SERS signal. In the SERS spectrum, the intensity ratio of the two characteristic peaks (1287 cm-1 and 1076 cm-1) had a good linear correlation with the logarithm of the concentration of nitrite, R2 = 0.9652. The recoveries of 50 µM and 100 µM nitrite in three kinds of foods, three kinds of cosmetics and tap water were 90.9-105.3%.

Interference-free SERS tags for ultrasensitive quantitative detection of tyrosinase in human serum based on magnetic bead separation.

Tyrosinase (TYR) expression and activity determine the rate and yield of melanin production. Studies have shown that TYR is a potential biomarker for melanoma and highly sensitive detection of TYR benefits early diagnosis of melanoma-related diseases. In this study, we developed a method that combines surface-enhanced Raman scattering (SERS) and sandwich-type immunity for sensitive detection of TYR, in which 4-mercaptobenzonitrile (4 MB) embedded between the Au core and Au shell (Au4MB @ Au) core-shell structure was employed as a SERS probe for quantitative detection of TYR while the magnetic bead serves as a capture substrate. Our results demonstrated that under magnetic separation, the specific SERS signal obtained is highly correlated with TYR concentrations. Furthermore, the combination of magnetic beads and Au4MB @ Au core-shell structure significantly improved the sensitivity of the sensing platform, resulting in detection limits of 0.45 ng mL-1. More importantly, the detection and analysis of TYR concentration in human serum samples showed good accuracy and an excellent recovery rate. Accuracy of the system was investigated from % recovery of spiked TYR standard solutions and found to be in the range of 90-104%, which further verified the feasibility and reliability of our method applied in a complex environment. We anticipate this SERS-based immunoassay method to be applied to TYR detection in the clinical setting and to be extended to other promising related fields.

Promoting identification of amyotrophic lateral sclerosis based on label-free plasma spectroscopy.

OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is an adult-onset fatal neurodegenerative disease which lacks identified biological markers. A label-free plasma surface-enhanced Raman spectroscopy (SERS) method was developed to explore a simple and noninvasive test for ALS. METHODS: ALS patients were enrolled serially and plasma samples were collected at the time of diagnosis prior to the start of ALS treatment. SERS spectra were recorded using a Renishaw micro-Raman system. RESULTS: To exclude the interference by varying disease severity, we enrolled three groups of ALS patients, including ALS-1 (n = 60; ALSFRS-R ≥ 42 and time interval ≤ 12 months), ALS-2 (n = 61; ALSFRS-R < 42 and time interval ≤ 12 months), and ALS-3 (n = 61; ALSFRS-R ≥ 38 and time interval> 12 months). The SERS spectra were analyzed using principal component analysis (PCA), which showed that ALS-1, ALS-2, ALS-3, and control groups were separated significantly. Then, decision tree (DT) models and receiver operating characteristic curves were employed and identified that bands at 722 and 739 cm-1 , and ratios of 635-722 cm-1 and 635-739 cm-1 were able to distinguish ALS from controls significantly. Finally, we highlighted six metabolism pathways correlated with ALS, including phenylalanine-tyrosine-tryptophan biosynthesis, aminoacyl-tRNA biosynthesis, phenylalanine metabolism, pantothenate and CoA biosynthesis, porphyrin and chlorophyll metabolism, and pyrimidine metabolism. INTERPRETATION: Plasma SERS could be a promising tool for the detection of ALS. The bands at 722 and 739 cm-1 , and the ratios of 635-722 cm-1 and 635-739 cm-1 could serve as potential indicator for ALS.

Metabolite profiling of human blood by surface-enhanced Raman spectroscopy for surgery assessment and tumor screening in breast cancer.

Mammography, a standard screening method for breast cancer, is effective for reducing the rate of death; however, it suffers from frequent false positive alarm and radiation risk. Besides, surgery treatment has a vital impact on the clinical outcomes of breast cancer, offering enormous benefits for breast cancer care and management. In this work, we analyzed the peripheral blood sample from breast cancer patients with pre- and post-surgery and healthy volunteers using label-free surface-enhanced Raman spectroscopy technology based on silver nanoparticles. Results showed that distinct patterns of blood belonging to specific subjects could be profiled, and corresponding accuracies of 95% and 100% were achieved by multivariate diagnostic algorithm for pre-surgery vs. post-surgery and pre-surgery vs. normal groups, respectively, providing a unique blood analysis method for surgery evaluation as well as tumor screening in breast cancer. Graphical abstract.

Rapid and label-free urine test based on surface-enhanced Raman spectroscopy for the non-invasive detection of colorectal cancer at different stages.

The concept of being able to urinate in a cup and screen for colorectal cancer (CRC) is fascinating to the public at large. Here, a simple and label-free urine test based on surface-enhanced Raman spectroscopy (SERS) was employed for CRC detection. Significant spectral differences among normal, stages I-II, and stages III-IV CRC urines were observed. Using discriminant function analysis, the diagnostic sensitivities of 95.8%, 80.9%, and 84.3% for classification of normal, stages I-II, and stages III-IV CRC were achieved in training model, indicating the great promise of urine SERS as a rapid, convenient and noninvasive method for CRC staging detection.

Interference-free and high precision biosensor based on surface enhanced Raman spectroscopy integrated with surface molecularly imprinted polymer technology for tumor biomarker detection in human blood.

The reliable quantitative analysis of tumor biomarkers in circulating blood is crucial for cancer early screening, therapy monitoring and prognostic prediction. Herein, a novel biosensor combing surface-enhanced Raman spectroscopy (SERS) and surface molecularly imprinted polymer (SMIP) technology was developed for quantitative detection of carcinoembryonic antigen (CEA) that is closely related to several common cancers. Owing to the use of SMIP, recognition sites with high affinity to the target of interest can be well imprinted on the surface of SERS substrate, leading to a more stable and specific capture ability. In addition, two layers of core-shell nanoparticles were integrated to this SERS substrate to form highly efficient electromagnetic enhancement for SERS measurement via the generation of lots of "hot spot". Besides, a unique Raman reporter (CC) with silent Raman signals at 2024 cm-1 was capsulated in the nanoparticles to avoid the optical noises originating from endogenous molecules at fingerprint region (300-1800 cm-1). Meanwhile, we employed an internal standard molecular (CN) to real time correct the fluctuating signals of Raman reporter when performing the quantitative analysis. Due to these features, a limit of detection (LOD) of 0.064 pg mL-1 with the detection range of 0.1 pg mL-1 - 10 µg mL-1 can be achieved by this assay. Excitingly, this technology even showed wonderful performances for CEA detection in real blood from cancer patients, demonstrating great potential for biomarker-based cancer screening.

Prognostic analysis of amyotrophic lateral sclerosis based on clinical features and plasma surface-enhanced Raman spectroscopy.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a wide range of survival times. We aimed to explore prognostic factors related to short survival based on clinical features and plasma metabolic signatures using surface-enhanced Raman spectroscopy (SERS). One hundred and thirty-eight sporadic ALS cases were enrolled serially, including 62 for the short-duration group (≤3 years) and 76 for the long-duration group (>3 years). Multivariate analysis showed that an older age of onset (>60 years; odds ratio [OR] = 3.98, 95% CI: 1.09-14.53), lower body mass index (BMI) (<18.5; OR = 6.80, 95% CI: 1.36-33.92), and lower ALSFRS-R score (<35; OR = 6.03, 95% CI: 1.42-25.63) were associated with higher odds of tracheotomy or death, while a higher uric acid (UA) level showed a protective effect (>356.36 µmol/L; OR = 0.19, 95% CI: 0.05-0.73). SERS analysis showed significant differences between the two groups, and pathway analysis highlighted five main metabolic pathways, including metabolisms of glutathione, pyrimidine, phenylalanine, galactose, and phenylalanine-tyrosine-tryptophan biosynthesis. In conclusion, age of onset, BMI, ALSFRS-R score and UA, together with dysregulation of glucose, amino acid, nucleic acid, and antioxidant metabolism contributed to disease progression, and are therefore potential therapeutic targets for ALS.

Rapid discrimination of colon cancer cells with single base mutation in KRAS gene segment using laser tweezers Raman spectroscopy.

Laser tweezers Raman spectroscopy (LTRS) as a label-free and noninvasive technology has been proven to be an ideal tool for analysis of single living cells, which provides important fingerprint information without interference from surrounding environments. For the first time, LTRS system was successfully used to examine the colon cancer cells with single base mutation in KRAS gene segment, including DKS-8 (KRAS wild-type [WT]) and DLD-1 (KRAS mutant-type [MT]), HKE-3 (KRAS WT) and HCT-116 (KRAS MT). Spectra changes of some vital biomolecules due to the gene mutation state were sensitively recorded by our home-made LTRS system. As a result of the comparison between DKS-8 and DLD-1 cells, an index of 97.5% of correct classification was obtained by combining LTRS with principle component analysis coupled with linear discriminant analysis (PCA-LDA) statistical analysis, while an index of 97.0% of correct classification was achieved between HKE-3 and HCT-116 cells. Moreover, between WT cells (DKS-8 and HKE-3) vs MT cells (DLD-1 and HCT-116), the index of correct classification was 81.2%, which was quite encouraging. Our preliminary results showed that the LTRS system coupled with PCA-LDA analysis will have a great potential for further applications in the rapid and label-free detection of circulating tumor cells in liquid biopsy.

A novel urine analysis technique combining affinity chromatography with Au nanoparticle based surface enhanced Raman spectroscopy for potential applications in non-invasive cancer screening.

Modified nucleoside in urine samples is one of the most common biomarkers for cancer screening. Therefore, we developed a novel detection method for modified nucleoside detection in human urine. In this work, the modified nucleoside from real cancer patient's urine samples was first separated and purified using the affinity chromatography (AC) technology relying on its specific adsorption capacity. Then, surface-enhanced Raman spectroscopy (SERS) technology with the capability of single molecular detection was used to sensitively characterize the biomolecular features of modified nucleoside. A total of 141 high-quality SERS spectra of urinary modified nucleoside can be obtained from 50 gastric cancer patients and 43 breast cancer patients, as well as 48 healthy volunteers. Using principal component analysis combined with linear discriminant analysis (PCA-LDA), the diagnostic sensitivities for identifying gastric cancer vs normal, breast cancer vs normal, gastric cancer vs breast cancer were 84.0%, 76.7% and 82.0%, respectively, and the corresponding diagnostic specificities for each combination were 95.8%, 87.5% and 90.7%, respectively. These results show that this novel method based on urinary modified nucleoside detection combining AC and SERS technologies holds promising potential for developing a specific, non-invasive and label-free tool for cancer screening.

Noninvasive detection of nasopharyngeal carcinoma based on saliva proteins using surface-enhanced Raman spectroscopy.

The present study evaluated the capability of saliva analysis combining membrane protein purification with surface-enhanced Raman spectroscopy (SERS) for noninvasive detection of nasopharyngeal carcinoma (NPC). A rapid and convenient protein purification method based on cellulose acetate membrane was developed. A total of 659 high-quality SERS spectra were acquired from purified proteins extracted from the saliva samples of 170 patients with pathologically confirmed NPC and 71 healthy volunteers. Spectral analysis of those saliva protein SERS spectra revealed specific changes in some biochemical compositions, which were possibly associated with NPC transformation. Furthermore, principal component analysis combined with linear discriminant analysis (PCA-LDA) was utilized to analyze and classify the saliva protein SERS spectra from NPC and healthy subjects. Diagnostic sensitivity of 70.7%, specificity of 70.3%, and diagnostic accuracy of 70.5% could be achieved by PCA-LDA for NPC identification. These results show that this assay based on saliva protein SERS analysis holds promising potential for developing a rapid, noninvasive, and convenient clinical tool for NPC screening.

[Advances in Raman Spectroscopy for Nasopharyngeal Carcinoma Tissue].

Nasopharyngeal carcinoma is a unique malignant tumor that has a distinct geographic and racial distribution, with a high incidence in southeast Asia and southern China. High degree of malignancy, poor prognosis and difficulty in early diagnosis remain a problem in nasopharyngeal carcinoma. Raman spectroscopy technique based on inelastic scattering is a rapid and nonivasive detection method, which is capable of providing the information of biochemical components at molecular vibration level.This article reviewed the recent research progress of nasopharyngeal carcinoma based on Raman spectroscopy. It mainly introduces the study of detecting nasopharyngeal carcinoma tissue by using Raman spectroscopy as well as surface-enhanced Raman scattering spectroscopy (SERS). The emphasis is put on the latest works by our research group, including high wavenumber Raman spectroscopy of tissue, Raman spectroscopy of tissue smears, and a specially designed endoscopic device combined with Raman spectroscopy for in vivo nasopharyngeal cancerous tissue detection, which was firstly developed by our group. Finally, the prospects of the development of Raman spectroscopy for nasopharyngeal carcinoma were discussed.