Cancer Diagnosis through SERS and Other Related Techniques

Raman spectroscopy analysis combined with computed tomography imaging to identify microsatellite instability in gastric cancers

Accurate determination of microsatellite instability (MSI) status is critical for tailoring treatment approaches for gastric cancer patients. Existing clinical techniques for MSI diagnosis are plagued by problems of suboptimal time efficiency, high cost, and burdensome experimental requirements. Here, we for the first time establish the classification model of gastric cancer MSI status based on Raman spectroscopy. To begin with, we reveal that tumor heterogeneity-induced signal variations pose a prominent impact on MSI classification. To eliminate this issue, we develop Euclidean distance-based Raman Spectroscopy (EDRS) algorithm, which establishes a standard spectrum to represent the "most microsatellite stable" status. The similarity between each spectrum of tissues with the standard spectrum is calculated to provide a direct assessment on the MSI status. Compared to machine learning-algorithms including k-Nearest Neighbors, Random Forest, and Extreme Learning Machine, the EDRS method shows the highest accuracy of 94.6 %. Finally, we integrate the EDRS method with the clinical diagnostic modality, computed tomography, to construct an innovative joint classification model with good classification performance (AUC = 0.914, Accuracy = 94.6 %). Our work demonstrates a robust, rapid, non-invasive, and convenient tool in identifying the MSI status, and opens new avenues for Raman techniques to fit into existing clinical workflow.

Identification of blood at simulated crime scenes using silver nanoparticles with SERS

The analysis of substances and samples obtained from a crime scene is very important in solving forensic cases. To determine the variables involved in a crime and to expedite the investigation process, the rapid analysis of body fluids in small quantities and within environments containing diverse components is particularly necessary. For this reason, it is of great importance to analyze biological fluids with rapid, noncontaminating, nondestructive, low-cost, and accurate techniques. In recent years, with advancements in laser technology, spectroscopic methods have been introduced as analytical techniques in forensic medicine and chemical studies. This study focuses on surface-enhanced Raman spectroscopy (SERS) to demonstrate the detection of blood samples in simulated crime scenes. To minimize the background signal from fluorescent biomolecules in blood, dilution was performed with two different components and Raman analysis was performed for four different concentrations of blood. In general, a decrease in noise in the spectra was observed as the blood was diluted. Crime scenes consisting of pure blood, blood diluted with ethanol and distilled water (1:2, 1:4, and 1:8), a blood-mineral water mixture, a blood-cherry juice mixture, and silver nanoparticle-added mixtures were simulated, and their spectra were examined. Chemometric analyses of the data were performed. Despite high noise and low peak intensities, blood-identifying signals were detected when examining different blood concentrations. It was observed that silver nanoparticles provided high enhancement of blood peaks thanks to their strong plasmonic properties.

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.

Label-Free SERS of Urine Components: A Powerful Tool for Discriminating Renal Cell Carcinoma through Multivariate Analysis and Machine Learning Techniques

The advent of Surface-Enhanced Raman Scattering (SERS) has enabled the exploration and detection of small molecules, particularly in biological fluids such as serum, blood plasma, urine, saliva, and tears. SERS has been proposed as a simple diagnostic technique for various diseases, including cancer. Renal cell carcinoma (RCC) ranks as the sixth most commonly diagnosed cancer in men and is often asymptomatic, with detection occurring incidentally. The onset of symptoms typically aligns with advanced disease, aggressive histology, and unfavorable prognosis, and therefore new methods for an early diagnosis are needed. In this study, we investigated the utility of label-free SERS in urine, coupled with two multivariate analysis approaches: Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) and Support Vector Machine (SVM), to discriminate between 50 RCC patients and 44 healthy donors. Employing LDA-PCA, we achieved a discrimination accuracy of 100% using 13 principal components, and an 88% accuracy in discriminating between different RCC stages. The SVM approach yielded a training accuracy of 100%, a validation accuracy of 99% for discriminating between RCC and controls, and an 80% accuracy for discriminating between stages. The comparative analysis of raw and normalized SERS spectral data shows that while raw data disclose relative concentration variations in urine metabolites between the two classes, the normalization of spectral data significantly improves the accuracy of discrimination. Moreover, the selection of principal components with markedly distinct scores between the two classes serves to alleviate overfitting risks and reduces the number of components employed for discrimination. We obtained the accuracy of the discrimination between the RCC patients cases and healthy donors of 90% for three PCs and a linear discrimination function, and a 88% accuracy of discrimination between stages using six PCs, mitigating practically the risk of overfitting and increasing the robustness of our analysis. Our findings underscore the potential of label-free SERS of urine in conjunction with chemometrics for non-invasive and early RCC detection.

Diatomite-Based, Flexible SERS Immunosensor Platform for Rapid, Specific, and Sensitive Detection of Circulating Cancer-Specific Protein Biomarkers in Serum Using Raman Probes

Cancer is one of the most actively researched diseases having a high mortality rate when not detected at an early stage. Thus, rapid, simultaneous, and sensitive quantification of cancer biomarkers plays an important role in early diagnosis, with patient impact to disability adjusted life years. Herein, a diatomite-based SERS flexible platform for the rapid and sensitive detection of circulating cancer-specific protein biomarkers in serum is presented. In this approach, diatomite/AgNPs strips with maximum SERS activity prepared using the layer-by-layer (LbL) technique were modified with specific antibodies, and specific antigens (HER2, CA15-3, PSA, and MUC4) were captured and detected. By using Raman probes specific to the captured antigens in serum, a SERS limit of detection (LOD) of 0.1 ng/mL was measured (calculated LOD < 0.1 ng/mL). This value is lower than the cutoff amount of cancer antigens in the person's blood. The specificity for the antigens of each antibody was calculated to be higher than 95%. As a result, an immunosensor for rapid detection of cancer biomarkers in serum with good specificity, high sensitivity, good reproducibility, and low cost has been demonstrated. Overall, we show that the prepared diatomite-based SERS substrate with a high surface-to-volume ratio is a useable platform for immunoassay tests.

Surface-Enhanced Raman Spectroscopy Substrate Time Stability Improvement Using an External Oxygen Barrier Method

The poor time stability of surface-enhanced Raman scattering (SERS) substrates greatly limits their application potential. Although core-shell structures are commonly used to enhance stability, their complex preparation processes, high costs, and susceptibility under acidic or alkaline conditions result in serious disadvantages for practical applications. Here, we propose a new method of external oxygen barrier to improve spectral stability, in which SERS substrates are stored in an oxygen-free environment. Controlled experiments are carried out under air and vacuum. Raman spectrum intensity is measured 11 times within six months for each group. Using the attenuation formula, the Raman spectrum intensity decay results of each SERS substrate over time are obtained. The effectiveness of the external oxygen barrier method is demonstrated through curve fitting using the corresponding function. The substrate spectral attenuation rates of the vacuum group and the argon group within six months are <20%, proving the effectiveness of the external oxygen barrier method.

Acoustofluidic lysis of cancer cells and Raman spectrum profiling

The lysis of cancer cells inside a sessile droplet was performed using traveling surface acoustic waves (SAWs) without any chemical reagents. Raman spectrum profiling was then carried out to explore detailed cell-derived data. The Rayleigh waves formed by an interdigital transducer were made to propagate along the surface of an LiNbO3 substrate. Polystyrene microparticles (PSMPs) were used to establish mechanical cell lysis effectively, and gold nanoparticles (AuNPs) were added to enhance the Raman signals from the lysed cells by SAWs. The lysis efficiency was evaluated according to the size and concentration of the PSMPs in experiments where the frequency was varied. Lysis occurred mainly by mechanical collision using PSMPs in a high-frequency domain, and the lysis efficiency was improved by increasing the application time and the energy density of the SAWs. Raman signals from the lysed cells were greatly enhanced by nanogaps formed by the AuNPs, which were evenly distributed irrespective of the SAWs through the frequency-independent behavior of the AuNPs. Finally, detailed Raman spectra of MDA-MB-231, malignant breast cancer cells, were acquired, and various organic matter-derived peaks were observed. The 95% confidence region for cells subjected to lysis was more widely distributed than that of cells not subjected to lysis. The proposed SAW platform is expected to facilitate the detection of small quantities and to be applied in biomedical applications.

Layer-by-layer coating of natural diatomite with silver nanoparticles for identification of circulating cancer protein biomarkers using SERS

Surface-enhanced Raman scattering (SERS) is an emerging spectroscopy technique for detecting and characterizing chemical or biological structures in the vicinity of plasmonic nanostructures. Colloidal, solid, and flexible nanostructures are widely used in SERS experiments to enhance the Raman intensity. The nanostructure used in SERS is one of the main influencing parameters and a growing research area. Fabrication of simple and cheap SERS substrates with a high enhancement factor is desired. In this study, we fabricated a reproducible, cheap, and flexible SERS active strip by coating natural diatomite (biosilica) with silver nanoparticles (AgNPs) using the layer-by-layer assembly method and the fabricated strip is used for the label-free identification of circulating cancer protein biomarkers. SERS active strips were fabricated having different numbers of AgNP layers on natural diatomite and comprehensive characterization techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), and UV/vis absorption spectrophotometry were used. SERS activities of the strips depending on the number of layers were evaluated using 4-aminothiophenol (4-ATP) and rhodamine 6G (Rh6G) molecules. We found that the SERS intensity is strongly dependent on the number of AgNP layers, with the maximum SERS intensity obtained from the strip with 5 layers of AgNPs, having a 2.0 × 105 enhancement factor. The strip with the highest SERS activity was used for the label-free identification of circulating cancer protein biomarkers (HER2, CA15-3, PSA, MUC4, and CA27-29). The results demonstrate that the fabricated strip can help in the effective label-free identification of circulating protein biomarkers and open new directions for SERS-based label-free biosensing applications.

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

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

Nanoplatforms: The future of oral cancer treatment

Background and Aims

Cytotoxicity is a key disadvantage of using chemotherapeutic drugs to treat cancer. This can be overcome by encapsulating chemotherapeutic drugs in suitable carriers for targeted delivery, allowing them to be released only at the cancerous sites. Herein, we aim to review the recent scientific developments in the utilization of nanotechnology-based drug delivery systems for treating oral malignancies that can lead to further improvements in clinical practice.

Methods

A comprehensive literature search was conducted on PubMed, Google Scholar, ScienceDirect, and other notable databases to identify recent peer-reviewed clinical trials, reviews, and research articles related to nanoplatforms and their applications in oral cancer treatment.

Results

Nanoplatforms offer a revolutionary strategy to overcome the challenges associated with conventional oral cancer treatments, such as poor drug solubility, non-specific targeting, and systemic toxicity. These nanoscale drug delivery systems encompass various formulations, including liposomes, polymeric nanoparticles, dendrimers, and hydrogels, which facilitate controlled release and targeted delivery of therapeutic agents to oral cancer sites. By exploiting the enhanced permeability and retention effect, Nanoplatforms accumulate preferentially in the tumor microenvironment, increasing drug concentration and minimizing damage to healthy tissues. Additionally, nanoplatforms can be engineered to carry multiple drugs or a combination of drugs and diagnostic agents, enabling personalized and precise treatment approaches.

Conclusion

The utilization of nanoplatforms in oral cancer treatment holds significant promise in revolutionizing therapeutic strategies. Despite the promising results in preclinical studies, further research is required to evaluate the safety, efficacy, and long-term effects of nanoformulations in clinical settings. If successfully translated into clinical practice, nanoplatform-based therapies have the potential to improve patient outcomes, reduce side effects, and pave the way for more personalized and effective oral cancer treatments.

Development of Responsive Nanoparticles for Cancer Therapy

Great efforts are focused on the development of safe nano-carriers for the treatment of cancer in order to overcome some of the typical limitations of conventional therapies [...].

Ultrasensitive miRNA-21 Biosensor Based on Zn(TCPP) PET-RAFT Polymerization Signal Amplification and Multiple Logic Gate Molecular Recognition

Quantitative measurement of microRNAs (miRNAs) is extremely important in plenty of biomedical applications especially cancer diagnosis but remains a great challenge. In this work, we developed a logic gate recognition biosensing platform based on the "trinity" molecular recognition mode for quantifying miRNAs with a detection limit of 4.48 aM, along with a linear range from 0.1 nM to 10 aM under optimal experimental conditions. In order to obtain excellent detection performance, we adopted a Zn(TCPP) photocatalytic electron transfer reversible addition-fragmentation chain transfer (PET-RAFT) polymerization signal amplification strategy. The light-induced PET-RAFT has developed green applications of free radical polymerization in the field of biosensors. This is the first report on the preparation of signal amplification biosensors using PET-RAFT for tumor marker detection. With the outstanding detection performance, we can apply the sensor system to the early screening of lung cancer patients.

Accuracy of Raman spectroscopy in the diagnosis of Alzheimer's disease

Objective

To systematically evaluate the accuracy of Raman spectroscopy in the diagnosis of Alzheimer's disease.

Methods

Databases including Web of Science, PubMed, The Cochrane Library, EMbase, CBM, CNKI, Wan Fang Data, and VIP were electronically searched for studies on Raman spectroscopy in diagnosis of Alzheimer's disease from inception to November 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias in the included studies. Then, meta-analysis was performed using Meta-Disc1.4 and Stata 16.0 software.

Results

A total of eight studies were finally included. The pooled sensitivity of Raman spectroscopy was 0.86 [95% CI (0.80–0.91)], specificity was 0.87 [95% CI (0.79–0.92)], positive likelihood ratio was 5.50 [95% CI (3.55–8.51)], negative likelihood ratio was 0.17 [95% CI (0.09–0.34)], diagnosis odds ratio and area under the curve of SROC were 42.44 [95% CI (19.80–90.97)] and 0.931, respectively. Sensitivity analysis was carried out after each study was excluded one by one, and the results showed that pooled sensitivity and specificity had no significant change, indicating that the stability of the meta-analysis results was great.

Conclusions

Our findings indicated that Raman spectroscopy had high accuracy in the diagnosis of AD, though it still did not rule out the possibility of misdiagnosis and missed diagnosis. Limited by the quantity and quality of the included studies, the above conclusions need to be verified by more high-quality studies.

Bioorthogonal surface-enhanced Raman scattering flower-like nanoprobe with embedded standards for accurate cancer cell imaging

Developing precise and effective strategies for cancer identification and imaging is attractive due to their importance for early cancer detection, prognosis, and subsequent treatment. Herein, we reported a novel bioorthogonal surface-enhanced Raman scattering (SERS) nanoprobe for accurate cancer cell imaging. A novel core-molecule-shell nanoflower (Au@4-MBN@Au) with rich electromagnetic hot spots and enhanced Raman scattering was first synthesized by optimizing the embedded concentrations of 4-mercaptobenzonitrile (4-MBN). Then, Au@4-MBN@Au was further modified with FA-PEG-SH molecules to acquire the bioorthogonal SERS nanoprobe Au@4-MBN@Au-PEG-FA. The SERS nanoprobe illustrated a robust and stable nitrile stretching vibration Raman signal (2223 cm^-1) in the cellular silent region, ensuring high sensitivity and ultra-accuracy SERS imaging of cancer cells. Furthermore, cell imaging results demonstrated Au@4-MBN@Au-PEG-FA could recognize FR-positive HeLa cells with high selectivity due to the high affinity between folate receptor and folic acid. More notably, Au@4-MBN@Au-PEG-FA has been applied to identify FR-positive Hela cells from co-cultured cancer cells with similar morphology by SERS imaging for the first time. With improved signal-to-background ratio, high selectivity, and excellent stability, we anticipate the SERS nanoprobe Au@4-MBN@Au-PEG-FA could be applied for FR-related cancer theranostics and clinical detection in the future.

Label-free surface-enhanced Raman spectroscopy of serum with machine-learning algorithms for gallbladder cancer diagnosis

Gallbladder cancer (GBC) is a rare but frequently fatal biliary tract malignancy that is typically only discovered when it is already advanced. In the search of an efficient diagnosis method. Therefore, in this study, we investigated a novel technique for the quick and non-invasive diagnosis of GBC based on serum surface-enhanced Raman spectroscopy (SERS). SERS spectra of serum from 41 patients with GBC and 72 normal subjects were recorded. Principal component analysis-linear discriminant analysis (PCA-LDA), and PCA-support vector machine (PCA-SVM), Linear SVM and Gaussian radial basis function-SVM (RBF-SVM) algorithms were used to establish the classification models, respectively. When the Linear SVM was used, the overall diagnostic accuracy for classifying the two groups could achieve 97.1%, and when RBF-SVM was used, the diagnostic sensitivity of GBC was 100%. The results demonstrated that SERS in combination with a machine learning algorithm is a promising candidate to be one of the diagnostic tools for GBC in the future.

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

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Rapid Identification of Benign Gallbladder Diseases Using Serum Surface-Enhanced Raman Spectroscopy Combined with Multivariate Statistical Analysis

In this study, we looked at the viability of utilizing serum to differentiate between gallbladder (GB) stones and GB polyps using Surface-enhanced Raman spectroscopy (SERS), which has the potential to be a quick and accurate means of diagnosing benign GB diseases. Rapid and label-free SERS was used to conduct the tests on 148 serum samples, which included those from 51 patients with GB stones, 25 patients with GB polyps and 72 healthy persons. We used an Ag colloid as a Raman spectrum enhancement substrate. In addition, we employed orthogonal partial least squares discriminant analysis (OPLS-DA) and principal component linear discriminant analysis (PCA-LDA) to compare and diagnose the serum SERS spectra of GB stones and GB polyps. The diagnostic results showed that the sensitivity, specificity, and area under curve (AUC) values of the GB stones and GB polyps based on OPLS-DA algorithm reached 90.2%, 97.2%, 0.995 and 92.0%, 100%, 0.995, respectively. This study demonstrated an accurate and rapid means of combining serum SERS spectra with OPLS-DA to identify GB stones and GB polyps.

Isolation, Detection and Analysis of Circulating Tumour Cells: A Nanotechnological Bioscope

Cancer is one of the dreaded diseases to which a sizeable proportion of the population succumbs every year. Despite the tremendous growth of the health sector, spanning diagnostics to treatment, early diagnosis is still in its infancy. In this regard, circulating tumour cells (CTCs) have of late grabbed the attention of researchers in the detection of metastasis and there has been a huge surge in the surrounding research activities. Acting as a biomarker, CTCs prove beneficial in a variety of aspects. Nanomaterial-based strategies have been devised to have a tremendous impact on the early and rapid examination of tumor cells. This review provides a panoramic overview of the different nanotechnological methodologies employed along with the pharmaceutical purview of cancer. Initiating from fundamentals, the recent nanotechnological developments toward the detection, isolation, and analysis of CTCs are comprehensively delineated. The review also includes state-of-the-art implementations of nanotechnological advances in the enumeration of CTCs, along with future challenges and recommendations thereof.

Nano-diagnostics as an emerging platform for oral cancer detection: Current and emerging trends

Globally, oral cancer kills an estimated 150,000 individuals per year, with 300,000 new cases being diagnosed annually. The high incidence rate of oral cancer among the South-Asian and American populations is majorly due to overuse of tobacco, alcohol, and poor dental hygiene. Additionally, socio-economic issues and lack of general awareness delay the primary screening of the disease. The availability of early screening techniques for oral cancer can help in carving out a niche for accurate disease prognosis and also its prevention. However, conventional diagnostic approaches and therapeutics are still far from optimal. Thus, enhancing the analytical performance of diagnostic platforms in terms of specificity and precision can help in understanding the disease progression paradigm. Fabrication of efficient nanoprobes that are sensitive, noninvasive, cost-effective, and less labor-intensive can reduce the global cancer burden. Recent advances in optical, electrochemical, and spectroscopy-based nano biosensors that employ noble and superparamagnetic nanoparticles, have been proven to be extremely efficient. Further, these sensitive nanoprobes can also be employed for predicting disease relapse after chemotherapy, when the majority of the biomarker load is eliminated. Herein, we provide the readers with a brief summary of conventional and new-age oral cancer detection techniques. A comprehensive understanding of the inherent challenges associated with conventional oral cancer detection techniques is discussed. We also elaborate on how nanoparticles have shown tremendous promise and effectiveness in radically transforming the approach toward oral cancer detection. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > Diagnostic Nanodevices Diagnostic Tools > In Vitro Nanoparticle-Based Sensing.

Label-Free Surface Enhanced Raman Spectroscopy for Cancer Detection

Simple Summary

Blood is considered a rich reservoir of biomarkers for disease diagnosis. Surface-enhanced Raman scattering (SERS) is known for its high sensitivity and has been successfully employed to differentiate blood samples from cancer patients versus healthy individuals. Different from previous reports, this study aims at investigating the reliability of the observed results by varying several parameters influencing the observed spectra. Thus, blood taken from 30 healthy individuals as the control group, 30 patients with different types of cancers, and 15 patients with various types of chronic diseases were used in the study. The results revealed that spectral differences in the cancer group was directly related to the presence of cancer-related biomarkers. Although data were obtained from only small group of patients, the recorded sensitivity and specificity values clearly show the power of the technique to detect cancer.

Abstract

Blood is a vital reservoir housing numerous disease-related metabolites and cellular components. Thus, it is also of interest for cancer diagnosis. Surface-enhanced Raman spectroscopy (SERS) is widely used for molecular detection due to its very high sensitivity and multiplexing properties. Its real potential for cancer diagnosis is not yet clear. In this study, using silver nanoparticles (AgNPs) as substrates, a number of experimental parameters and scenarios were tested to disclose the potential for this technique for cancer diagnosis. The discrimination of serum samples from cancer patients, healthy individuals and patients with chronic diseases was successfully demonstrated with over 90% diagnostic accuracies. Moreover, the SERS spectra of the blood serum samples obtained from cancer patients before and after tumor removal were compared. It was found that the spectral pattern for serum from cancer patients evolved into the spectral pattern observed with serum from healthy individuals after the removal of tumors. The data strongly suggests that the technique has a tremendous potential for cancer detection and screening bringing the possibility of early detection onto the table.

Diagnosis accuracy of Raman spectroscopy in the diagnosis of breast cancer: a meta-analysis

To investigate the diagnostic efficiency of Raman spectroscopy for the diagnosis of breast cancer, we searched PubMed, Web of Science, Cochrane Library, and Embase for articles published from the database establishment to May 20, 2022. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the receiver pooled operating characteristic curve were derived for the included studies as outcome measures. The methodological quality was assessed according to the questionnaires and criteria suggested by the Diagnostic Accuracy Research Quality Assessment-2 tool. Sixteen studies were included in this meta-analysis. The pooled sensitivity and specificity of Raman spectroscopy for breast cancer diagnosis were 0.97 (95% CI, [0.92-0.99]) and 0.96 (95% CI, [0.91-0.98]). The diagnostic odds ratio was 720.89 (95% CI, [135.73-3828.88]) and the area under the curve of summary receiver operating characteristic curves was 0.99 (95% CI, [0.98-1]). Subgroup analysis revealed that all subgroup types in our analysis, including different races, sample types, diagnostic algorithms, number of spectra, instrument types, and laser wavelengths, turned out to have a sensitivity and specificity greater than 0.9. Significant heterogeneity was found between studies. Deeks' funnel plot demonstrated that publication bias was acceptable. This meta-analysis suggests that Raman spectroscopy may be an effective and accurate tool to differentiate breast cancer from normal breast tissue, which will help us diagnose and treat breast cancer.

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.

SERS liquid biopsy in breast cancer. What can we learn from SERS on serum and urine?

SERS analysis of biofluids, coupled with classification algorithms, has recently emerged as a candidate for point-of-care medical diagnosis. Nonetheless, despite the impressive results reported in the literature, there are still gaps in our knowledge of the biochemical information provided by the SERS analysis of biofluids. Therefore, by a critical assignment of the SERS bands, our work aims to provide a systematic analysis of the molecular information that can be achieved from the SERS analysis of serum and urine obtained from breast cancer patients and controls. Further, we compared the relative performance of five different machine learning algorithms for breast cancer and control samples classification based on the serum and urine SERS datasets, and found comparable classification accuracies in the range of 61-89%. This result is not surprising since both biofluids show striking similarities in their SERS spectra providing similar metabolic information, related to purine metabolites. Lastly, by carefully comparing the two datasets (i.e., serum and urine) we show that it is possible to link the misclassified samples to specific metabolic imbalances, such as carotenoid levels, or variations in the creatinine concentration.

Gold Nanoparticles Based Optical Biosensors for Cancer Biomarker Proteins: A Review of the Current Practices

Cancer prognosis depends on the early detection of the disease. Gold nanoparticles (AuNPs) have attracted much importance in biomedical research due to their distinctive optical properties. The AuNPs are easy to fabricate, biocompatible, surface controlled, stable, and have surface plasmonic properties. The AuNPs based optical biosensors can intensely improve the sensitivity, specificity, resolution, penetration depth, contrast, and speed of these devices. The key optical features of the AuNPs based biosensors include localized surface plasmon resonance (LSPR), SERS, and luminescence. AuNPs based biomarkers have the potential to sense the protein biomarkers at a low detection level. In this review, the fabrication techniques of the AuNPs have been reviewed. The optical biosensors based on LSPR, SERS, and luminescence are also evaluated. The application of these biosensors for cancer protein detection is discussed. Distinct examples of cancer research that have a substantial impact on both scientific and clinical research are presented.

Surface-enhanced Raman scattering biosensors for detection of oncomiRs in breast cancer

Surface-enhanced Raman scattering (SERS) has emerged as one of the most promising platforms for various biosensing applications. These sensing systems encompass the advantages of specificity, ultra-high sensitivity, stability, low cost, repeatability, and easy-to-use methods. Moreover, their ability to offer a molecular fingerprint and identify the target analyte at low levels make SERS a promising technique for detecting circulating cancer biomarkers with greater sensitivity and reliability. Among the various circulating biomolecules, oncomiRs are emerging as prominent biomarkers for the early screening of breast cancers (BCs). In this review, we provide a comprehensive understanding of different SERS-based biosensors and their application to identify BC-specific oncomiRs. We also discuss different SERS-based sensing strategies, nano-analytical frameworks, and challenges to be addressed for effective clinical translation.

Next-Generation Intelligent MXene-Based Electrochemical Aptasensors for Point-of-Care Cancer Diagnostics

Delayed diagnosis of cancer using conventional diagnostic modalities needs to be addressed to reduce the mortality rate of cancer. Recently, 2D nanomaterial-enabled advanced biosensors have shown potential towards the early diagnosis of cancer. The high surface area, surface functional groups availability, and excellent electrical conductivity of MXene make it the 2D material of choice for the fabrication of advanced electrochemical biosensors for disease diagnostics. MXene-enabled electrochemical aptasensors have shown great promise for the detection of cancer biomarkers with a femtomolar limit of detection. Additionally, the stability, ease of synthesis, good reproducibility, and high specificity offered by MXene-enabled aptasensors hold promise to be the mainstream diagnostic approach. In this review, the design and fabrication of MXene-based electrochemical aptasensors for the detection of cancer biomarkers have been discussed. Besides, various synthetic processes and useful properties of MXenes which can be tuned and optimized easily and efficiently to fabricate sensitive biosensors have been elucidated. Further, futuristic sensing applications along with challenges will be deliberated herein.

Recent development of a magneto-optical nanoplatform for multimodality imaging of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer. Given its inconspicuous and atypical early symptoms and hidden location, most patients have already reached the terminal stage before diagnosis. At present, the diagnosis of PDAC mainly depends on serological and imaging examinations. However, serum tests cannot identify specific tumor locations and each imaging technology has its own defects, bringing great challenges to the early diagnosis of PDAC. Therefore, it is of great significance to find new strategies for the early and accurate diagnosis of PDAC. In recent years, a magneto-optical nanoplatform integrating near infrared fluorescence, photoacoustic, magnetic resonance imaging, etc. has attracted widespread attention, giving full play to the complementary advantages of each imaging modality. Herein, we summarize the recent advances of imaging modalities in the diagnosis of pancreatic cancer, and then discuss in detail the construction and modification of magneto or/and optical probes for multimodal imaging, and advances in early diagnosis using the combination of various imaging modalities, which can provide potential tools for the early diagnosis or even intraoperative navigation and post-treatment follow-up of PDAC patients.

Raman, Infrared and Brillouin Spectroscopies of Biofluids for Medical Diagnostics and for Detection of Biomarkers

This review surveys Infrared, Raman/SERS and Brillouin spectroscopies for medical diagnostics and detection of biomarkers in biofluids, that include urine, blood, saliva and other biofluids. These optical sensing techniques are non-contact, noninvasive and relatively rapid, accurate, label-free and affordable. However, those techniques still have to overcome some challenges to be widely adopted in routine clinical diagnostics. This review summarizes and provides insights on recent advancements in research within the field of vibrational spectroscopy for medical diagnostics and its use in detection of many health conditions such as kidney injury, cancers, cardiovascular and infectious diseases. The six comprehensive tables in the review and four tables in supplementary information summarize a few dozen experimental papers in terms of such analytical parameters as limit of detection, range, diagnostic sensitivity and specificity, and other figures of merits. Critical comparison between SERS and FTIR methods of analysis reveals that on average the reported sensitivity for biomarkers in biofluids for SERS vs FTIR is about 103 to 105 times higher, since LOD SERS are lower than LOD FTIR by about this factor. High sensitivity gives SERS an edge in detection of many biomarkers present in biofluids at low concentration (nM and sub nM), which can be particularly advantageous for example in early diagnostics of cancer or viral infections.HighlightsRaman, Infrared spectroscopies use low volume of biofluidic samples, little sample preparation, fast time of analysis and relatively inexpensive instrumentation.Applications of SERS may be a bit more complicated than applications of FTIR (e.g., limited shelf life for nanoparticles and substrates, etc.), but this can be generously compensated by much higher (by several order of magnitude) sensitivity in comparison to FTIR.High sensitivity makes SERS a noninvasive analytical method of choice for detection, quantification and diagnostics of many health conditions, metabolites, and drugs, particularly in diagnostics of cancer, including diagnostics of its early stages.FTIR, particularly ATR-FTIR can be a method of choice for efficient sensing of many biomarkers, present in urine, blood and other biofluids at sufficiently high concentrations (mM and even a few µM)Brillouin scattering spectroscopy detecting visco-elastic properties of probed liquid medium, may also find application in clinical analysis of some biofluids, such as cerebrospinal fluid and urine.

Advances in the Application of Exosomes Identification Using Surface-Enhanced Raman Spectroscopy for the Early Detection of Cancers

Exosomes are small nanoscale vesicles with a double-layered lipid membrane structure secreted by cells, and almost all types of cells can secrete exosomes. Exosomes carry a variety of biologically active contents such as nucleic acids and proteins, and play an important role not only in intercellular information exchange and signal transduction, but also in various pathophysiological processes in the human body. Surface-enhanced Raman Spectroscopy (SERS) uses light to interact with nanostructured materials such as gold and silver to produce a strong surface plasmon resonance effect, which can significantly enhance the Raman signal of molecules adsorbed on the surface of nanostructures to obtain a rich fingerprint of the sample itself or Raman probe molecules with ultra-sensitivity. The unique advantages of SERS, such as non-invasive and high sensitivity, good selectivity, fast analysis speed, and low water interference, make it a promising technology for life science and clinical testing applications. In this paper, we briefly introduce exosomes and the current main detection methods. We also describe the basic principles of SERS and the progress of the application of unlabeled and labeled SERS in exosome detection. This paper also summarizes the value of SERS-based exosome assays for early tumor diagnosis.

Current and Future Advancements of Raman Spectroscopy Techniques in Cancer Nanomedicine

Raman scattering is one of the most used spectroscopy and imaging techniques in cancer nanomedicine due to its high spatial resolution, high chemical specificity, and multiplexity modalities. The flexibility of Raman techniques has led, in the past few years, to the rapid development of Raman spectroscopy and imaging for nanodiagnostics, nanotherapy, and nanotheranostics. This review focuses on the applications of spontaneous Raman spectroscopy and bioimaging to cancer nanotheranostics and their coupling to a variety of diagnostic/therapy methods to create nanoparticle-free theranostic systems for cancer diagnostics and therapy. Recent implementations of confocal Raman spectroscopy that led to the development of platforms for monitoring the therapeutic effects of anticancer drugs in vitro and in vivo are also reviewed. Another Raman technique that is largely employed in cancer nanomedicine, due to its ability to enhance the Raman signal, is surface-enhanced Raman spectroscopy (SERS). This review also explores the applications of the different types of SERS, such as SERRS and SORS, to cancer diagnosis through SERS nanoprobes and the detection of small-size biomarkers, such as exosomes. SERS cancer immunotherapy and immuno-SERS (iSERS) microscopy are reviewed.

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.

Application of selected biosensor techniques in clinical diagnostics

INTRODUCTION

Examination of disease biomarkers mostly performed on crude materials, such as serum, meets some obstacles, resulting from sample complexity and the wide range of concentrations and sizes of the components. Techniques currently used in clinical diagnostics are usually time-consuming and expensive. The more sensitive and portable devices are needed for early diagnostics. Chemical sensors are devices that convert chemical information into parameters suitable for fast and precise processing and measurement.

AREA COVERED

We review the use of biosensors and their possible application in early diagnostics of some diseases like cancer or viral infections. We focus on different types of biorecognition and some technical modifications, lowering the limit of detection potentially attractive to medical practitioners.

EXPERT OPINION

Among the new diagnostic strategies, the use of biosensors is of increasing interest. In these techniques, the capture ligand interacts with the analyte of interest. Measuring interactions between partners in real time by surface plasmon resonance yields valuable information about kinetics and affinity in a short time and without labels. Importantly, the tendency in such techniques is to make biosensor devices smaller and the test results apparent with the naked eye, so they can be used in point-of-care medicine.

Advances in Optical Aptasensors for Early Detection and Diagnosis of Various Cancer Types

Cancer is a life-threatening concern worldwide. Sensitive and early-stage diagnostics of different cancer types can make it possible for patients to get through the best available treatment options to combat this menace. Among several new detection methods, aptamer-based biosensors (aptasensors) have recently shown promising results in terms of sensitivity, identification, or detection of either cancerous cells or the associated biomarkers. In this mini-review, we have summarized the most recent (2016-2020) developments in different approaches belonging to optical aptasensor technologies being widely employed for their simple operation, sensitivity, and early cancer diagnostics. Finally, we shed some light on limitations, advantages, and current challenges of aptasensors in clinical diagnostics, and we elaborated on some future perspectives.

Combined negative dielectrophoresis with a flexible SERS platform as a novel strategy for rapid detection and identification of bacteria

Surface-enhanced Raman spectroscopy (SERS) is a vibrational method successfully applied in analytical chemistry, molecular biology and medical diagnostics. In this article, we demonstrate the combination of the negative dielectrophoretic (nDEP) phenomenon and a flexible surface-enhanced Raman platform for quick isolation (3 min), concentration and label-free identification of bacteria. The platform ensures a strong enhancement factor, high stability and reproducibility for the SERS response of analyzed samples. By introducing radial dielectrophoretic forces directed at the SERS platform, we can efficiently execute bacterial cell separation, concentration and deposition onto the SERS-active surface, which simultaneously works as a counter electrode and thus enables such hybrid DEP-SERS device vibration-based detection. Additionally, we show the ability of our DEP-SERS system to perform rapid, cultivation-free, direct detection of bacteria in urine and apple juice samples. The device provides new opportunities for the detection of pathogens.

Vibrational Spectroscopy for Identification of Metabolites in Biologic Samples

Vibrational spectroscopy (mid-infrared (IR) and Raman) and its fingerprinting capabilities offer rapid, high-throughput, and non-destructive analysis of a wide range of sample types producing a characteristic chemical "fingerprint" with a unique signature profile. Nuclear magnetic resonance (NMR) spectroscopy and an array of mass spectrometry (MS) techniques provide selectivity and specificity for screening metabolites, but demand costly instrumentation, complex sample pretreatment, are labor-intensive, require well-trained technicians to operate the instrumentation, and are less amenable for implementation in clinics. The potential for vibration spectroscopy techniques to be brought to the bedside gives hope for huge cost savings and potential revolutionary advances in diagnostics in the clinic. We discuss the utilization of current vibrational spectroscopy methodologies on biologic samples as an avenue towards rapid cost saving diagnostics.

MicroRNAs from Liquid Biopsy Derived Extracellular Vesicles: Recent Advances in Detection and Characterization Methods

Liquid biopsies have become a convenient tool in cancer diagnostics, real-time disease monitoring, and evaluation of residual disease. Yet, the information still encrypted in the variety of tumor-derived molecules identified in biofluids has proven difficult to decipher due to the technological limitations imposed by their biological nature. Such is the case of extracellular vesicle (EV) encapsulated ncRNAs, which have gained traction in recent years as biomarkers. Due to their resilience towards degrading factors they may act as suitable disease indicators. This review addresses the less described issues in this context. We present an overview of less investigated biofluids that can be used for EV isolation in addition to different isolation approaches to overcome the technical challenges these specimens harbor. Furthermore, we summarize the latest technological advances providing improvement to ncRNA detection and analysis. Thereby, this review summarizes the current state-of-the-art methodologies regarding EV and EV derived miRNA analysis and how they compare to current approaches.

Surface-Enhanced Raman Spectroscopy for Cancer Immunotherapy Applications: Opportunities, Challenges, and Current Progress in Nanomaterial Strategies

Cancer immunotherapy encompasses a variety of approaches which target or use a patient's immune system components to eliminate cancer. Notably, the current use of immune checkpoint inhibitors to target immune checkpoint receptors such as CTLA-4 or PD-1 has led to remarkable treatment responses in a variety of cancers. To predict cancer patients' immunotherapy responses effectively and efficiently, multiplexed immunoassays have been shown to be advantageous in sensing multiple immunomarkers of the tumor microenvironment simultaneously for patient stratification. Surface-enhanced Raman spectroscopy (SERS) is well-regarded for its capabilities in multiplexed bioassays and has been increasingly demonstrated in cancer immunotherapy applications in recent years. This review focuses on SERS-active nanomaterials in the modern literature which have shown promise for enabling cancer patient-tailored immunotherapies, including multiplexed in vitro and in vivo immunomarker sensing and imaging, as well as immunotherapy drug screening and delivery.