Surface-enhanced Raman spectroscopy of blood serum based on gold nanoparticles for tumor stages detection and histologic grades classification of oral squamous cell carcinoma

Colloidal surface-enhanced Raman spectroscopic study of grouper epidermal mucus using acidified sodium sulphate as the aggregating agent

Fish epidermal mucus is an important reservoir of antipathogenic compounds which serves as the first line of the immune defence. Despite its significant role in the physiology and health of fish, detailed profiling of fish epidermal mucus has yet to be explored. Therefore, this study investigates a label-free colloidal surface-enhanced Raman spectroscopic (SERS) method for profiling grouper mucus. Gold nanoparticles were first synthesised using the standard citrate reduction and characterised using ultraviolet-visible spectroscopy, transmission electron microscopy and dynamic light scattering. The influence of acidified sodium sulphate (Na2SO4) at pH 3 as the aggregating agent on the enhancement of the SERS spectrum of different analyte samples including rhodamine 6G (R6G) dye, lysozyme solution and hybrid grouper (Epinephelus fuscoguttatus × Epinephelus lanceolatus) mucus was observed. Based on the results, an optimal Na2SO4 concentration of 1 M was recorded to achieve the highest enhancement of the SERS signal for R6G and grouper mucus, while the optimal concentration for lysozyme was 0.1 M. The results indicated a higher degree of aggregation induced by lysozyme than R6G and grouper mucus. A few overlapping peaks of the SERS spectra of lysozyme and grouper mucus made it possible to confirm the presence of lysozyme as potential biomarkers.

Intraoperative Assessment of Resection Margin in Oral Cancer: The Potential Role of Spectroscopy

In parallel with the increasing number of oncological cases, the need for faster and more efficient diagnostic tools has also appeared. Different diagnostic approaches are available, such as radiological imaging or histological staining methods, but these do not provide adequate information regarding the resection margin, intraoperatively, or are time consuming. The purpose of this review is to summarize the current knowledge on spectrometric diagnostic modalities suitable for intraoperative use, with an emphasis on their relevance in the management of oral cancer. The literature agrees on the sensitivity, specificity, and accuracy of spectrometric diagnostic modalities, but further long-term prospective, multicentric clinical studies are needed, which may standardize the intraoperative assessment of the resection margin and the use of real-time spectroscopic approaches.

Rapid multi-task diagnosis of oral cancer leveraging fiber-optic Raman spectroscopy and deep learning algorithms

Introduction

Oral cancer, a predominant malignancy in developing nations, represents a global health challenge with a five-year survival rate below 50%. Nonetheless, substantial reductions in both its incidence and mortality rates can be achieved through early detection and appropriate treatment. Crucial to these treatment plans and prognosis predictions is the identification of the pathological type of oral cancer.

Methods

Toward this end, fiber-optic Raman spectroscopy emerges as an effective tool. This study combines Raman spectroscopy technology with deep learning algorithms to develop a portable intelligent prototype for oral case analysis. We propose, for the first time, a multi-task network (MTN) Raman spectroscopy classification model that utilizes a shared backbone network to simultaneously achieve different clinical staging and histological grading diagnoses.

Results

The developed model demonstrated accuracy rates of 94.88%, 94.57%, and 94.34% for tumor staging, lymph node staging, and histological grading, respectively. Its sensitivity, specificity, and accuracy compare closely with the gold standard: routine histopathological examination.

Discussion

Thus, this prototype proposed in this study has great potential for rapid, non-invasive, and label-free pathological diagnosis of oral cancer.

Quantitative detection of dopamine in human serum with surface-enhanced Raman scattering (SERS) of constrained vibrational mode

Dopamine (DA) is a crucial neurotransmitter involved in the hormonal, nervous, and vascular systems being considered as an index to diagnose neurodegenerative diseases, including Parkinson's and Alzheimer's disease. Herein, we demonstrate the quantitative sensing of DA using the peak shift in surface-enhanced Raman scattering (SERS) of 4-mercaptophenylboronic acid (4-MPBA), resulting from the concentration of DA. To enable the signal enhancement of Raman scattering, Ag nanostructure was built with one-step gas-flow sputtering. 4-MPBA was then introduced using vapor-based deposition, acting as a reporter molecule for bonding with DA. The gradual peak-shift from 1075.6 cm^-1 to 1084.7 cm^-1 was observed with the increasing concentration of DA from 1 pM to 100nM. The numerical simulation revealed that DA bonding induced a constrained vibrational mode corresponding to 1084.7 cm^-1 instead of a C-S-coupled C-ring in-plane bending mode of 4-MPBA corresponding to 1075.6 cm^-1. Proposed SERS sensors depicted reliable DA detection in human serum and good selectivity against other analytes, including glucose, creatinine, and uric acid.

Insights into Gold Nanoparticles Possibilities for Diagnosis and Treatment of the Head and Neck Upper Aerodigestive Tract Cancers

Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer affecting people and accounts for more than 300,000 deaths worldwide. Improvements in treatment modalities, including immunotherapy, have demonstrated promising prognoses for eligible patients. Nevertheless, the five-year overall survival rate has not increased significantly, and the tumor recurrence ratio remains at 50% or higher, except for patients with HPV-positive HNSCC. Over the last decades, nanotechnology has provided promising tools, especially for biomedical applications, due to some remarkable physicochemical properties of numerous nanomaterials, particularly gold nanoparticles. This review addresses the features and some applications of gold nanoparticles reported in the literature over the last five years regarding the diagnosis and treatment of head and neck cancer, highlighting the exciting possibilities of this nanomaterial in oncology. Methods: The scientific papers selected for this review were obtained from the PubMed Advanced, Web of Science, Scopus, ClinicalTrials.gov, and Google Scholar platforms. Conclusions: Results from papers applying gold nanoparticles have suggested that their application is a feasible approach to diagnostics, prognostics, and the treatment of HNC. Moreover, phase I clinical trials suggest that gold nanoparticles are safe and can potentially become theranostic agents for humans.

Diagnosis of urogenital cancer combining deep learning algorithms and surface-enhanced Raman spectroscopy based on small extracellular vesicles

PURPOSE

To identify and compare the capacities of serum and serum-derived small extracellular vesicles (EV) in diagnosis of common urogenital cancer combining Surface-enhanced Raman spectroscopy (SERS) and Convolutional Neural Networks (CNN).

MATERIALS AND METHODS

We collected serum samples from 32 patients with prostate cancer (PCa), 33 patients with renal cell cancer (RCC) and 30 patients with bladder cancer (BCa) as well as 35 healthy control (HC), which were thereafter used to enrich extracellular vesicles by ultracentrifuge. Label-free SERS was utilized to collect Raman spectra from serum and matched EV samples. We constructed CNN models to process SERS data for classification of malignant patients and healthy controls (HCs).

RESULTS

We collected 650 and 1206 spectra from serum and serum-derived EV, respectively. CNN models of EV spectra revealed high testing accuracies of 79.3%, 78.7% and 74.2% in diagnosis of PCa, RCC and BCa, respectively. In comparison, serum SERS-based CNN model had testing accuracies of 73.0%, 71.1%, 69.2% in PCa, RCC and BCa, respectively. Moreover, CNN models based on EV SERS data show significantly higher diagnostic capacities than matched serum CNN models with the area under curve (AUC) of 0.80, 0.88 and 0.74 in diagnosis of PCa, RCC and BCa, respectively.

CONCLUSION

Deep learning-based SERS analysis of EV has great potentials in diagnosis of urologic cancer outperforming serum SERS analysis, providing a novel tool in cancer screening.

Fused Raman spectroscopic analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics

As a rapid, label-free, non-destructive analytical measurement requiring little to no sample preparation, Raman spectroscopy shows great promise for liquid biopsy cancer detection and diagnosis. We carried out Raman analysis and mass spectrometry of plasma and saliva from more than 50 subjects in a cohort of head and neck cancer patients and benign controls (e.g., patients with benign oral masses). Unsupervised data models were built to assess diagnostic performance. Raman spectra collected from either biofluid provided moderate performance to discriminate cancer samples. However, by fusing together the Raman spectra of plasma and saliva for each patient, subsequent analytical models delivered an impressive sensitivity, specificity, and accuracy of 96.3%, 85.7%, and 91.7%, respectively. We further confirmed that the metabolites driving the differences in Raman spectra for our models are among the same ones that drive mass spectrometry models, unifying the two techniques and validating the underlying ability of Raman to assess metabolite composition. This study bolsters the relevance of Raman to provide additive value by probing the unique chemical compositions across biofluid sources. Ultimately, we show that a simple data augmentation routine of fusing plasma and saliva spectra provided significantly higher clinical value than either biofluid alone, pushing forward the potential of clinical translation of Raman spectroscopy for liquid biopsy cancer diagnostics.

Label-Free Sensing with Metal Nanostructure-Based Surface-Enhanced Raman Spectroscopy for Cancer Diagnosis

Surface-Enhanced Raman Spectroscopy (SERS) is a powerful analytical technique for the detection of small analytes with great potential for medical diagnostic applications. Its high sensitivity and excellent molecular specificity, which stems from the unique fingerprint of molecular species, have been applied toward the detection of different types of cancer. The noninvasive and rapid detection offered by SERS highlights its applicability for point-of-care (PoC) deployment for cancer diagnosis, screening, and staging, as well as for predicting tumor recurrence and treatment monitoring. This review provides an overview of the progress in label-free (direct) SERS-based chemical detection for cancer diagnosis with the main focus on the advances in the design and preparation of SERS substrates on the basis of metal nanoparticle structures formed via bottom-up strategies. It begins by introducing a synopsis of the working principles of SERS, including key chemometric approaches for spectroscopic data analysis. Then it introduces the advances of label-free sensing with SERS in cancer diagnosis using biofluids (blood, urine, saliva, sweat) and breath as the detection media. In the end, an outlook of the advances and challenges in cancer diagnosis via SERS is provided.

Analyzing the serum of hemodialysis patients with end-stage chronic kidney disease by means of the combination of SERS and machine learning

The aim of this paper is a multivariate analysis of SERS characteristics of serum in hemodialysis patients, which includes constructing classification models (PLS-DA, CNN) by the presence/absence of end-stage chronic kidney disease (CKD) with dialysis and determining the most informative spectral bands for identifying dialysis patients by variable importance distribution. We found the spectral bands that are informative for detecting the hemodialysis patients: the 641 cm^-1, 724 cm^-1, 1094 cm^-1 and 1393 cm^-1 bands are associated with the degree of kidney function inhibition; and the 1001 cm^-1 band is able to demonstrate the distinctive features of hemodialysis patients with end-stage CKD.

Micro-Raman spectroscopy study of blood samples from myocardial infarction patients

Acute myocardial infarction (MI) is found to be a major causative factor for global mortality and morbidity. This situation demands necessity of developing efficient and rapid diagnostic tools to detect acute MI. Raman spectroscopy is a non-destructive optical diagnostic technique, which has high potential in probing biochemical changes in clinical samples during initiation and progress of diseases. In this work, blood was taken as the sample to examine inflammation in acute MI patients using Raman spectroscopy. Ratio of Raman peak intensities that corresponds to phenylalanine (1000 cm^-1) and tyrosine (825 cm^-1) can facilitate indirect information about tetrahydrobiopterin (BH4) availability, which can indicate inflammatory status in patients. This ratio obtained was higher for MI patients in comparison with control subjects. The decrease in phenylalanine and tyrosine ratio (Phe-Tyr ratio) is attributed to the prognosis of standard of care (medications like antiplatelets including aspirin, statin and revascularisation) leading to inflammation reduction. Phe-Tyr ratio estimated from the Raman spectra of blood can be exploited as a reliable method to probe inflammation due to MI. The method is highly objective, require only microliters of sample and minimal sample preparation, signifying its clinical utility.

New Insights into the Multivariate Analysis of SER Spectra Collected on Blood Samples for Prostate Cancer Detection: Towards a Better Understanding of the Role Played by Different Biomolecules on Cancer Screening: A Preliminary Study

Simple Summary

In recent years, research on biofluids using Raman and SERS has expanded dramatically, indicating the enormous promise of this technology as a high-throughput tool for identifying cancer and other disorders. In the investigations thus far, researchers have concentrated on a specific illness or condition, but the techniques employed to acquire experimental spectra prevent direct comparison of the data. This necessitates comparative research of a variety of diseases and an increase in scientific cooperation to standardize experimental conditions. In our study, positive results were reached by applying a combined SERS multivariate analysis (MVA) to the urgent problem of prostate cancer diagnosis that was directly linked to real-world settings in healthcare. Moreover, in comparison to the prostate-specific antigen (PSA) test, which has a high sensitivity but limited specificity, our combined SERS-MVA method has greater specificity, which may assist in preventing the overtreatment of patients.

Abstract

It is possible to obtain diagnostically relevant data on the changes in biochemical elements brought on by cancer via the use of multivariate analysis of vibrational spectra recorded on biological fluids. Prostate cancer and control groups included in this research generated almost similar SERS spectra, which means that the values of peak intensities present in SERS spectra can only give unspecific and limited information for distinguishing between the two groups. Our diagnostic algorithm for prostate cancer (PCa) differentiation was built using principal component analysis and linear discriminant analysis (PCA-LDA) analysis of spectral data, which has been widely used in spectral data management in many studies and has shown promising results so far. In order to fully utilize the entire SERS spectrum and automatically determine the most meaningful spectral features that can be used to differentiate PCa from healthy patients, we perform a multivariate analysis on both the entire and specific spectral intervals. Using the PCA-LDA model, the prostate cancer and control groups are clearly distinguished in our investigation. The separability of the following two data sets is also evaluated using two alternative discrimination techniques: principal least squares discriminant analysis (PLS-DA) and principal component analysis—support vector machine (PCA-SVM).

Applications of Titanium Dioxide Nanostructure in Stomatology

Breakthroughs in the field of nanotechnology, especially in nanochemistry and nanofabrication technologies, have been attracting much attention, and various nanomaterials have recently been developed for biomedical applications. Among these nanomaterials, nanoscale titanium dioxide (nano-TiO₂) has been widely valued in stomatology due to the fact of its excellent biocompatibility, antibacterial activity, and photocatalytic activity as well as its potential use for applications such as dental implant surface modification, tissue engineering and regenerative medicine, drug delivery carrier, dental material additives, and oral tumor diagnosis and treatment. However, the biosafety of nano-TiO₂ is controversial and has become a key constraint in the development of nano-TiO₂ applications in stomatology. Therefore, in this review, we summarize recent research regarding the applications of nano-TiO₂ in stomatology, with an emphasis on its performance characteristics in different fields, and evaluations of the biological security of nano-TiO₂ applications. In addition, we discuss the challenges, prospects, and future research directions regarding applications of nano-TiO₂ in stomatology that are significant and worthy of further exploration.

Gold nanomaterials for oral cancer diagnosis and therapy: Advances, challenges, and prospects

Early diagnosis and treatment of oral cancer are vital for patient survival. Since the oral cavity accommodates the second largest and most diverse microbiome community after the gut, the diagnostic and therapeutic approaches with low invasiveness and minimal damage to surrounding tissues are keys to preventing clinical intervention-related infections. Gold nanoparticles (AuNPs) are widely used in the research of cancer diagnosis and therapy due to their excellent properties such as surface-enhanced Raman spectroscopy, surface plasma resonance, controlled synthesis, the plasticity of surface morphology, biological safety, and stability. AuNPs had been used in oral cancer detection reagents, tumor-targeted therapy, photothermal therapy, photodynamic therapy, and other combination therapies for oral cancer. AuNPs-based noninvasive diagnosis and precise treatments further reduce the clinical intervention-related infections. This review is focused on the recent advances in research and application of AuNPs for early screening, diagnostic typing, drug delivery, photothermal therapy, radiotherapy sensitivity treatment, and combination therapy of oral cancer. Distinctive reports from the literature are summarized to highlight the latest advances in the development and application of AuNPs in oral cancer diagnosis and therapy. Finally, this review points out the challenges and prospects of possible applications of AuNPs in oral cancer diagnosis and therapy.

Raman Spectroscopy: A Potential Diagnostic Tool for Oral Diseases

Oral diseases impose a major health burden worldwide and have a profound effect on general health. Dental caries, periodontal diseases, and oral cancers are the most common oral health conditions. Their occurrence and development are related to oral microbes, and effective measures for their prevention and the promotion of oral health are urgently needed. Raman spectroscopy detects molecular vibration information by collecting inelastic scattering light, allowing a “fingerprint” of a sample to be acquired. It provides the advantages of rapid, sensitive, accurate, and minimally invasive detection as well as minimal interference from water in the “fingerprint region.” Owing to these characteristics, Raman spectroscopy has been used in medical detection in various fields to assist diagnosis and evaluate prognosis, such as detecting and differentiating between bacteria or between neoplastic and normal brain tissues. Many oral diseases are related to oral microbial dysbiosis, and their lesions differ from normal tissues in essential components. The colonization of keystone pathogens, such as Porphyromonas gingivalis, resulting in microbial dysbiosis in subgingival plaque, is the main cause of periodontitis. Moreover, the components in gingival crevicular fluid, such as infiltrating inflammatory cells and tissue degradation products, are markedly different between individuals with and without periodontitis. Regarding dental caries, the compositions of decayed teeth are transformed, accompanied by an increase in acid-producing bacteria. In oral cancers, the compositions and structures of lesions and normal tissues are different. Thus, the changes in bacteria and the components of saliva and tissue can be used in examinations as special markers for these oral diseases, and Raman spectroscopy has been acknowledged as a promising measure for detecting these markers. This review summarizes and discusses key research and remaining problems in this area. Based on this, suggestions for further study are proposed.

A New Look into Cancer-A Review on the Contribution of Vibrational Spectroscopy on Early Diagnosis and Surgery Guidance

Simple Summary

Cancer is a leading cause of death worldwide, with the detection of the disease in its early stages, as well as a correct assessment of the tumour margins, being paramount for a successful recovery. While breast cancer is one of most common types of cancer, head and neck cancer is one of the types of cancer with a lower prognosis and poor aesthetic results. Vibrational spectroscopy detects molecular vibrations, being sensitive to different sample compositions, even when the difference was slight. The use of spectroscopy in biomedicine has been extensively explored, since it allows a broader assessment of the biochemical fingerprint of several diseases. This literature review covers the most recent advances in breast and head and neck cancer early diagnosis and intraoperative margin assessment, through Raman and Fourier transform infrared spectroscopies. The rising field of spectral histopathology was also approached. The authors aimed at expounding in a more concise and simple way the challenges faced by clinicians and how vibrational spectroscopy has evolved to respond to those needs for the two types of cancer with the highest potential for improvement regarding an early diagnosis, surgical margin assessment and histopathology.

Abstract

In 2020, approximately 10 million people died of cancer, rendering this disease the second leading cause of death worldwide. Detecting cancer in its early stages is paramount for patients’ prognosis and survival. Hence, the scientific and medical communities are engaged in improving both therapeutic strategies and diagnostic methodologies, beyond prevention. Optical vibrational spectroscopy has been shown to be an ideal diagnostic method for early cancer diagnosis and surgical margins assessment, as a complement to histopathological analysis. Being highly sensitive, non-invasive and capable of real-time molecular imaging, Raman and Fourier transform infrared (FTIR) spectroscopies give information on the biochemical profile of the tissue under analysis, detecting the metabolic differences between healthy and cancerous portions of the same sample. This constitutes tremendous progress in the field, since the cancer-prompted morphological alterations often occur after the biochemical imbalances in the oncogenic process. Therefore, the early cancer-associated metabolic changes are unnoticed by the histopathologist. Additionally, Raman and FTIR spectroscopies significantly reduce the subjectivity linked to cancer diagnosis. This review focuses on breast and head and neck cancers, their clinical needs and the progress made to date using vibrational spectroscopy as a diagnostic technique prior to surgical intervention and intraoperative margin assessment.

Nanoparticle surface-enhanced Raman spectroscopy as a noninvasive, label-free tool to monitor hematological malignancy

Aim: Monitoring minimal residual disease remains a challenge to the effective medical management of hematological malignancies; yet surface-enhanced Raman spectroscopy (SERS) has emerged as a potential clinical tool to do so. Materials & methods: We developed a cell-free, label-free SERS approach using gold nanoparticles (nanoSERS) to classify hematological malignancies referenced against two control cohorts: healthy and noncancer cardiovascular disease. A predictive model was built using machine-learning algorithms to incorporate disease burden scores for patients under standard treatment upon. Results: Linear- and quadratic-discriminant analysis distinguished three cohorts with 69.8 and 71.4% accuracies, respectively. A predictive nanoSERS model correlated (MSE = 1.6) with established clinical parameters. Conclusion: This study offers a proof-of-concept for the noninvasive monitoring of disease progression, highlighting the potential to incorporate nanoSERS into translational medicine.

SERS liquid biopsy: An emerging tool for medical diagnosis

Surface-enhanced Raman scattering (SERS) is emerging as a novel strategy for biofluid analysis. In this review, we delineate four experimental SERS protocols that are frequently used for the profiling of biofluids: 1) liquid SERS for the detection of purine metabolites; 2) iodide-modified liquid SERS for the detection of proteins; 3) dried SERS for the detection of both purine metabolites and proteins; 4) resonant Raman for the detection of carotenoids. To explain the selectivity of each experimental SERS protocol, we introduce a heuristic model for the chemisorption of analytes mediated by adsorbed ions (adions) onto the SERS substrate. Next, we show that the promising results of SERS liquid biopsy stem from the fact that the concentration levels of purine metabolites, proteins and carotenoids are informative of the cellular turnover rate, inflammation, and oxidative stress, respectively. These processes are perturbed in virtually every disease, from cancer to autoimmune maladies. Finally, we review recent SERS liquid biopsy studies and discuss future steps that are required for translating SERS in the clinical setting.

Nanoparticles in Dentistry: A Comprehensive Review

In recent years, nanoparticles (NPs) have been receiving more attention in dentistry. Their advantageous physicochemical and biological properties can improve the diagnosis, prevention, and treatment of numerous oral diseases, including dental caries, periodontal diseases, pulp and periapical lesions, oral candidiasis, denture stomatitis, hyposalivation, and head, neck, and oral cancer. NPs can also enhance the mechanical and microbiological properties of dental prostheses and implants and can be used to improve drug delivery through the oral mucosa. This paper reviewed studies from 2015 to 2020 and summarized the potential applications of different types of NPs in the many fields of dentistry.

Perspective of Future SERS Clinical Application Based on Current Status of Raman Spectroscopy Clinical Trials

Raman spectroscopy has emerged as a promising tool in biomedical analysis and clinical diagnosis. The development of surface-enhanced Raman scattering spectroscopy (SERS) improved the detection limit with ultrahigh sensitivity and simplicity. More and more Raman spectroscopy clinical trials (R-PCT) have been conducted recently. However, there is a lack of an up-to-date review summarizing the current status of Raman clinical trials performed until now. Hence, the clinical trials for Raman were retrieved from the International Clinical Trials Registration Platform. We summarized the clinical characteristics of 55 registered Raman spectroscopy clinical trials (R-RSCTs) and 44 published Raman spectroscopy clinical trials (P-RSCTs). This review could assist researchers and clinicians to understand the current status of Raman spectroscopy clinical research and perhaps could benefit the reasonable and accurate design of future SERS studies.

Establishment of a reliable scheme for obtaining highly stable SERS signal of biological serum

As a rapid and non-destructive biological serum detection method, SERS technology was widely used in the screening and medical diagnosis of various diseases by combining the analysis of serum SERS spectrum and multivariate statistical algorithm. Because of the high complexity of serum components and the variability of SERS spectra, which often resulted in the phenomenon that the SERS spectrum of the same biological serum was significantly different due to the different test conditions. In this experiment, through the dilution treatment of the serum and the systematic test of the serum of all concentration gradients with lasers of wavelength of 785, 633 and 532 nm, the most suitable conditions for detecting the serum were investigated. The experimental results showed that only when the serum is diluted to low concentration (10 ppm), the SERS spectrum with high reproducibility and stability could be obtained, furthermore, the low concentration serum had weak tolerance to laser, and 532 nm laser was not suitable for serum detection. In this paper, a set of test scheme for obtaining highly stable serum SERS spectra was established by using high-performance gold nanoparticles (Au NPs) as the active substrate of SERS. Through comparative analysis of SERS spectrum of serum of normal people and cervical cancer, the reliability of the established low-concentration serum test program was verified, as well as its great potential advantages in disease screening and diagnosis.

Identifying functioning and nonfunctioning adrenal tumors based on blood serum surface-enhanced Raman spectroscopy

Adrenal tumors are common tumors in urology and they can be further divided into functioning and nonfunctioning tumors according to whether there is uncommon endocrine function. In clinical practice, the early identification and accurate assessment of adrenal tumors are essential for the guidance of subsequent treatment. However, a nonfunctioning adrenal tumor often lacks obvious clinical symptoms, making it difficult to be timely and precisely diagnosed by conventional examinations. Therefore, a rapid and accurate method for identifying the functioning and nonfunctioning adrenal tumors is urgently required to achieve precise treatment of adrenal tumors. In this study, surface-enhanced Raman spectroscopy was investigated as a diagnostic tool to identify the blood serum samples from healthy volunteers as well as the patients with functioning and nonfunctioning adrenal tumors. Based on the SERS peak analysis, abnormal glycolysis, DNA/RNA, and amino acid metabolites were found to be potential biomarkers for identifying patients with adrenal tumors, while metabolites related to disordered protein catabolism and excessive hormone secretion were expected to further differentiate functioning adrenal tumors from nonfunctioning adrenal tumors. In addition, principal component analysis followed by support vector machine (PCA-SVM) was further applied on those serum SERS measurements, and the classification accuracies of 96.8% and 84.5% were achieved for differentiating healthy group versus adrenal tumor group and functioning adrenal tumor group versus nonfunctioning adrenal tumor group, respectively. The results have demonstrated the prodigious potential of precise adrenal tumor diagnosis by using the blood serum surface-enhanced Raman spectroscopy technique.

Nanoparticles for Oral Cancer Diagnosis and Therapy

Oral cancer is the sixth most common malignant cancer, affecting the health of people with an unacceptably high mortality rate. Despite numerous clinical methods in the diagnosis and therapy of oral cancer (e.g., magnetic resonance imaging, computed tomography, surgery, and chemoradiotherapy), they still remain far from optimal. Therefore, an urgent need exists for effective and practical techniques of early diagnosis and effective therapy of oral cancer. Currently, various types of nanoparticles have aroused wide public concern, representing a promising tool for diagnostic probes and therapeutic devices. Their inherent physicochemical features, including ultrasmall size, high reactivity, and tunable surface modification, enable them to overcome some of the limitations and achieve the expected diagnostic and therapeutic effect. In this review, we introduce different types of nanoparticles that emerged for the diagnosis and therapy of oral cancers. Then, the challenges and future perspectives for nanoparticles applied in oral cancer diagnosis and therapy are presented. The objective of this review is to help researchers better understand the effect of nanoparticles on oral cancer diagnosis and therapy and may accelerate breakthroughs in this field.

Highly accurate colorectal cancer prediction model based on Raman spectroscopy using patient serum

BACKGROUND

Colorectal cancer (CRC) is an important disease worldwide, accounting for the second highest number of cancer-related deaths and the third highest number of new cancer cases. The blood test is a simple and minimally invasive diagnostic test. However, there is currently no blood test that can accurately diagnose CRC.

AIM

To develop a comprehensive, spontaneous, minimally invasive, label-free, blood-based CRC screening technique based on Raman spectroscopy.

METHODS

We used Raman spectra recorded using 184 serum samples obtained from patients undergoing colonoscopies. Patients with malignant tumor histories as well as those with cancers in organs other than the large intestine were excluded. Consequently, the specific diseases of 184 patients were CRC (12), rectal neuroendocrine tumor (2), colorectal adenoma (68), colorectal hyperplastic polyp (18), and others (84). We used the 1064-nm wavelength laser for excitation. The power of the laser was set to 200 mW.

RESULTS

Use of the recorded Raman spectra as training data allowed the construction of a boosted tree CRC prediction model based on machine learning. Therefore, the generalized R² values for CRC, adenomas, hyperplastic polyps, and neuroendocrine tumors were 0.9982, 0.9630, 0.9962, and 0.9986, respectively.

CONCLUSION

For machine learning using Raman spectral data, a highly accurate CRC prediction model with a high R² value was constructed. We are currently planning studies to demonstrate the accuracy of this model with a large amount of additional data.

Identifying benign and malignant thyroid nodules based on blood serum surface-enhanced Raman spectroscopy

The aim of this study is to evaluate the feasibility of using blood serum surface-enhanced Raman spectroscopy (SERS) to identify benign and malignant thyroid nodules. Blood serum samples collected from three different groups including healthy volunteers (n = 22), patients with benign nodules (n = 19) and malignant nodules (n = 22) were measured by SERS. The spectral analysis results demonstrate that biomolecules in serum, such as amino acids, adenine and nucleic acid bases, change differently due to the different progression of nodules. By further combining with partial least square analysis and linear discriminant analysis (PLS-LDA) method, diagnostic accuracies of 93.65% and 82.93%, sensitivities of 92.68% and 81.82% and specificities of 95.45% and 84.21% can be achieved for differentiating healthy versus thyroid nodular groups and benign versus malignant groups, respectively. The above results have suggested that the blood serum SERS technique is helpful for precise diagnosis and timely treatment for patients with thyroid nodules.

Surface-enhanced Raman spectroscopy of preoperative serum samples predicts Gleason grade group upgrade in biopsy Gleason grade group 1 prostate cancer

PURPOSE

To predict Gleason grade group (GG) upgrade in biopsy Gleason grade group 1 (GG1) prostate cancer (CaP) patients using surface-enhanced Raman spectroscopy (SERS).

MATERIALS AND METHODS

Preoperative serum samples of patients with biopsy GG1 and subsequent radical prostatectomy were analyzed using SERS. The role of clinical variables and distinctive SERS spectra in the prediction of GG upgrade were evaluated. Principal component analysis and linear discriminant analysis (PCA-LDA) were used to manage spectral data and develop diagnostic algorithms.

RESULTS

A total of 342 preoperative serum SERS spectra from 114 patients were obtained. SERS detected a higher level of circulating free nucleic acid bases and a lower level of lipids in patients with GG upgrade to GG3 and higher, presenting as SERS spectral peaks of 728 cm^-1 and 1,655 cm^-1, respectively. Both spectral peaks were independent predictors of GG upgrade and their addition to clinical predictors of PSA and positive core percent significantly improved predictive power of the logistic regression model with area under curve improved from 0.65 to 0.80 (P = 0.0045). Meanwhile, PCA-LDA diagnostic model based on serum SERS spectra showed a high accuracy of 91.2% in predicted groups and remained stable with a sensitivity, specificity, and accuracy of 65%, 97.3%, 86.0%, respectively when validated by leave-one-out cross-validation method.

CONCLUSIONS

By analyzing preoperative serum samples, SERS combined with PCA-LDA model could be a promising tool for prediction of Gleason GG upgrade in biopsy GG1 CaP and assist in treatment decision-making in clinical practice.

Label-free diagnosis for colorectal cancer through coffee ring-assisted surface-enhanced Raman spectroscopy on blood serum

Surface-enhanced Raman spectroscopy (SERS) is garnering considerable attention for the swift diagnosis of pathogens and abnormal biological status, that is, cancers. In this work, a simple, fast and inexpensive optical sensing platform is developed by the design of SERS sampling and data analysis. The pretreatment of spectral measurement employed gold nanoparticle colloid mixing with the serum from patients with colorectal cancer (CRC). The droplet of particle-serum mixture formed coffee-ring-like region at the rim, providing strong and stable SERS profiles. The obtained spectra from cancer patients and healthy volunteers were analyzed by unsupervised principal component analysis (PCA) and supervised machine learning model, such as support-vector machine (SVM), respectively. The results demonstrate that the SVM model provides the superior performance in the classification of CRC diagnosis compared with PCA. In addition, the values of carcinoembryonic antigen from the blood samples were compiled with the corresponding SERS spectra for SVM calculation, yielding improved prediction results.

Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning

This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.

Identifying non-muscle-invasive and muscle-invasive bladder cancer based on blood serum surface-enhanced Raman spectroscopy

The assessment of the muscle invasion of bladder cancer typically plays a crucial role in therapeutic decision-making and has significant impacts on the recurrence rate and survival rate. Although histopathology is sufficiently accurate and usually served as the gold standard for bladder cancer diagnosis, it is invasive, time-consuming, and requires intensive sample preparation by a well-trained pathologist to achieve an optimal diagnosis. Therefore, a fast and noninvasive method to accurately identify non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) is in demand. In this study, the SERS technique combined with the PLS-LDA method based on a small amount of blood serum samples is employed to distinguish healthy volunteers, NMIBC, and MIBC patients. According to the results, the overall diagnostic accuracy is 93.3%. The diagnostic accuracies are 97.8% and 93.2% for healthy versus bladder cancer groups and NMIBC versus MIBC groups, respectively. Therefore, the proposed method has demonstrated excellent performance on accurately identifying muscle invasion of bladder cancer, which can assist timely diagnosis and proper treatment for bladder cancer patients.

Recent Progress on Liquid Biopsy Analysis using Surface-Enhanced Raman Spectroscopy

Traditional tissue biopsy is limited in understanding heterogeneity and dynamic evolution of tumors. Instead, analyzing circulating cancer markers in various body fluids, commonly referred to as "liquid biopsy", has recently attracted remarkable interest for their great potential to be applied in non-invasive early cancer screening, tumor progression monitoring and therapy response assessment. Among the various approaches developed for liquid biopsy analysis, surface-enhanced Raman spectroscopy (SERS) has emerged as one of the most powerful techniques based on its high sensitivity, specificity, tremendous spectral multiplexing capacity for simultaneous target detection, as well as its unique capability for obtaining intrinsic fingerprint spectra of biomolecules. In this review, we will first briefly explain the mechanism of SERS, and then introduce recently reported SERS-based techniques for detection of circulating cancer markers including circulating tumor cells, exosomes, circulating tumor DNAs, microRNAs and cancer-related proteins. Cancer diagnosis based on SERS analysis of bulk body fluids will also be included. In the end, we will summarize the "state of the art" technologies of SERS-based platforms and discuss the challenges of translating them into clinical settings.