Brain tumour homogenates analysed by surface-enhanced Raman spectroscopy: Discrimination among healthy and cancer cells

Raman Spectroscopy: A Tool for Molecular Fingerprinting of Brain Cancer

Brain cancer is one of those few cancers with very high mortality and low five-year survival rate. First and foremost reason for the woes is the difficulty in diagnosing and monitoring the progression of brain tumors both benign and malignant, noninvasively and in real time. This raises a need in this hour for a tool to diagnose the tumors in the earliest possible time frame. On the other hand, Raman spectroscopy which is well-known for its ability to precisely represent the molecular markers available in any sample given, including biological ones, with great sensitivity and specificity. This has led to a number of studies where Raman spectroscopy has been used in brain tumors in various ways. This review article highlights the fundamentals of Raman spectroscopy and its types including conventional Raman, SERS, SORS, SRS, CARS, etc. are used in brain tumors for diagnostics, monitoring, and even theragnostics, collating all the major works in the area. Also, the review explores how Raman spectroscopy can be even more effectively used in theragnostics and the clinical level which would make them a one-stop solution for all brain cancer needs in the future.

The emerging applications and advancements of Raman spectroscopy in pediatric cancers

Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.

Discovering Glioma Tissue through Its Biomarkers' Detection in Blood by Raman Spectroscopy and Machine Learning

The most commonly occurring malignant brain tumors are gliomas, and among them is glioblastoma multiforme. The main idea of the paper is to estimate dependency between glioma tissue and blood serum biomarkers using Raman spectroscopy. We used the most common model of human glioma when continuous cell lines, such as U87, derived from primary human tumor cells, are transplanted intracranially into the mouse brain. We studied the separability of the experimental and control groups by machine learning methods and discovered the most informative Raman spectral bands. During the glioblastoma development, an increase in the contribution of lactate, tryptophan, fatty acids, and lipids in dried blood serum Raman spectra were observed. This overlaps with analogous results of glioma tissues from direct Raman spectroscopy studies. A non-linear relationship between specific Raman spectral lines and tumor size was discovered. Therefore, the analysis of blood serum can track the change in the state of brain tissues during the glioma development.

Research on the difference between patients with coronary heart disease and healthy controls by surface enhanced Raman spectroscopy

Coronary heart disease (CHD) is one of the primary causes of death globally. There are several diagnostic techniques for CHD at present, but they are invasive and with limited accuracy. In the work, measurement of human urine based on surface-enhanced Raman spectroscopy (SERS) was proposed to diagnose CHD. Urine samples of 157 CHD patients and 63 healthy controls (HC) were investigated by SERS. Statistical analysis of the measured data was then performed. It was found that there were intensity differences in nine Raman peaks (1223/1243/1272/1463/1481/1516/1536/1541/1550 cm^-1) between CHD and HC in their average SERS spectrum. Furthermore, principal component analysis (PCA)-linear discriminant analysis (LDA) was then utilized to establish a prediction model to classify CHD and HC. It revealed that the accuracy, specificity and sensitivity of the prediction model validated by leave-one-patient-out cross validation (LOPOCV) were 84.09%, 92.06% and 80.89%, respectively. Therefore, the proposed method can be employed as a non-invasive, rapid and accurate tool for CHD diagnosis in clinical application.

Lung Cancer: Spectral and Numerical Differentiation among Benign and Malignant Pleural Effusions Based on the Surface-Enhanced Raman Spectroscopy

We present here that the surface-enhanced Raman spectroscopy (SERS) technique in conjunction with the partial least squares analysis is as a potential tool for the differentiation of pleural effusion in the course of the cancerous disease and a tool for faster diagnosis of lung cancer. Pleural effusion occurs mainly in cancer patients due to the spread of the tumor, usually caused by lung cancer. Furthermore, it can also be initiated by non-neoplastic diseases, such as chronic inflammatory infection (the most common reason for histopathological examination of the exudate). The correlation between pleural effusion induced by tumor and non-cancerous diseases were found using surface-enhanced Raman spectroscopy combined with principal component regression (PCR) and partial least squares (PLS) multivariate analysis method. The PCR predicts 96% variance for the division of neoplastic and non-neoplastic samples in 13 principal components while PLS 95% in only 10 factors. Similarly, when analyzing the SERS data to differentiate the type of tumor (squamous cell vs. adenocarcinoma), PLS gives more satisfactory results. This is evidenced by the calculated values of the root mean square errors of calibration and prediction but also the coefficients of calibration determination and prediction (R2C = 0.9570 and R2C = 0.7968), which are more robust and rugged compared to those calculated for PCR. In addition, the relationship between cancerous and non-cancerous samples in the dependence on the gender of the studied patients is presented.

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.

SERS-based sensor for direct L-selectin level determination in plasma samples as alternative method of tumor detection

Selectin ligands are present on the surface of tumor cells, for this reason lowering the L-selectin level in the blood and lymph can indicate presence of the tumor. Therefore the selectin level in the plasma are potential targets for anticancer therapy. We demonstrate the surface enhanced Raman spectroscopy (SERS)-based sensor for the determination of L-selectin level in biological samples that can be used in medical diagnosis. The combination of SERS with the method of multivariate analysis as principle component analysis (PCA) allows to strengthen the presented data analysis. The loadings of PCA permit to indicate those vibration modes, that are the most important for the assumed identification (bands at 1574, 1450, 1292 cm^-1 ). Two bands at 1286 and 1580 cm^-1 were selected for the determination of the calibration curve (bands intensities I₁₂₈₆ /I₁₅₈₀ ratio). The L-selectin level of biological samples can be read, directly from the calibration curve. The presented sensor is as a sensitive tool with good specificity and selectivity of L-selectin, even in the case of coexistence of P- and E-selectin.

Recent Progress in Optical Sensors for Biomedical Diagnostics

In recent years, several types of optical sensors have been probed for their aptitude in healthcare biosensing, making their applications in biomedical diagnostics a rapidly evolving subject. Optical sensors show versatility amongst different receptor types and even permit the integration of different detection mechanisms. Such conjugated sensing platforms facilitate the exploitation of their neoteric synergistic characteristics for sensor fabrication. This paper covers nearly 250 research articles since 2016 representing the emerging interest in rapid, reproducible and ultrasensitive assays in clinical analysis. Therefore, we present an elaborate review of biomedical diagnostics with the help of optical sensors working on varied principles such as surface plasmon resonance, localised surface plasmon resonance, evanescent wave fluorescence, bioluminescence and several others. These sensors are capable of investigating toxins, proteins, pathogens, disease biomarkers and whole cells in varied sensing media ranging from water to buffer to more complex environments such as serum, blood or urine. Hence, the recent trends discussed in this review hold enormous potential for the widespread use of optical sensors in early-stage disease prediction and point-of-care testing devices.