Non-invasive SERS serum detection technology combined with multivariate statistical algorithm for simultaneous screening of cervical cancer and breast cancer

Jia

et al. 2022

J Raman Spectroscopy

Human blood contains a variety of biomolecules that are highly related to the occurrence, progression, and prognosis of diseases, including cancer. The development of rapid and accurate methods for the detection of these biomarkers with extremely low concentration is vital for improving cancer screening. Herein, a label‐free surface‐enhanced Raman scattering (SERS) technology was employed to detect human serum samples from healthy volunteers (n = 51) and breast cancer patients (n = 58) with the aim of exploring the differences in bio‐components between them. Moreover, the influence of measurement location during SERS assay was evaluated due to the occurrence of “coffee‐ring (CR)” effect associated with this work. Results showed that the SERS spectra obtained from the CR area can better reflect the overall biochemical information of serum samples and exhibited excellent diagnostic sensitivity (94.8%) and specificity (98%) for identifying breast cancer from healthy groups using principal component analysis and linear discriminant analysis (PCA‐LDA) algorithm. This work showed a preliminary study of the serum SERS method for breast cancer detection, providing a potential rapid and accurate liquid biopsy strategy for clinical cancer screening.

Section: Resultsmentioning

confidence: 99%

A comparative study based on serum SERS spectra in and on the coffee ring for high precision breast cancer detection

Jia

et al. 2022

J Raman Spectroscopy

“…On the other hand, our univariate analysis identified the presence of the following 6 major peaks relevant for SERS-based discrimination between PCa and controls: 390, 1012, 1210, 1260, 1622, and 1666 cm −1 . They can be assigned as follows: ~390-uric acid [29], ~1012-breathing mode of aromatic amino acids and nucleic acids [18,[30][31][32][33], ~1210-proteins, aromatic amino acids [34][35][36][37][38], ~1260-proteins, amide III [29,32,36,39], ~1666 cm −1 -proteins, amide I α-helix [11,30,35,40,41].…”

Section: Discussionmentioning

confidence: 99%

“…The bands situated between 900 and 1300 cm −1 are mostly generated by carbohydrates and phosphates found in nucleic acids [36]. Carbohydrates are represented by the C-COO − stretching vibration at the band 915 cm −1 [39,53]. Biopsies of prostate cancer tissue and cervical cancer tissue have shown that glycogen concentration is lower in prostate cancer tissue and cervical adenocarcinoma cells [75].…”

Section: Discussionmentioning

confidence: 99%

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

Munteanu

Gulei

et al. 2022

Cancers

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

“…There has also been a growing interest in AgNP-based SERS screening of breast ,− and cervical cancer , because they are the most diagnosed cancers and leading causes of cancer-related death in women with over 2 million cases and 600 000 deaths per year . New possibilities for the discrimination of breast cancer at various stages opened up by analyzing and differentiating the stages of breast cancer using serum samples of cancer patients ( n = 7 for stage-2; n = 5 for stage-3; n = 5 for stage-4) versus healthy individuals ( n = 12).…”

Section: Bloodmentioning

confidence: 99%

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

Constantinou

Hadjigeorgiou

Abalde‐Cela

et al. 2022

ACS Appl. Nano Mater.

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.