Surface-Enhanced Raman Spectroscopy Clinical Applications: History
Please note this is an old version of this entry, which may differ significantly from the current revision.

Surface-enhanced Raman spectroscopy (SERS) has become a powerful analytical technique, widely used for the detection of various analytes at low concentrations. In comparison to many other analytical methods, SERS is a highly sensitive, fast, humidity-independent analytical method with a high potential for multiplexed detection. SERS advantages over fluorescence, for instance, include good robustness/low photobleaching and capabilities for label-free detection, while SERS can be applied for in situ monitoring, in vivo biosensing, and even single molecule detection. Due to these advantages, SERS is widely used in molecular biology, biomedicine, and environmental science. Furthermore, SERS is capable of detecting single molecules Factors such as pH, the degree of nanoparticle (NP) aggregation, temperature and substrate composition can have a significant impact on the reproducibility and enhancement of SERS applications.

  • SERS
  • lung cancer
  • SARS-CoV-2
  • accuracy of analysis
  • nanoparticles
  • surface enhanced spectroscopy
  • silicon
  • gold nanoparticles
  • Raman spectroscopy
  • biosensing

1. Introduction

With the enhancement of the Raman signal up to 10 million times and the limit of detection as good as  25 pM, Silicon can be regarded as a substrate with promising potential in clinical applications. However, despite there is a  multitude  of the analytical surface-enhanced Raman spectroscopy (SERS) measurement papers using Si-based substrates that provided promising SERS results, the number of clinical papers was limited. 35 studies were selected for analysis of the clinical application of the SERS substrates, among which 14 are silicon-based, 7 are aluminum-based, 7 are bare silver, and 7 gold applying papers, respectively. Table 1 compares the average reported clinal performance of different substrate groups. The comparison reveals that the combined Au/Ag@silicon-based substrates provide the most reliable SERS results, with a sensitivity/specificity/accuracy of 96%/95%/94.4%. Comparable accuracy of 94.2% was achieved by using aluminum based substrates. The Ag@Al substrates demonstrated a notably better performance than the whole Al group, with the sensitivity of 93%, specificity of 100%, and overall accuracy of about 96%. The conventional substrates (Au or  Ag only) have slightly lower accuracy of 93% and 94%. The information about the specificity, sensitivity, or accuracy of each substrate type is limited to just several reports so those comparisons have limited reliability.
One of the advantages of conventional substrates such as AuNP and AgNP is they can be applied to differentiate between the various disorders and still maintain their high reliability. As an example, nasopharyngeal and liver cancer, and healthy species (overall 75 samples) were distinguished with an accuracy of 90.7% in the study conducted by Yun et al. employing AgNP as a SERS substrate [1]. Fang Yaping achieved 81.2% accuracy in differentiating the 7 different types of cancer cells in 350 samples by using gold nanoparticles for the SERS measurements [2]. This means that Au and Ag substrates are accurate not only in simply detecting the analyte, but also demonstrate fairly accurate results in the multi-variate clinical analysis.

2. Prostate Cancer Clinical Diagnosis by Surface-Enhanced Raman Spectroscopy

Prostate cancer (PCa) is the second most frequent cancer and one of the leading causes of cancer-related deaths among men that can be diagnosed by an elevated level of the prostate-specific antigen (PSA) [36]. However, sometimes the increased amount of PSA can correlate with non-cancerous benign prostate hyperplasia (BPH), which is the enlargement of the prostate gland that is less severe for the health than PCa [37]. Different SERS substrates coupled with statistical analysis methods such as Principal component analysis (PCA), Partial least squares-discriminant analysis (PLS-Da), and Support-vector machine (SVM) can be used to accurately diagnose PCa. PCa and BPH classification by SERS study of PSA in serum on AgNP substrates was carried out by Chen Na et al., as a result, prostate cancer was detected with 94.2% accuracy in 120 samples [31]. In the comparable multi-variate immunoassay applying SiC@Ag(film)@AgNPs as a substrate to differentiate PCa from BPH and healthy samples, prostate cancer was determined with 70% accuracy, while benign prostate hyperplasia with 60%, and healthy samples with 75%, respectively [4]. However, due to the small sample size of 10 prostate cancer positive, 10 benign prostate hyperplasia (BPH), and 12 healthy samples, the following results have limited reliability [4]. Furthermore, aluminum foil coated with the silver colloid also demonstrated high reliability in classifying prostate cancer and BPH by providing 98% accuracy for 28 plasma samples [24]. In addition, aluminum-based SERS substrate presented a high accuracy of 98% in diagnosing prostate cancer among 93 positive and 68 negative serum samples [19].

3. Lung Cancer Diagnosis by Surface-Enhanced Raman Spectroscopy

Lung cancer is considered to be the cause of the most cancer-related death both among males and females worldwide resulting in the death of more than 1.8 million people in 2020 [36], this shows the significance of the early and accurate diagnosis of lung cancer. Qian Kun et al. presented a 100% accurate diagnosis of lung cancer by the saliva test [28]. In this assay, SERS measurements of 61 positive and 66 negative samples were taken on gold substrates and later analyzed using the SVM [28]. According to Zhang et al., pure AgNP substrates in the SERS analysis of the serum samples resulted in the issue with the repeatability and the stability of the signals, due to the maldistribution of the substrate [9]. Nevertheless, the problem was tackled by coating the mixture of AgNP and serum on the pyramidical Si surface, in this research work, lung cancer was diagnosed with the sensitivity/specificity/accuracy of 100%/90%/95% in 50 patients and 50 healthy serum samples from the PCA-LDA method [9].

4. SARS-CoV-2 Detection by Surface-Enhanced Raman Spectroscopy

SARS-CoV-2 virus is the cause of the Coronavirus infection (COVID-19) that up to date resulted in more than 6 million deaths and about 627 million confirmed cases according to WHO ( accessed on 1 November 2022). Since the start of the pandemic, numerous studies based on different techniques including SERS have been conducted on the detection of SARS-CoV-2. Clustered regularly interspaced short palindromic repeats (CRISPR) coupled SERS method by Liang et al. introduced the amplification-free detection of SARS-CoV-2 RNA in 30–40 min of incubation by using silver nanoparticle substrates [34]. RNA extracts obtained from 24 infected and 88 healthy nasopharyngeal samples were classified with 87.5% sensitivity and 100% specificity, resulting in 97% accuracy of the following method [34]. The silicon-based substrate delivered better clinical results in the diagnosis of SARS-CoV-2 than AgNP. SERS lateral flow immunoassay (LFIA) biosensor employing Ag shell on SiO2 core substrate demonstrated 100% accuracy on 19 positive and 49 negative serum samples [8]. The SERS tags were modified with SARS-CoV-2 S protein and three test lines designated for human IgG, IgM and control were present. When viral immunoglobulin is present in the blood, it would attach to the SERS tags in the conjugate pad and later be detected by the human immunoglobulins present on the test lines. The limit of detection was as low as 1 pg/mL which is about 2.2 fM (taking the molar mass of SARS-CoV-2 S protein as 455 kDa). Furthermore, according to the authors when the S protein concentration is above 1 ng/mL, the black bands can be easily detected by the naked eye. Another beneficial side of the assay was the quickness of the test because only about 25 min are required to obtain the results. In addition, the substrates can be considered stable, since even after 60 days of storage the detection performance of the SERS-LFIA method was not significantly worsened [8].

5. Clinical Diagnostics of Other Bio-Analytes on Si and Al-Based Surface-Enhanced Raman Spectroscopy Substrates

AgNP@Si substrate used in the research by Kaminska demonstrated 100% accuracy with PLS-Da analysis in the detection of Neisseria gonorrhoeae in male swab specimens and the differentiation from other bacterial pathogens such as Mycoplasma hominis, Mycoplasma genitalium, Ureaplasma urealyticum, and Haemophilus ducreyi [6]. 10 samples infected with gonorrhea or chlamydiosis and 10 healthy male urethra swabs were collected from which 600 spectra were obtained. 120 of spectra were used to test the following method, while the other spectra were used as a training set. Moreover, the following SERS study presented a good sensitivity for N. gonorrhoeae with the limit of detection as low as 100 colony forming units per mL. By the comparison of the bands’ characteristic to infected and healthy samples as shown, sexually transmitted diseases can be diagnosed as fast as 15 min with high accuracy [6]. This demonstrates the promising potential of point-of-care SERS devices with Si-based substrates in the fast and reliable diagnosis of sexually transmitted diseases (STDs) and other illnesses.
100% accurate clinical diagnosis of breast cancer was achieved by Weng et al. for 60 samples [13]. In the following study, the targeted miRNA-21 and miRNA-155 were amplified by applying the isothermal catalytic hairpin assembly (CHA) strategy before SERS analysis. In addition, through the linkage between two-dimensional Au–Si substrate and upper Ag@4-MBA@Au core-shell nanoparticles, a sandwich SERS chip with numerous hot spots was built. The application of the signal second order peak of Si 936 cm−1 as an internal standard calibration enabled the reliable quantitative detection of miRNA with the correlation coefficient (R2) of 0.99. Thus, the ultralow concentration of miRNA-21 and miRNA-155 was detected (0.398 fM and 0.215 fM, respectively) and a 100% accurate diagnosis of breast cancer was performed [13].
Ma et al. compared the conventional AgNPs single-layer porous Si substrates with the AgNP porous silicon Bragg reflector SERS substrate, which was prepared by controlling the corrosion current. It was found that the enhancement coefficient of the Bragg reflector substrate is 3.2 times higher than the single-layer Si substrate. The X-ray diffraction analysis of the substrates demonstrated that the diffraction peaks of the multi-layer porous Si (Bragg reflector) became wider and wider, and the field intensity of the porous Si photonic crystals was stronger. The enhanced electric field on the surface of photonic crystals is effective in attaining a stronger LSPR that results in the enhancement of the Raman signal. As a result of using Bragg reflector porous Si substrate, breast cancer was diagnosed with 95% accuracy in 60 samples [10].
Electric field enhancements of the hybrid silica microsphere covered gold/silver nanoparticles and the pure metal nanoparticles (gold/silver) were investigated in the article by Wang et al. [12]. The interface of the plasmonic metal and dielectric spheres produces extra enhancement of neighboring electromagnetic field therefore both SiO2@Au and SiO2@Ag particles demonstrate substantial E-field enhancement in contrast to pure metal nanoparticles (AgNP and AuNP). It was also revealed that the hybrid silica sphere gold nanoparticles generate more enhancement of the signal than the silica-covered silver nanoparticles at 785 nm, while the silica-covered gold NPs produce higher signal at 532 nm This is because of the matched overlapping between AuNP LSPR in the hybrid particle and the excitation wavelength at 785 nm, whereas the electric field enhancement of SiO2@Ag particle is escalated around the LSPR of AgNP [12].
Metal-free SERS substrate approach using ∼a 2 nm layer of silicon dioxide on Quantum (Q)-probes for in situ live biosensing was attempted by Keshavarz et al. [3] Si@SiO2 Q-probes in contrast with Si Q-Probes displayed the further enhancement of the signal with the decrease in the dye concentration till reaching the limit of 5 pM, whereas the limit for Si Q-probes was about 1 mM. This phenomenon was hypothesized to be caused by the charge transfer mechanism from the semiconductor band edges to the affinity levels of the adsorbed molecule. The clinical diagnosis of HeLa cancer cells using Si@SiO2 substrate resulted in 86% sensitivity and 94% specificity [3].
Even though the number of clinical studies on aluminum is limited if compared with silicon, in general aluminum demonstrated pretty good results in clinical studies that researchers reviewed so far [17][18][19][20][22][23][24]. For example, in the diagnosis of colorectal cancer (CRC), the third most common cause of cancer death worldwide (WHO 2020), aluminum foil alone proved to be not only cheap but also efficient at drying serum samples [19] and collecting the spectrum, along with being diagnostically effective when analyzing the fresh serum samples with sensitivity 83%, specificity 83% and accuracy 83.3% [17]. Several studies where AgNPs have been transferred onto an Al substrate (either Al plate or foil) also showed significantly good clinical results [19][20][22][23][24]. In addition, work by Liu et al. showed the application of aluminum oxide-modified silver nanorods on the diagnosis of lung adenocarcinoma with the sensitivity of 98.1% and specificity of 97.6% [21]. The comparison of spectra before and after atomic layer deposition of 1.5 nm Al2O3 layer on silver nanorods revealed a significant increase in Raman signal after aluminum oxide modification. This comparison and results of LDA analysis. Overall, Ag@Al substrates have demonstrated better clinical specificity (100% Ag@Al vs. 95.5% Au) than both silver and gold and relatively better accuracy (94.2% Ag@Al vs. 92.8% Au) than gold-based substrates. It’s worth mentioning that these values only give the general picture on how aluminum-based substrates perform relative to noble metals. There are several limitations in calculation of average sensitivity/specificity/accuracy values. For example, researchers did not take into account the type of statistical algorithms applied in individual cases. Among studies that researchers reviewed, different multivariate statistical algorithms such as principal component analysis (PCA) and linear discriminate analysis (LDA), support vector machine (SVM), genetic algorithm (GA) combined with linear discriminate analysis (GA-LDA) techniques were used to distinguish different type of cancers from healthy individuals. In terms of performance, GA-LDA is better than the SVM algorithm, while SVM is better than PCA-LDA (GA-LDA>SVM>PCA-LDA), hence the difference between individual sensitivity/specificity/accuracy values was significantly different [22][23][24]. For example, Li et al. compared the performance of PCA-LDA algorithms to SVM in the classification of prostate cancer patients from healthy individuals [19]. The receiver operating characteristic curve (ROC) is a plot that demonstrates the performance of the classification model as a classification threshold is varied. The integration area under the ROC (AUC) of Gaussian radial basis function (RBF) kernel SVM and PCA-LDA were 0.998 and 0.991, respectively, in the work of Li et al. [19]. Generally, the AUC value correlates with diagnostic accuracy. The larger the AUC value, the greater the forecast accuracy for the classifier. From these results, the SVM algorithm exhibits a better accuracy (98.1% vs. 91.3%) than PCA-LDA. There are two possible reasons for the worse performance of the PCA-LDA algorithm. First, PCA-LDA can lose some important diagnostic information while processing the SERS spectra. Second, since PCA-LDA is a linear algorithm, it is unable to distinguish the nonlinear boundary between SERS spectra of prostate cancer and normal serum samples. Similarly, GA-LDA yielded a better diagnostic sensitivity of 90.9% and specificity of 100% than PCA (sensitivity 74.6%, specificity 97.2%) for classifying bladder cancer patients from healthy individuals [23].
In addition, due to different sample sizes, the classification accuracy of certain individual studies that researchers reviewed might not be reliable. For example, Zhao et al. achieved significantly higher accuracy 97.9% (28 serums samples) [24] than Li et al. 91.3% (161 serum samples) using the same PCA-LDA algorithm [19]. Herein, the lucky chance of getting a better result in a smaller sample-sized experiment is likely to be higher than with larger samples.
Finally, it’s worth mentioning that in clinical studies researchers reviewed so far, patients who submitted blood have been at different stages of cancer development. Thus, the results of statistical models that have been applied correspond to different cancer staging and may not be applied for the early detection of cancer as authors of individual studies claim. These and other factors have to be taken into account in the future works to improve the reliability of the reported results and present a clearer picture of the performance of non-noble substrates relative to traditional noble metals.
The clinical performance of the overall Si-based SERS substrates is a bit inferior in comparison to noble metals such as gold, silver, and silver on aluminum. Nevertheless, the silicon coated with noble metals as a substrate has demonstrated the best sensitivity/specificity/accuracy values (95.7%/95.1%/94.4%) and this illustrates the promising potential of the following substrate in clinical detection. The reason for such a claim is that there are already methods that demonstrated 100% accuracy [5][8], and with a slight optimization, the Au or Ag @ silicon-based substrates can be a good alternative to conventional substrates, while Ag@Al-based substrates already show comparable to gold and silver detection parameters.

This entry is adapted from the peer-reviewed paper 10.3390/bios12110967


  1. Yu, Y.; Lin, Y.; Xu, C.; Lin, K.; Ye, Q.; Wang, X.; Xie, S.; Chen, R.; Lin, J. Label-free detection of nasopharyngeal and liver cancer using surface-enhanced Raman spectroscopy and partial lease squares combined with support vector machine. Biomed. Opt. Express 2018, 9, 6053–6066.
  2. Fang, Y.; Lin, T.; Zheng, D.; Zhu, Y.; Wang, L.; Fu, Y.; Wang, H.; Wu, X.; Zhang, P. Rapid and label-free identification of different cancer types based on surface-enhanced Raman scattering profiles and multivariate statistical analysis. J. Cell Biochem. 2021, 122, 277–289.
  3. Keshavarz, M.; Tan, B.; Venkatakrishnan, K. Label-Free SERS Quantum Semiconductor Probe for Molecular-Level and in Vitro Cellular Detection: A Noble-Metal-Free Methodology. ACS Appl. Mater. Interfaces 2018, 10, 34886–34904.
  4. Zhou, L.; Liu, Y.; Wang, F.; Jia, Z.; Zhou, J.; Jiang, T.; Petti, L.; Chen, Y.; Xiong, Q.; Wang, X. Classification analyses for prostate cancer, benign prostate hyperplasia and healthy subjects by SERS-based immunoassay of multiple tumour markers. Talanta 2018, 188, 238–244.
  5. Berus, S.M.; Adamczyk-Poplawska, M.; Mlynarczyk-Bonikowska, B.; Witkowska, E.; Szymborski, T.; Waluk, J.; Kaminska, A. SERS-based sensor for the detection of sexually transmitted pathogens in the male swab specimens: A new approach for clinical diagnosis. Biosens. Bioelectron. 2021, 189, 113358.
  6. Kaminska, A.; Witkowska, E.; Kowalska, A.; Skoczynska, A.; Gawryszewska, I.; Guziewicz, E.; Snigurenko, D.; Waluk, J. Highly efficient SERS-based detection of cerebrospinal fluid neopterin as a diagnostic marker of bacterial infection. Anal. Bioanal. Chem. 2016, 408, 4319–4327.
  7. Meng, S.; Chen, R.; Xie, J.; Li, J.; Cheng, J.; Xu, Y.; Cao, H.; Wu, X.; Zhang, Q.; Wang, H. Surface-enhanced Raman scattering holography chip for rapid, sensitive and multiplexed detection of human breast cancer-associated MicroRNAs in clinical samples. Biosens. Bioelectron. 2021, 190, 113470.
  8. Liu, H.; Dai, E.; Xiao, R.; Zhou, Z.; Zhang, M.; Bai, Z.; Shao, Y.; Qi, K.; Tu, J.; Wang, C.; et al. Development of a SERS-based lateral flow immunoassay for rapid and ultra-sensitive detection of anti-SARS-CoV-2 IgM/IgG in clinical samples. Sens. Actuators B Chem. 2021, 329, 129196.
  9. Zhang, K.; Liu, X.; Man, B.; Yang, C.; Zhang, C.; Liu, M.; Zhang, Y.; Liu, L.; Chen, C. Label-free and stable serum analysis based on Ag-NPs/PSi surface-enhanced Raman scattering for noninvasive lung cancer detection. Biomed. Opt. Express 2018, 9, 4345–4358.
  10. Ma, X.; Cheng, H.; Hou, J.; Jia, Z.; Wu, G.; Lü, X.; Li, H.; Zheng, X.; Chen, C. Detection of breast cancer based on novel porous silicon Bragg reflector surface-enhanced Raman spectroscopy-active structure. Chin. Opt. Lett. 2020, 18, 051701.
  11. Cheng, N.; Lou, B.; Wang, H. An intelligent serological SERS test toward early-stage hepatocellular carcinoma diagnosis through ultrasensitive nanobiosensing. Nano Res. 2022, 15, 5331–5339.
  12. Wang, Z.; Hong, Y.; Yan, H.; Luo, H.; Zhang, Y.; Li, L.; Lu, S.; Chen, Y.; Wang, D.; Su, Y.; et al. Fabrication of optoplasmonic particles through electroless deposition and the application in SERS-based screening of nodule-involved lung cancer. Spectrochim. Acta Part. A Mol. Biomol. Spectrosc. 2022, 279, 121483.
  13. Weng, S.; Lin, D.; Lai, S.; Tao, H.; Chen, T.; Peng, M.; Qiu, S.; Feng, S. Highly sensitive and reliable detection of microRNA for clinically disease surveillance using SERS biosensor integrated with catalytic hairpin assembly amplification technology. Biosens. Bioelectron. 2022, 208, 114236.
  14. Jayakumar, P.; Kapil, D.; Pyng, L.; Hann Qian, L.; Dinish, U.S.; Malini, O. Proof of concept clinical study for the rapid diagnosis of Lung cancer from pleural fluid using label free SERS based chemometric approach. In Proceedings of the SPIE 11655, Label-free Biomedical Imaging and Sensing (LBIS), Online, 5 March 2021.
  15. Buse, B.; Hülya, T.; Müslüm, İ.; Cenk, Y.; Sükrü Numan, B.; Süleyman, Ç.; Meriç, Ö.; Özlem, D.; Önder, E.; Ihsan, S.; et al. Clinical validation of SERS metasurface SARS-CoV-2 biosensor. In Proceedings of the SPIE 11957, Biomedical Vibrational Spectroscopy 2022: Advances in Research and Industry, San Francisco, CA, USA, 2 March 2022.
  16. Czaplicka, M.; Kowalska, A.A.; Nowicka, A.B.; Kurzydłowski, D.; Gronkiewicz, Z.; Machulak, A.; Kukwa, W.; Kamińska, A. Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) spectra of salivary glands carcinoma, tumor and healthy tissues and their homogenates analyzed by chemometry: Towards development of the novel tool for clinical diagnosis. Anal. Chim. Acta 2021, 1177, 338784.
  17. Jenkins, C.A.; Jenkins, R.A.; Pryse, M.M.; Welsby, K.A.; Jitsumura, M.; Thornton, C.A.; Dunstan, P.R.; Harris, D.A. A high-throughput serum Raman spectroscopy platform and methodology for colorectal cancer diagnostics. Analyst 2018, 143, 6014–6024.
  18. Stefancu, A.; Moisoiu, V.; Couti, R.; Andras, I.; Rahota, R.; Crisan, D.; Pavel, I.E.; Socaciu, C.; Leopold, N.; Crisan, N. Combining SERS analysis of serum with PSA levels for improving the detection of prostate cancer. Nanomedicine 2018, 13, 2455–2467.
  19. Li, S.; Zhang, Y.; Xu, J.; Li, L.; Zeng, Q.; Lin, L.; Guo, Z.; Liu, Z.; Xiong, H.; Liu, S. Noninvasive prostate cancer screening based on serum surface-enhanced Raman spectroscopy and support vector machine. Appl. Phys. Lett. 2014, 105, 091104.
  20. Chen, S.; Zhu, S.; Cui, X.; Xu, W.; Kong, C.; Zhang, Z.; Qian, W. Identifying non-muscle-invasive and muscle-invasive bladder cancer based on blood serum surface-enhanced Raman spectroscopy. Biomed. Opt. Express 2019, 10, 3533–3544.
  21. Liu, K.; Jin, S.; Song, Z.; Jiang, L.; Ma, L.; Zhang, Z. Label-free surface-enhanced Raman spectroscopy of serum based on multivariate statistical analysis for the diagnosis and staging of lung adenocarcinoma. Vib. Spectrosc. 2019, 100, 177–184.
  22. Zhang, Y.; Lai, X.; Zeng, Q.; Li, L.; Lin, L.; Li, S.; Liu, Z.; Su, C.; Qi, M.; Guo, Z. Classifying low-grade and high-grade bladder cancer using label-free serum surface-enhanced Raman spectroscopy and support vector machine. Laser Phys. 2018, 28, 035603.
  23. Li, S.; Li, L.; Zeng, Q.; Zhang, Y.; Guo, Z.; Liu, Z.; Jin, M.; Su, C.; Lin, L.; Xu, J.; et al. Characterization and noninvasive diagnosis of bladder cancer with serum surface enhanced Raman spectroscopy and genetic algorithms. Sci. Rep. 2015, 5, 9582.
  24. Xin, Z.; Tingting, L.; Yamin, L.; Jiamin, G.; Wei, G.; Xiang, W.; Yun, Y.; Juqiang, L. Label-free detection of prostate cancer and benign prostatic hyperplasia based on SERS spectroscopy of Plasma. In Proceedings of the SPIE 11900, Optics in Health Care and Biomedical Optics XI, Nantong, China, 9 October 2021.
  25. Li, H.; Wang, Q.; Tang, J.; Gao, N.; Yue, X.; Zhong, F.; Lv, X.; Fu, J.; Wang, T.; Ma, C. Establishment of a reliable scheme for obtaining highly stable SERS signal of biological serum. Biosens. Bioelectron. 2021, 189, 113315.
  26. Tan, Y.; Yan, B.; Xue, L.; Li, Y.; Luo, X.; Ji, P. Surface-enhanced Raman spectroscopy of blood serum based on gold nanoparticles for the diagnosis of the oral squamous cell carcinoma. Lipids Health Dis. 2017, 16, 73.
  27. Lin, D.; Feng, S.; Pan, J.; Chen, Y.; Lin, J.; Chen, G.; Xie, S.; Zeng, H.; Chen, R. Colorectal cancer detection by gold nanoparticle based surface-enhanced Raman spectroscopy of blood serum and statistical analysis. Opt. Express 2011, 19, 13565–13577.
  28. Qian, K.; Wang, Y.; Hua, L.; Chen, A.; Zhang, Y. New method of lung cancer detection by saliva test using surface-enhanced Raman spectroscopy. Thorac. Cancer 2018, 9, 1556–1561.
  29. Del Mistro, G.; Cervo, S.; Mansutti, E.; Spizzo, R.; Colombatti, A.; Belmonte, P.; Zucconelli, R.; Steffan, A.; Sergo, V.; Bonifacio, A. Surface-enhanced Raman spectroscopy of urine for prostate cancer detection: A preliminary study. Anal. Bioanal. Chem. 2015, 407, 3271–3275.
  30. Hong, Y.; Li, Y.; Huang, L.; He, W.; Wang, S.; Wang, C.; Zhou, G.; Chen, Y.; Zhou, X.; Huang, Y.; et al. Label-free diagnosis for colorectal cancer through coffee ring-assisted surface-enhanced Raman spectroscopy on blood serum. J. Biophoton. 2020, 13, e201960176.
  31. Chen, N.; Rong, M.; Shao, X.; Zhang, H.; Liu, S.; Dong, B.; Xue, W.; Wang, T.; Li, T.; Pan, J. Surface-enhanced Raman spectroscopy of serum accurately detects prostate cancer in patients with prostate-specific antigen levels of 4-10 ng/mL. Int. J. Nanomed. 2017, 12, 5399–5407.
  32. Li, B.; Ding, H.; Wang, Z.; Liu, Z.; Cai, X.; Yang, H. Research on the difference between patients with coronary heart disease and healthy controls by surface enhanced Raman spectroscopy. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2022, 272, 120997.
  33. Hernández-Arteaga, A.; de Jesús Zermeño Nava, J.; Kolosovas-Machuca, E.S.; Velázquez-Salazar, J.J.; Vinogradova, E.; José-Yacamán, M.; Navarro-Contreras, H.R. Diagnosis of breast cancer by analysis of sialic acid concentrations in human saliva by surface-enhanced Raman spectroscopy of silver nanoparticles. Nano Res. 2017, 10, 3662–3670.
  34. Liang, J.; Teng, P.; Xiao, W.; He, G.; Song, Q.; Zhang, Y.; Peng, B.; Li, G.; Hu, L.; Cao, D.; et al. Application of the amplification-free SERS-based CRISPR/Cas12a platform in the identification of SARS-CoV-2 from clinical samples. J. Nanobiotechnol. 2021, 19, 273.
  35. Wang, J.; Lin, D.; Lin, J.; Yu, Y.; Huang, Z.; Chen, Y.; Lin, J.; Feng, S.; Li, B.; Liu, N.; et al. Label-free detection of serum proteins using surface-enhanced Raman spectroscopy for colorectal cancer screening. J. Biomed. Opt. 2014, 19, 087003.
  36. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249.
  37. Roehrborn, C.G. Benign prostatic hyperplasia: An overview. Rev. Urol. 2005, 7 (Suppl. S9), S3–S14.
This entry is offline, you can click here to edit this entry!
Video Production Service