A Novel Surface Enhanced Raman Catheter for Rapid Detection, Classification, and Grading of Oral Cancer

Rapid diagnosis of celiac disease based on plasma Raman spectroscopy combined with deep learning

Celiac Disease (CD) is a primary malabsorption syndrome resulting from the interplay of genetic, immune, and dietary factors. CD negatively impacts daily activities and may lead to conditions such as osteoporosis, malignancies in the small intestine, ulcerative jejunitis, and enteritis, ultimately causing severe malnutrition. Therefore, an effective and rapid differentiation between healthy individuals and those with celiac disease is crucial for early diagnosis and treatment. This study utilizes Raman spectroscopy combined with deep learning models to achieve a non-invasive, rapid, and accurate diagnostic method for celiac disease and healthy controls. A total of 59 plasma samples, comprising 29 celiac disease cases and 30 healthy controls, were collected for experimental purposes. Convolutional Neural Network (CNN), Multi-Scale Convolutional Neural Network (MCNN), Residual Network (ResNet), and Deep Residual Shrinkage Network (DRSN) classification models were employed. The accuracy rates for these models were found to be 86.67%, 90.76%, 86.67% and 95.00%, respectively. Comparative validation results revealed that the DRSN model exhibited the best performance, with an AUC value and accuracy of 97.60% and 95%, respectively. This confirms the superiority of Raman spectroscopy combined with deep learning in the diagnosis of celiac disease.

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.

SERS sensing for cancer biomarker: Approaches and directions

These days, cancer is thought to be more than just one illness, with several complex subtypes that require different screening approaches. These subtypes can be distinguished by the distinct markings left by metabolites, proteins, miRNA, and DNA. Personalized illness management may be possible if cancer is categorized according to its biomarkers. In order to stop cancer from spreading and posing a significant risk to patient survival, early detection and prompt treatment are essential. Traditional cancer screening techniques are tedious, time-consuming, and require expert personnel for analysis. This has led scientists to reevaluate screening methodologies and make use of emerging technologies to achieve better results. Using time and money saving techniques, these methodologies integrate the procedures from sample preparation to detection in small devices with high accuracy and sensitivity. With its proven potential for biomedical use, surface-enhanced Raman scattering (SERS) has been widely used in biosensing applications, particularly in biomarker identification. Consideration was given especially to the potential of SERS as a portable clinical diagnostic tool. The approaches to SERS-based sensing technologies for both invasive and non-invasive samples are reviewed in this article, along with sample preparation techniques and obstacles. Aside from these significant constraints in the detection approach and techniques, the review also takes into account the complexity of biological fluids, the availability of biomarkers, and their sensitivity and selectivity, which are generally lowered. Massive ways to maintain sensing capabilities in clinical samples are being developed recently to get over this restriction. SERS is known to be a reliable diagnostic method for treatment judgments. Nonetheless, there is still room for advancement in terms of portability, creation of diagnostic apps, and interdisciplinary AI-based applications. Therefore, we will outline the current state of technological maturity for SERS-based cancer biomarker detection in this article. The review will meet the demand for reviewing various sample types (invasive and non-invasive) of cancer biomarkers and their detection using SERS. It will also shed light on the growing body of research on portable methods for clinical application and quick cancer detection.

Early-stage oral cancer diagnosis by artificial intelligence-based SERS using Ag NWs@ZIF core-shell nanochains

Surface-enhanced Raman spectroscopy (SERS) has great potential in the early diagnosis of diseases by detecting the changes of volatile biomarkers in exhaled breath, because of its high sensitivity, rich chemical molecular fingerprint information, and immunity to humidity. Here, an accurate diagnosis of oral cancer (OC) is demonstrated using artificial intelligence (AI)-based SERS of exhaled breath in plasmonic-metal organic framework (MOF) nanoparticles. These plasmonic-MOF nanoparticles were prepared using a zeolitic imidazolate framework coated on Ag nanowires (Ag NWs@ZIF), which offers Raman enhancement from the plasmonic nanowires and gas enrichment from the ZIF shells. Then, the core-shell nanochains of Ag NWs@ZIF prepared with 0.5 mL Ag NWs were selected to capture gaseous methanethiol, which is a tumor biomarker, from the exhalation of OC patients. The substrate was used to collect a total of 400 SERS spectra of exhaled breath of simulated healthy people and simulated OC patients. The artificial neural network (ANN) model in the AI algorithm was trained with these SERS spectra and could classify them with an accuracy of 99%. Notably, the model predicted OC with an area under the curve (AUC) of 0.996 for the simulated OC breath samples. This work suggests the great potential of the combination of breath analysis and AI as a method for the early-stage diagnosis of oral cancer.

A New Detection Method of Oral and Oropharyngeal Squamous Cell Carcinoma Based on Multivariate Analysis of Surface Enhanced Raman Spectra of Salivary Exosomes

Raman spectroscopy recently proved a tremendous capacity to identify disease-specific markers in various (bio)samples being a non-invasive, rapid, and reliable method for cancer detection. In this study, we first aimed to record vibrational spectra of salivary exosomes isolated from oral and oropharyngeal squamous cell carcinoma patients and healthy controls using surface enhancement Raman spectroscopy (SERS). Then, we assessed this method's capacity to discriminate between malignant and non-malignant samples by means of principal component-linear discriminant analysis (PC-LDA) and we used area under the receiver operating characteristics with illustration as the area under the curve to measure the power of salivary exosomes SERS spectra analysis to identify cancer presence. The vibrational spectra were collected on a solid plasmonic substrate developed in our group, synthesized using tangential flow filtered and concentrated silver nanoparticles, capable of generating very reproducible spectra for a whole range of bioanalytes. SERS examination identified interesting variations in the vibrational bands assigned to thiocyanate, proteins, and nucleic acids between the saliva of cancer and control groups. Chemometric analysis indicated discrimination sensitivity between the two groups up to 79.3%. The sensitivity is influenced by the spectral interval used for the multivariate analysis, being lower (75.9%) when the full-range spectra were used.

Toward Personalized Nanomedicine: The Critical Evaluation of Micro and Nanodevices for Cancer Biomarker Analysis in Liquid Biopsy

Liquid biopsy for the analysis of circulating cancer biomarkers (CBs) is a major advancement toward the early detection of cancer. In comparison to tissue biopsy techniques, liquid biopsy is relatively painless, offering multiple sampling opportunities across easily accessible bodily fluids such as blood, urine, and saliva. Liquid biopsy is also relatively inexpensive and simple, avoiding the requirement for specialized laboratory equipment or trained medical staff. Major advances in the field of liquid biopsy are attributed largely to developments in nanotechnology and microfabrication that enables the creation of highly precise chip-based platforms. These devices can overcome detection limitations of an individual biomarker by detecting multiple markers simultaneously on the same chip, or by featuring integrated and combined target separation techniques. In this review, the major advances in the field of portable and semi-portable micro, nano, and multiplexed platforms for CB detection for the early diagnosis of cancer are highlighted. A comparative discussion is also provided, noting merits and drawbacks of the platforms, especially in terms of portability. Finally, key challenges toward device portability and possible solutions, as well as discussing the future direction of the field are highlighted.

Euclidean distance-based Raman spectroscopy (EDRS) for the prognosis analysis of gastric cancer: A solution to tumor heterogeneity

The prognosis analysis of gastric cancer is critical for selection of treatments and development of advanced therapeutic methods. A prognosis approach that is accurate, fast, convenient, and of low cost for gastric cancers is in high demand. Raman spectroscopy is a label-free and non-destructive technique to provide molecular fingerprints of biological samples, holding promises for cancer prognosis. However, the major challenge of gastric cancer prognosis lies in the widely existing tumor heterogeneity, which leads to unexpected spectral variations within one type of samples. In this work, we have developed the Euclidean distance (ED)-based Raman spectroscopy (EDRS) method for the prognosis analysis of gastric cancer to eliminate the influence of tumor heterogeneity. Raman spectra were first collected on the slices of paraffin-preserved tumor tissues from gastric cancer patients. A standard spectrum to represent the 'worst prognostic tumor cells' was then established. The similarity between each spectrum of tissues and the standard spectrum was assessed by ED, to provide a direct assessment on the prognosis status. We have successfully classified the patients into poor and favorable prognosis groups, either based on the averaged regional ED values (sensitivity of 75 %, specificity of 96.8 %), or based on the minimal ED values at the patient level (sensitivity of 90 %, specificity of 100 %). EDRS was also investigated for survival analysis (AUC = 0.955), much better than the commonly applied post-neoadjuvant therapy (ypTNM) category (AUC = 0.718). Our work highlights EDRS as a rapid, accurate, low-cost and robust tool for heterogeneous cancer-related prognosis assessment and survival prediction, providing new insights for spectroscopic tumor analysis.

Advanced materials and technologies for oral diseases

Oral disease, as a class of diseases with very high morbidity, brings great physical and mental damage to people worldwide. The increasing burden and strain on individuals and society make oral diseases an urgent global health problem. Since the treatment of almost all oral diseases relies on materials, the rapid development of advanced materials and technologies has also promoted innovations in the treatment methods and strategies of oral diseases. In this review, we systematically summarized the application strategies in advanced materials and technologies for oral diseases according to the etiology of the diseases and the comparison of new and old materials. Finally, the challenges and directions of future development for advanced materials and technologies in the treatment of oral diseases were refined. This review will guide the fundamental research and clinical translation of oral diseases for practitioners of oral medicine.

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.

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.

Raman Imaging and Fluorescence Lifetime Imaging Microscopy for Diagnosis of Cancer State and Metabolic Monitoring

Hurdles for effective tumor therapy are delayed detection and limited effectiveness of systemic drug therapies by patient-specific multidrug resistance. Non-invasive bioimaging tools such as fluorescence lifetime imaging microscopy (FLIM) and Raman-microspectroscopy have evolved over the last decade, providing the potential to be translated into clinics for early-stage disease detection, in vitro drug screening, and drug efficacy studies in personalized medicine. Accessing tissue- and cell-specific spectral signatures, Raman microspectroscopy has emerged as a diagnostic tool to identify precancerous lesions, cancer stages, or cell malignancy. In vivo Raman measurements have been enabled by recent technological advances in Raman endoscopy and signal-enhancing setups such as coherent anti-stokes Raman spectroscopy or surface-enhanced Raman spectroscopy. FLIM enables in situ investigations of metabolic processes such as glycolysis, oxidative stress, or mitochondrial activity by using the autofluorescence of co-enzymes NADH and FAD, which are associated with intrinsic proteins as a direct measure of tumor metabolism, cell death stages and drug efficacy. The combination of non-invasive and molecular-sensitive in situ techniques and advanced 3D tumor models such as patient-derived organoids or microtumors allows the recapitulation of tumor physiology and metabolism in vitro and facilitates the screening for patient-individualized drug treatment options.

Nanostructure-based surface-enhanced Raman scattering for diagnosis of cancer

Cancer is a malignant disease that seriously affects human health and life. Early diagnosis and timely treatment can significantly improve the survival rate of cancer patients. Surface-enhanced Raman scattering (SERS) is an optical technology that can detect and image samples at the single-molecule level. It has the advantages of rapidity, high specificity, high sensitivity and no damage to the sample. The performance of SERS is highly dependent on the properties, size and morphology of the SERS substrate. Preparation of SERS substrates with good reproducibility and chemical stability is a key factor in realizing the wide application of SERS technology in cancer diagnosis. In this review we provide a detailed presentation of the latest research on SERS in cancer diagnosis and the detection of cancer biomarkers, mainly focusing on nanotechnological approaches in cancer diagnosis by using SERS. We also consider the future development of nanostructure-based SERS in cancer diagnosis.

Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) spectra of salivary glands carcinoma, tumor and healthy tissues and their homogenates analyzed by chemometry: Towards development of the novel tool for clinical diagnosis

In this study, two approaches to salivary glands studies are presented: Raman imaging (RI) of tissue cross-section and surface-enhanced Raman spectroscopy (SERS) of tissue homogenates prepared according to elaborated protocol. Collected and analyzed data demonstrate the significant potential of SERS combined with multivariate analysis for distinguishing carcinoma or tumor from the normal salivary gland tissues as a rapid, label-free tool in cancer detection in oncological diagnostics. Raman imaging allows a detailed analysis of the cell wall's chemical composition; thus, the compound's distribution can be semi-quantitatively analyzed, while SERS of tissue homogenates allow for detailed analysis of all moieties forming these tissues. In this sense, SERS is more sensitive and reliable to study any changes in the area of infected tissues. Principal component analysis (PCA), as an unsupervised pattern recognition method, was used to identify the differences in the SERS salivary glands homogenates. The partial least squares-discriminant analysis (PLS-DA), the supervised pattern classification technique, was also used to strengthen further the computed model based on the latent variables in the SERS spectra. Moreover, the chemometric quantification of obtained data was analyzed using principal component regression (PCR) multivariate calibration. The presented data prove that the PCA algorithm allows for 91% in seven following components and the determination between healthy and tumor salivary gland homogenates. The PCR and PLS-DA methods predict 90% and 95% of the variance between the studied groups (in 6 components and 4 factors, respectively). Moreover, according to calculated RMSEC (RMSEP), R2C (R2P) values and correlation accuracy (based on the ROC curve), the PLS-DA model fits better for the studied data. Thus, SERS methods combined with PLS-DA analysis can be used to differentiate healthy, neoplastic, and mixed tissues as a competitive tool in relation to the commonly used method of histopathological staining of tumor tissue.

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.

Rapid and noninvasive diagnosis of oral and oropharyngeal cancer based on micro-Raman and FT-IR spectra of saliva

Fast, sensitive, and noninvasive techniques are needed for better health care management, particularly when traditional biopsies could be replaced with appropriate analyses of body fluids, such as saliva. Here is presented a proof-of-concept study, which aims to test a recently developed saliva samples preparation method, for oral and oropharyngeal cancer diagnosis, using micro-Raman and Fourier transform infrared (FT-IR) spectroscopic techniques. The detected biomarker bands and the cancer classification rates are compared and discussed. Saliva samples were collected from healthy donors and pathologically confirmed oral and oropharyngeal cancer patients. Principal components analysis (PCA) and principal components analysis-linear discriminant analysis (PCA-LDA) chemometric methods were applied to build discrimination models for the test and control groups. Based on the differences between salivary spectra of healthy and cancer patients, several biomarker bands were identified. Noteworthy, a significant vibrational biomarker band at 2064 cm^-1, assigned to thiocyanate, was observed in both the FT-IR and Raman data-set. Other cancer characteristic Raman bands were 754 cm^-1 (tryptophan), 530 and 927 cm^-1 (lysozyme), 1001 cm^-1 (phenylalanine), while the FT-IR biomarker band was located at 1075 cm^-1 (phosphodiester bonds stretching in DNA, RNA). The oral and oropharyngeal cancer was classified with an accuracy of 90% based on the micro-Raman data and 82% based on the FT-IR data set, respectively. The study showed that oral and oropharyngeal cancer can be differentiated from control saliva samples based on their respective micro-Raman and FT-IR spectral signatures, due to the biomolecular modifications induced by the disease.

Recent advances in point-of-care diagnostics for oral cancer

Early-stage diagnosis is a crucial step in reducing the mortality rate in oral cancer cases. Point-of-care (POC) devices for oral cancer diagnosis hold great future potential in improving the survival rates as well as the quality of life of oral cancer patients. The conventional oral examination followed by needle biopsy and histopathological analysis have limited diagnostic accuracy. Besides, it involves patient discomfort and is not feasible in resource-limited settings. POC detection of biomarkers and diagnostic adjuncts has emerged as non- or minimally invasive tools for the diagnosis of oral cancer at an early stage. Various biosensors have been developed for the rapid detection of oral cancer biomarkers at the point-of-care. Several optical imaging methods have also been employed as adjuncts to detect alterations in oral tissue indicative of malignancy. This review summarizes the different POC platforms developed for the detection of oral cancer biomarkers, along with various POC imaging and cytological adjuncts that aid in oral cancer diagnosis, especially in low resource settings. Various immunosensors and nucleic acid biosensors developed to detect oral cancer biomarkers are summarized with examples. The different imaging methods used to detect oral tissue malignancy are also discussed herein. Additionally, the currently available commercial devices used as adjuncts in the POC detection of oral cancer are emphasized along with their characteristics. Finally, we discuss the limitations and challenges that persist in translating the developed POC techniques in the clinical settings for oral cancer diagnosis, along with future perspectives.

Optical diagnosis of oral cavity lesions by label-free Raman spectroscopy

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers and frequently preceded by non-malignant lesions. Using Shifted-Excitation Raman Difference Spectroscopy (SERDS), principal component and linear discriminant analysis in native tissue specimens, 9500 raw Raman spectra of OSCC, 4300 of non-malignant lesions and 4200 of physiological mucosa were evaluated. Non-malignant lesions were distinguished from physiological mucosa with a classification accuracy of 95.3% (95.4% sensitivity, 95.2% specificity, area under the curve (AUC) 0.99). Discriminating OSCC from non-malignant lesions showed an accuracy of 88.4% (93.7% sensitivity, 76.7% specificity, AUC 0.93). OSCC was identified against physiological mucosa with an accuracy of 89.8% (93.7% sensitivity, 81.0% specificity, AUC 0.90). These findings underline the potential of SERDS for the diagnosis of oral cavity lesions.

Towards a point-of-care SERS sensor for biomedical and agri-food analysis applications: a review of recent advancements

The growing demand for reliable and robust methodology in bio-chemical sensing calls for the continuous advancement of sensor technologies. Over the last two decades, surface-enhanced Raman spectroscopy (SERS) has emerged as one of the most promising analytical techniques for sensitive and trace analysis or detection in biomedical and agri-food applications. SERS overcomes the inherent sensitivity limitation associated with Raman spectroscopy, which provides vibrational "fingerprint" spectra of molecules that makes it unique and versatile among other spectroscopy techniques. This paper comprehensively reviews the recent advancements of SERS for biomedical, food and agricultural applications over the last 6 years, and we envision that, in the near future, some of these platforms have the potential to be translated as a point-of-care and rapid sensor for real-life end-user applications. The merits and limitations of various SERS sensor designs are analysed and discussed based on critical features such as sensitivity, specificity, usability, repeatability and reproducibility. We conclude by highlighting the opportunities and challenges in the field while stressing the technological gaps to be addressed in realizing commercially viable point-of-care SERS sensors for practical biomedical and agri-food technological applications.

Plasma-Enabled Amorphous TiO2 Nanotubes as Hydrophobic Support for Molecular Sensing by SERS

We devise a unique heteronanostructure array to overcome a persistent issue of simultaneously utilizing the surface-enhanced Raman scattering, inexpensive, Earth-abundant materials, large surface areas, and multifunctionality to demonstrate near single-molecule detection. Room-temperature plasma-enhanced chemical vapor deposition and thermal evaporation provide high-density arrays of vertical TiO₂ nanotubes decorated with Ag nanoparticles. The role of the TiO₂ nanotubes is 3-fold: (i) providing a high surface area for the homogeneous distribution of supported Ag nanoparticles, (ii) increasing the water contact angle to achieve superhydrophobic limits, and (iii) enhancing the Raman signal by synergizing the localized electromagnetic field enhancement (Ag plasmons) and charge transfer chemical enhancement mechanisms (amorphous TiO₂) and by increasing the light scattering because of the formation of vertically aligned nanoarchitectures. As a result, we reach a Raman enhancement factor of up to 9.4 × 10⁷, satisfying the key practical device requirements. The enhancement mechanism is optimized through the interplay of the optimum microstructure, nanotube/shell thickness, Ag nanoparticles size distribution, and density. Vertically aligned amorphous TiO₂ nanotubes decorated with Ag nanoparticles with a mean diameter of 10-12 nm provide enough sensitivity for near-instant concentration analysis with an ultralow few-molecule detection limit of 10^-12 M (Rh6G in water) and the possibility to scale up device fabrication.

Cancer Diagnosis through SERS and Other Related Techniques

Cancer heterogeneity increasingly requires ultrasensitive techniques that allow early diagnosis for personalized treatment. In addition, they should preferably be non-invasive tools that do not damage surrounding tissues or contribute to body toxicity. In this context, liquid biopsy of biological samples such as urine, blood, or saliva represents an ideal approximation of what is happening in real time in the affected tissues. Plasmonic nanoparticles are emerging as an alternative or complement to current diagnostic techniques, being able to detect and quantify novel biomarkers such as specific peptides and proteins, microRNA, circulating tumor DNA and cells, and exosomes. Here, we review the latest ideas focusing on the use of plasmonic nanoparticles in coded and label-free surface-enhanced Raman scattering (SERS) spectroscopy. Moreover, surface plasmon resonance (SPR) spectroscopy, colorimetric assays, dynamic light scattering (DLS) spectroscopy, mass spectrometry or total internal reflection fluorescence (TIRF) microscopy among others are briefly examined in order to highlight the potential and versatility of plasmonics.

WNT7A Promotes EGF-Induced Migration of Oral Squamous Cell Carcinoma Cells by Activating β-Catenin/MMP9-Mediated Signaling

Aims and hypothesis

Epidermal growth factor (EGF) has been shown to induce the migration of various cancer cells. However, the underlying signaling mechanisms for EGF-induced migration of oral squamous cell carcinoma (OSCC) remain to be elucidated. WNT7A, a member of the family of 19 Wnt secreted glycoproteins, is commonly associated with tumor development. It is mostly unknown whether and, if so, how EGF modulates WNT7A in OSCC cells. The role of WNT7A in OSCC was thus investigated to explore the underlying signaling mechanisms for EGF-induced migration of OSCC.

Methods

Cell migration was measured by Wound healing assay and Transwell assay. Western blotting was carried out to detect the expression of WNT7A, MMP9, β-catenin, p-AKT, and p-ERK. The cells were transfected with plasmids or siRNA to upregulate or downregulate the expression of WNT7A. The location of β-catenin was displayed by immunofluorescence microscopy. Immunohistochemistry was carried out to confirm the relation between WNT7A expression and OSCC progression.

Results

The present study showed that the levels of WNT7A mRNA and protein were increased by EGF stimulation in OSCC cells. Besides, it was proved that p-AKT, but not p-ERK, mediated the expression of WNT7A protein induced by EGF. Furthermore, the inhibition of AKT activation prevented the EGF-induced increase of WNT7A and matrix metallopeptidase 9 (MMP9) expression and translocation of β-catenin from the cytoplasm to the nucleus. Moreover, histological analysis of OSCC specimens revealed an association between WNT7A expression and poor clinical prognosis of the disease.

Conclusions

The data in this paper indicated that WNT7A could be a potential oncogene in OSCC and identified a novel PI3K/AKT/WNT7A/β-catenin/MMP9 signaling for EGF-induced migration of OSCC cells.

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.