Detection of liver cancer and prostate cancer by serum surface-enhanced Raman spectroscopy

2021 ◽  
Author(s):  
Jiamin Gao ◽  
Yamin Lin ◽  
Mengmeng Zheng ◽  
Shuzhen Tang ◽  
Wei Gong ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Raman Spectroscopy ◽  
Liver Cancer ◽  
Surface Enhanced Raman Spectroscopy ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

scholarly journals Surface-Enhanced Raman Spectroscopy to Characterize Different Fractions of Extracellular Vesicles from Control and Prostate Cancer Patients

Biomedicines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 580
Author(s):  
Eric Boateng Osei ◽  
Liliia Paniushkina ◽  
Konrad Wilhelm ◽  
Jürgen Popp ◽  
Irina Nazarenko ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Raman Spectroscopy ◽  
Cancer Patients ◽  
Extracellular Vesicles ◽  
Protein Secondary Structure ◽  
Surface Enhanced Raman Spectroscopy ◽  
Cancer Diagnostics ◽  
Label Free ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Extracellular vesicles (EVs) are membrane-enclosed structures ranging in size from about 60 to 800 nm that are released by the cells into the extracellular space; they have attracted interest as easily available biomarkers for cancer diagnostics. In this study, EVs from plasma of control and prostate cancer patients were fractionated by differential centrifugation at 5000× g, 12,000× g and 120,000× g. The remaining supernatants were purified by ultrafiltration to produce EV-depleted free-circulating (fc) fractions. Spontaneous Raman and surface-enhanced Raman spectroscopy (SERS) at 785 nm excitation using silver nanoparticles (AgNPs) were employed as label-free techniques to collect fingerprint spectra and identify the fractions that best discriminate between control and cancer patients. SERS spectra from 10 µL droplets showed an enhanced Raman signature of EV-enriched fractions that were much more intense for cancer patients than controls. The Raman spectra of dehydrated pellets of EV-enriched fractions without AgNPs were dominated by spectral contributions of proteins and showed variations in S-S stretch, tryptophan and protein secondary structure bands between control and cancer fractions. We conclude that the AgNPs-mediated SERS effect strongly enhances Raman bands in EV-enriched fractions, and the fractions, EV12 and EV120 provide the best separation of cancer and control patients by Raman and SERS spectra.

scholarly journals Surface-enhanced raman spectroscopy of serum predicts sensitivity to docetaxel-based chemotherapy in patients with metastatic castration-resistant prostate cancer

2021 ◽  
pp. 2141006
Author(s):  
Jianian Hu ◽  
Xiaoguang Shao ◽  
Chenfei Chi ◽  
Yinjie Zhu ◽  
Zhixiang Xin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Raman Spectroscopy ◽  
Cross Validation ◽  
Prediction Models ◽  
Surface Enhanced Raman Spectroscopy ◽  
Castration Resistant Prostate Cancer ◽  
Castration Resistant ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Docetaxel Chemotherapy

Docetaxel-based chemotherapy, as the first-line treatment for metastatic castration-resistant prostate cancer (mCRPC), has succeeded in helping quite a number of patients to improve quality of life and prolong survival time. However, almost half of mCRPC patients are not sensitive to docetaxel chemotherapy initially. This study aimed to establish models to predict sensitivity to docetaxel chemotherapy in patients with mCRPC by using serum surface-enhanced Raman spectroscopy (SERS). A total of 32 mCPRC patients who underwent docetaxel chemotherapy at our center from July 2016 to March 2018 were included in this study. Patients were dichotomized in prostate-specific antigen (PSA) response group ([Formula: see text]) versus PSA failure group ([Formula: see text]) according to the response to docetaxel. 64 matched spectra from 32 mCRPC patients were obtained by using SERS of serum at baseline ([Formula: see text]0) and after 1 cycle of docetaxel chemotherapy ([Formula: see text]1). Comparing Raman peaks of serum samples at baseline ([Formula: see text]0) between two groups, significant differences revealed at the peaks of 638, 810, 890 ([Formula: see text]) and 1136[Formula: see text]cm[Formula: see text] ([Formula: see text]). The prediction models of peak 1363[Formula: see text]cm[Formula: see text] and principal component analysis and linear discriminant analysis (PCA–LDA) based on Raman data were established, respectively. The sensitivity and specificity of the prediction models were 71%, 80% and 69%, 78% through the way of leave-one-out cross-validation. According to the results of five-cross-validation, the PCA–LDA model revealed an accuracy of 0.73 and AUC of 0.83.

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