Detecting urine metabolites of bladder cancer by surface-enhanced Raman spectroscopy

Label-free detection of kidney stones urine combined with SERS and multivariate statistical algorithm

Kidney stones are a common urological disease with an increasing incidence worldwide. Traditional diagnostic methods for kidney stones are relatively complex and time-consuming, thus necessitating the development of a quicker and simpler diagnostic approach. This study investigates the clinical screening of kidney stones using Surface-Enhanced Raman Scattering (SERS) technology combined with multivariate statistical algorithms, comparing the classification performance of three algorithms (PCA-LDA, PCA-LR, PCA-SVM). Urine samples from 32 kidney stone patients, 30 patients with other urinary stones, and 36 healthy individuals were analyzed. SERS spectra data were collected in the range of 450-1800 cm-1 and analyzed. The results showed that the PCA-SVM algorithm had the highest classification accuracy, with 92.9 % for distinguishing kidney stone patients from healthy individuals and 92 % for distinguishing kidney stone patients from those with other urinary stones. In comparison, the classification accuracy of PCA-LR and PCA-LDA was slightly lower. The findings indicate that SERS combined with PCA-SVM demonstrates excellent performance in the clinical screening of kidney stones and has potential for practical clinical application. Future research can further optimize SERS technology and algorithms to enhance their stability and accuracy, and expand the sample size to verify their applicability across different populations. Overall, this study provides a new method for the rapid diagnosis of kidney stones, which is expected to play an important role in clinical diagnostics.

Biomarker profiling and integrating heterogeneous models for enhanced multi-grade breast cancer prognostication

BACKGROUND

Breast cancer remains a leading cause of female mortality worldwide, exacerbated by limited awareness, inadequate screening resources, and treatment options. Accurate and early diagnosis is crucial for improving survival rates and effective treatment.

OBJECTIVES

This study aims to develop an innovative artificial intelligence (AI) based model for predicting breast cancer and its various histopathological grades by integrating multiple biomarkers and subject age, thereby enhancing diagnostic accuracy and prognostication.

METHODS

A novel ensemble-based machine learning (ML) framework has been introduced that integrates three distinct biomarkers-beta-human chorionic gonadotropin (β-hCG), Programmed Cell Death Ligand 1 (PD-L1), and alpha-fetoprotein (AFP)-alongside subject age. Hyperparameter optimization was performed using the Particle Swarm Optimization (PSO) algorithm, and minority oversampling techniques were employed to mitigate overfitting. The model's performance was validated through rigorous five-fold cross-validation.

RESULTS

The proposed model demonstrated superior performance, achieving a 97.93% accuracy and a 98.06% F1-score on meticulously labeled test data across diverse age groups. Comparative analysis showed that the model outperforms state-of-the-art approaches, highlighting its robustness and generalizability.

CONCLUSION

By providing a comprehensive analysis of multiple biomarkers and effectively predicting tumor grades, this study offers a significant advancement in breast cancer screening, particularly in regions with limited medical resources. The proposed framework has the potential to reduce breast cancer mortality rates and improve early intervention and personalized treatment strategies.

scEGG: an exogenous gene-guided clustering method for single-cell transcriptomic data

In recent years, there has been significant advancement in the field of single-cell data analysis, particularly in the development of clustering methods. Despite these advancements, most algorithms continue to focus primarily on analyzing the provided single-cell matrix data. However, within medical contexts, single-cell data often encompasses a wealth of exogenous information, such as gene networks. Overlooking this aspect could result in information loss and produce clustering outcomes lacking significant clinical relevance. To address this limitation, we introduce an innovative deep clustering method for single-cell data that leverages exogenous gene information to generate discriminative cell representations. Specifically, an attention-enhanced graph autoencoder has been developed to efficiently capture topological signal patterns among cells. Concurrently, a random walk on an exogenous protein-protein interaction network enabled the acquisition of the gene's embeddings. Ultimately, the clustering process entailed integrating and reconstructing gene-cell cooperative embeddings, which yielded a discriminative representation. Extensive experiments have demonstrated the effectiveness of the proposed method. This research provides enhanced insights into the characteristics of cells, thus laying the foundation for the early diagnosis and treatment of diseases. The datasets and code can be publicly accessed in the repository at https://github.com/DayuHuu/scEGG.

Recover then aggregate: unified cross-modal deep clustering with global structural information for single-cell data

Single-cell cross-modal joint clustering has been extensively utilized to investigate the tumor microenvironment. Although numerous approaches have been suggested, accurate clustering remains the main challenge. First, the gene expression matrix frequently contains numerous missing values due to measurement limitations. The majority of existing clustering methods treat it as a typical multi-modal dataset without further processing. Few methods conduct recovery before clustering and do not sufficiently engage with the underlying research, leading to suboptimal outcomes. Additionally, the existing cross-modal information fusion strategy does not ensure consistency of representations across different modes, potentially leading to the integration of conflicting information, which could degrade performance. To address these challenges, we propose the 'Recover then Aggregate' strategy and introduce the Unified Cross-Modal Deep Clustering model. Specifically, we have developed a data augmentation technique based on neighborhood similarity, iteratively imposing rank constraints on the Laplacian matrix, thus updating the similarity matrix and recovering dropout events. Concurrently, we integrate cross-modal features and employ contrastive learning to align modality-specific representations with consistent ones, enhancing the effective integration of diverse modal information. Comprehensive experiments on five real-world multi-modal datasets have demonstrated this method's superior effectiveness in single-cell clustering tasks.

Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction

Nasopharyngeal cancer (NPC) is a malignant tumor with high prevalence in Southeast Asia and highly invasive and metastatic characteristics. Radiotherapy is the primary strategy for NPC treatment, however there is still lack of effect method for predicting the radioresistance that is the main reason for treatment failure. Herein, the molecular profiles of patient plasma from NPC with radiotherapy sensitivity and resistance groups as well as healthy group, respectively, were explored by label-free surface enhanced Raman spectroscopy (SERS) based on surface plasmon resonance for the first time. Especially, the components with different molecular weight sizes were analyzed via the separation process, helping to avoid the possible missing of diagnostic information due to the competitive adsorption. Following that, robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA) was employed to extract the feature of blood-SERS data and establish an effective predictive model with the accuracy of 96.7% for identifying the radiotherapy resistance subjects from sensitivity ones, and 100% for identifying the NPC subjects from healthy ones. This work demonstrates the potential of molecular separation-assisted label-free SERS combined with machine learning for NPC screening and treatment strategy guidance in clinical scenario.

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.

An integrated computational pipeline for machine learning-driven diagnosis based on Raman spectra of saliva samples

Raman Spectroscopy promises the ability to encode in spectral data the significant differences between biological samples belonging to patients affected by a disease and samples of healthy patients (controls). However, the decoding and interpretation of the Raman spectral fingerprint is still a difficult and time-consuming procedure even for domain experts. In this work, we test an end-to-end deep-learning diagnostic pipeline able to classify spectral data from saliva samples. The pipeline has been validated against the SARS-COV-2 Infection and for the screening of neurodegenerative diseases such as Parkinson's and Alzheimer's diseases. The proposed system can be used for the fast prototyping of promising non-invasive, cost and time-efficient diagnostic screening tests.

Label-free screening of common urinary system tumors from blood plasma based on surface-enhanced Raman spectroscopy

BACKGROUND

The incidence of common urinary system tumors has been rising rapidly in recent years, and most urinary system-derived tumors lack specific biomarkers.

OBJECTIVES

To explore the efficacy of surface-enhanced Raman spectroscopy (SERS) of blood plasma in screening three common urinary system tumors, including bladder cancer (BC), prostate cancer (PCa), and renal cell carcinoma (RCC).

METHODS

SERS plasma spectra from 125 plasma samples, including 25 PCa, 38 RCC, 24 BC patients, and 38 normal volunteers, were collected. All candidates had no other comorbidities. The Diagnosis was based on the combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and the effectiveness of the diagnostic algorithms was verified using the Receiver Operating Characteristic Curve (ROC).

RESULTS

There are significant differences in SERS signals between PCa, BC, RCC, and normal plasma, especially at 639, 889, 1010, 1136, and 1205 cm-1. The PCA-LDA results show that high sensitivity (100 %), specificity (100 %), and accuracy (100 %) could be achieved for screening the PCa, RCC, BC group vs. the normal group, the PCa group vs. the BC and RCC group, respectively. The diagnostic sensitivity, specificity, and accuracy for the BC group vs. the RCC group are 79.2 %, 71.1 %, and 75.15 %, respectively. The integrated area under the ROC curve (AUC) is 1.0, 1.0, and 1.0 for the PCa, RCC, and BC group vs. the normal group, respectively. The AUC of the PCa group vs. the BC group and RCC group and the BC group vs. the RCC group are 1.0, 1.0, and 0.842, respectively.

CONCLUSIONS

Label-free plasma-SERS technology with PCA-LDA analysis could be a useful screening method for detecting urinary system tumors (PCa, RCC, and BC) in this exploratory study.

Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges

Early clinical diagnosis, effective intraoperative guidance, and an accurate prognosis can lead to timely and effective medical treatment. The current conventional clinical methods have several limitations. Therefore, there is a need to develop faster and more reliable clinical detection, treatment, and monitoring methods to enhance their clinical applications. Raman spectroscopy is noninvasive and provides highly specific information about the molecular structure and biochemical composition of analytes in a rapid and accurate manner. It has a wide range of applications in biomedicine, materials, and clinical settings. This review primarily focuses on the application of Raman spectroscopy in clinical medicine. The advantages and limitations of Raman spectroscopy over traditional clinical methods are discussed. In addition, the advantages of combining Raman spectroscopy with machine learning, nanoparticles, and probes are demonstrated, thereby extending its applicability to different clinical phases. Examples of the clinical applications of Raman spectroscopy over the last 3 years are also integrated. Finally, various prospective approaches based on Raman spectroscopy in clinical studies are surveyed, and current challenges are discussed.

Metabolomic profiling of human bladder tissue extracts

INTRODUCTION

Bladder cancer is a common malignancy affecting the urinary tract and effective biomarkers and for which monitoring therapeutic interventions have yet to be identified.

OBJECTIVES

Major aim of this work was to perform metabolomic profiling of human bladder cancer and adjacent normal tissue and to evaluate cancer biomarkers.

METHODS

This study utilized nuclear magnetic resonance (NMR) and high-resolution nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS) methods to investigate polar metabolite profiles in tissue samples from 99 bladder cancer patients.

RESULTS

Through NMR spectroscopy, six tissue metabolites were identified and quantified as potential indicators of bladder cancer, while LDI-MS allowed detection of 34 compounds which distinguished cancer tissue samples from adjacent normal tissue. Thirteen characteristic tissue metabolites were also found to differentiate bladder cancer tumor grades and thirteen metabolites were correlated with tumor stages. Receiver-operating characteristics analysis showed high predictive power for all three types of metabolomics data, with area under the curve (AUC) values greater than 0.853.

CONCLUSION

To date, this is the first study in which bladder human normal tissues adjacent to cancerous tissues are analyzed using both NMR and MS method. These findings suggest that the metabolite markers identified in this study may be useful for the detection and monitoring of bladder cancer stages and grades.

Using urine FTIR spectra to screen autism spectrum disorder

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder caused by multiple factors, lacking clear biomarkers. Diagnosing ASD still relies on behavioural and developmental signs and usually requires lengthy observation periods, all of which are demanding for both clinicians and parents. Although many studies have revealed valuable knowledge in this field, no clearly defined, practical, and widely acceptable diagnostic tool exists. In this study, 26 children with ASD (ASD+), aged 3-5 years, and 26 sex and age-matched controls are studied to investigate the diagnostic potential of the Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy. The urine FTIR spectrum results show a downward trend in the 3000-2600/cm region for ASD+ children when compared to the typically developing (TD) children of the same age. The average area of this region is 25% less in ASD+ level 3 children, 29% less in ASD+ level 2 children, and 16% less in ASD+ level 1 children compared to that of the TD children. Principal component analysis was applied to the two groups using the entire spectrum window and five peaks were identified for further analysis. The correlation between the peaks and natural urine components is validated by artificial urine solutions. Less-than-normal levels of uric acid, phosphate groups, and ammonium ([Formula: see text]) can be listed as probable causes. This study shows that ATR-FTIR can serve as a practical and non-invasive method to screen ASD using the high-frequency region of the urine spectrum.

Application of nanotechnology in bladder cancer diagnosis and therapeutic drug delivery

Bladder cancer (BC) is one of the most common malignant tumors in the urinary system, and its high recurrence rate is a great economic burden to patients. Traditional diagnosis and treatment methods have the disadvantages of insufficient targeting, obvious side effects and low sensitivity, which seriously limit the accurate diagnosis and efficient treatment of BC. Due to their small size, easy surface modification, optical properties such as plasmon resonance, and surface enhanced Raman scattering, good electrical conductivity and photothermal conversion properties, nanomaterials have great potential application value in the realization of specific diagnosis and targeted therapy of BC. At present, the application of nanomaterials in the diagnosis and treatment of BC is attracting great attention and achieving rich research results. Therefore, this paper summarizes the recent research on nanomaterials in the diagnosis and treatment of BC, clarifies the existing advantages and disadvantages, and provides theoretical guidance for promoting the accurate diagnosis and efficient treatment of BC.

Congenital malformations of the urinary system as visceral markers of undifferentiated connective tissue dysplasia

OBJECTIVE

The relevance of this study is conditioned by the need for urgent search and implementation of effective methods of treatment of urinary system diseases in people of different ages, as well as addressing issues of quality treatment of connective tissue diseases in general and its dysplasia in particular. The aim of the article is to identify congenital defects as visceral markers of connective tissue dysplasia.

METHODS

The methodology of this study includes a survey of a group of children with considerable problems in the development and functioning of the urinary system at the age of 2 weeks to 3 years, in order to qualitatively select and determine the most effective methods of treatment. Children who took part in this study had a set of phenotypic and clinical properties of undifferentiated connective tissue dysplasia.

RESULTS

The considerable prevalence of undifferentiated connective tissue dysplasia in young children with congenital malformations of the urinary system, especially in children with abnormal development and functioning of kidney tissue, which substantially influences the course of the disease was determined. Also, treatment of undifferentiated connective tissue dysplasia was predicted.

CONCLUSIONS

It was concluded that the presence of a malformation of the urinary system, which is acquired by a child from birth, can be considered as a visceral manifestation of undifferentiated connective tissue dysplasia.

Combining fiber optical tweezers and Raman spectroscopy for rapid identification of melanoma

Cutaneous melanoma is a skin tumor with a high degree of malignancy and fatality rate, the incidence of which has increased in recent years. Therefore, a rapid and sensitive diagnostic technique of melanoma cells is urgently needed. In this paper, we present a new approach using fiber optical tweezers to manipulate melanoma cells to measure their Raman spectra. Then, combined with Principal Component Analysis and Support Vector Machines (PCA-SVM) classification model, to achieve the classification of common mutant, wild-type and drug-resistant melanoma cells. A total of 150 Raman spectra of 30 cells were collected from mutant, wild-type and drug-resistant melanoma cell lines, and the classification accuracy was 92%, 94%, 97.5%, respectively. These results suggest that the study of tumor cells based on fiber optical tweezers and Raman spectroscopy is a promising method for early and rapid identification and diagnosis of tumor cells.

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

In this study, we mainly aimed to investigate the diagnostic potential of surface-enhanced Raman spectroscopy for bladder cancer and kidney cancer which are the most common cancers of the urinary system, and evaluate the classification ability of three statistical algorithms: principal component analysis-linear discriminate analysis (PCA-LDA), partial least square-random forest (PLS-RF), and partial least square-support vector machine (PLS-SVM). The plasma of 26 bladder cancer patients, 38 kidney cancer patients and 39 normal subjects was mixed with the same volume of silver nanoparticles, respectively, and then high-quality SERS signal was obtained. The SERS spectra in the range of 400-1800 cm^-1 were compared and analyzed. There were some significant differences in SERS peak intensity, which may reflect the changes in the content of some biomacromolecules in the plasma of cancer patients. Based on the three algorithms of PCA-LDA, PLS-RF and PLS-SVM, the classification accuracy of SERS spectra of plasma from cancer patients and normal subjects was 98.1%, 100% and 100%, respectively. In addition, the classification accuracy of the three diagnostic algorithms to classify the SERS spectra of bladder cancer and kidney cancer was 81.3%, 91.7%, and 98.4%, respectively. This exploratory work demonstrates that SERS combined with PLS-SVM algorithm has superior performance for clinical screening of bladder cancer and kidney cancer through peripheral plasma.

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.

[Application of Raman-based technologies in the detection of urological tumors]

Urinary system tumors affect a huge number of individuals, and are frequently recurrent and progressing following surgery, necessitating lifelong surveillance. As a result, early and precise diagnosis of urinary system cancers is important for prevention and therapy. Histopathology is now the golden stan-dard for the diagnosis, but it is invasive, time-consuming, and inconvenient for initial diagnosis and re-gular follow-up assessment. Endoscopy can directly witness the tumor's structure, but intrusive detection is likely to cause harm to the patient's organs, and it is apt to create other hazards in frequently examined patients. Imaging is a valuable non-invasive and quick assessment tool; however, it can be difficult to define the type of lesions and has limited sensitivity for early tumor detection. The conventional approaches for detecting tumors have their own set of limitations. Thus, detection methods that combine non-invasive detection, label-free detection, high sensitivity and high specificity are urgently needed to aid clinical diagnosis. Optical diagnostics and imaging are increasingly being employed in healthcare settings in a variety of sectors. Raman scattering can assess changes in molecular signatures in cancer cells or tissues based on the interaction with vibrational modes of common molecular bonds. Due to the advantages of label-free, strong chemical selectivity, and high sensitivity, Raman scattering, especially coherent Raman scattering microscopy imaging with high spatial resolution, has been widely used in biomedical research. And quantity studies have shown that it has a good application in the detection and diagnosis of bladder can-cer, renal clear cell carcinoma, prostate cancer, and other cancers. In this paper, several nonlinear imaging techniques based on Raman scattering technology are briefly described, including Raman spectroscopy, coherent anti-Stokes Raman scattering, stimulated Raman scattering, and surface-enhanced Raman spectroscopy. And we will discuss the application of these techniques for detecting urologic malignancy. Future research directions are predicted using the advantages and limitations of the aforesaid methodologies in the research. For clinical practice, Raman scattering technology is intended to enable more accurate, rapid, and non-invasive in early diagnosis, intraoperative margins, and pathological grading basis for clinical practice.

Surface-Enhanced Raman Spectroscopy of Pretreated Plasma Samples Predicts Disease Recurrence in Muscle-Invasive Bladder Cancer Patients Undergoing Neoadjuvant Chemotherapy and Radical Cystectomy

Objective

To explore the value of surface-enhanced Raman spectroscopy analysis of pretreated plasma samples in prediction of bladder cancer (BCa) recurrence after neoadjuvant chemotherapy (NAC) and radical cystectomy (RC).

Patients and Methods

SERS was used to analyze plasma samples collected before biopsy and treatment in BCa patients undergoing NAC and RC. The value of clinicopathological parameters and distinctive SERS peaks in the prediction of disease recurrence were analyzed in Cox regression proportional hazard analysis and Log rank test. Principal component analysis and linear discriminant analysis (PCA-LDA) were employed to process spectral data and construct diagnostic algorithms.

Results

A total of 88 patients with 440 plasma SERS spectra were collected. The SRES spectra from recurrent patients were compared with patients who remained recurrence free. The SERS demonstrated higher levels of circulating free nucleic acid components in recurrent population, which is represented by significantly higher intensities at SERS peaks of 725 cm⁻¹, 1328 cm⁻¹ and 1455 cm⁻¹. The SERS also detected significantly lower levels of tryptophan shown as lower significantly intensities at the 1558 cm⁻¹, which is proved to be an independent predictor of BCa recurrence. The addition of SERS peaks of 1558 cm⁻¹ to classic clinicopathological predictors including pathological tumor stage, lymph node metastasis and pathological downstaging can significantly enhance the power of the predictive model from 0.66 to 0.76 in the area under curve (AUC) of receiver operating characteristic (ROC) curves. Meanwhile, the PCA-LDA diagnostic model based on SERS spectra reveals a high accuracy of 85.2% in prediction of disease recurrence and the AUC of 0.92 in the ROC curve. When validated in the leave-one-out cross-validation method, the accuracy of the model remained 84.1%.

Conclusion

We show that SERS analysis of plasma before NAC treatment can accurately classify patients with different risks of disease recurrence after surgery and improve the power of clinicopathological predictive models, thus refining clinical decision-making.

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

BACKGROUND

Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine.

METHODS

Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types.

RESULTS

Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 ± 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 ± 0.03) or SERS data (AUC = 0.84 ± 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 ± 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 ± 0.04) or SERS (AUC = 0.92 ± 0.05) individually, although SERS alone performed better in terms of classification accuracy.

CONCLUSION

miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged.

Toward a SERS Diagnostic Tool for Discrimination between Cancerous and Normal Bladder Tissues via Analysis of the Extracellular Fluid

Vibrational spectroscopy provides the possibility for sensitive and precise detection of chemical changes in biomolecules due to development of cancers. In this work, label-free near-infrared surface enhanced Raman spectroscopy (SERS) was applied for the differentiation between cancerous and normal human bladder tissues via analysis of the extracellular fluid of the tissue. Specific cancer-related SERS marker bands were identified by using a 1064 nm excitation wavelength. The prominent spectral marker band was found to be located near 1052 cm-1 and was assigned to the C-C, C-O, and C-N stretching vibrations of lactic acid and/or cysteine molecules. The correct identification of 80% of samples is achieved with even limited data set and could be further improved. The further development of such a detection method could be implemented in clinical practice for the aid of surgeons in determining of boundaries of malignant tumors during the surgery.

Emergence of Responsive Surface-Enhanced Raman Scattering Probes for Imaging Tumor-Associated Metabolites

As a core hallmark of cancer, metabolic reprogramming alters the metabolic networks of cancer cells to meet their insatiable appetite for energy and nutrient. Tumor-associated metabolites, the products of metabolic reprogramming, are valuable in evaluating tumor occurrence and progress timely and accurately because their concentration variations usually happen earlier than the aberrances demonstrated in tissue structure and function. As an optical spectroscopic technique, surface-enhanced Raman scattering (SERS) offers advantages in imaging tumor-associated metabolites, including ultrahigh sensitivity, high specificity, multiplexing capacity, and uncompromised signal intensity. This review first highlights recent advances in the development of stimuli-responsive SERS probes. Then the mechanisms leading to the responsive SERS signal triggered by tumor metabolites are summarized. Furthermore, biomedical applications of these responsive SERS probes, such as the image-guided tumor surgery and liquid biopsy examination for tumor molecular typing, are summarized. Finally, the challenges and prospects of the responsive SERS probes for clinical translation are also discussed.

Diagnosis and staging of diffuse large B-cell lymphoma using label-free surface-enhanced Raman spectroscopy

Non-invasive diagnosis and staging of diffuse large B-cell lymphoma (DLBCL) were achieved using label-free surface-enhanced Raman spectroscopy (SERS). SERS spectra were measured for serum samples of DLBCL patients at different progressive stages and healthy controls (HCs), using colloidal silver nano-particles (AgNPs) as the substrate. Differences in the spectral intensities of Raman peaks were observed between the DLBCL and HC groups, and a close correlation between the spectral intensities of Raman peaks with the progressive stages of the cancer was obtained, demonstrating the possibility of diagnosis and staging of the disease using the serum SERS spectra. Multivariate analysis methods, including principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM) classifier, and k-nearest neighbors (kNN) classifier, were used to build the diagnosis and staging models for DLBCL. Leave-one-out cross-validation was used to evaluate the performances of the models. The kNN model achieved the best performances for both diagnosis and staging of DLBCL: for the diagnosis analysis, the accuracy, sensitivity, and specificity were 87.3%, 0.921, and 0.809, respectively; for the staging analysis between the early (Stage I & II) and the late (Stage III & IV) stages, the accuracy was 90.6%, and the sensitivity values for the early and the late stages were 0.947 and 0.800, respectively. The label-free serum SERS in combination with multivariate analysis could serve as a potential technique for non-invasive diagnosis and staging of DLBCL.

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.

Developing non-invasive bladder cancer screening methodology through potentiometric multisensor urine analysis

We report on the feasibility study exploring the potential of a simple electrochemical multisensor system as a tool for distinguishing between urine samples from patients with confirmed bladder cancer (36 samples) and healthy volunteers (51 samples). The potentiometric sensor responses obtained in urine samples were employed as the input data for various machine learning classification algorithms (logistic regression, random forest, extreme gradient boosting classifier, support vector machine, and voting classifier). The performance metrics of the classifiers were evaluated via Monte-Carlo cross-validation. The best model combining all the acquired data from the people aged 19-88 with different tumor grades and malignancies, including patients with recurrent bladder cancer, yielded 72% accuracy, 71% sensitivity, and 58% specificity. It was found that these metrics can be improved to 76% accuracy, 80% sensitivity, and 75% specificity when only a limited age group (50-88 years of age) is considered. Taking into account the simplicity of the proposed screening method, this technique appears to be a promising tool for further research.