Label‐free liquid biopsy based on urine analysis using surface‐enhanced Raman spectroscopy for noninvasive gastric and breast cancer detection

Lin, Xueliang; Wu, Qiong; Lin, Jinyong; Liu, Xiaokun; Jia, Xianggang; Peng, Min; Lin, Duo; Qiu, Sufang; Feng, Shouhua

doi:10.1002/jrs.5978

Cited by 20 publications

(16 citation statements)

References 43 publications

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“…These samples included samples that are likely to be confused in mass screening of GC. Most of the previous work was only deduced on a small sample size [9], [33], [37]. Only a few works were studied on a large dataset with thousands of samples, as in this work, such as in works by Hu et al [19] and Huang et al [32].…”

Section: Comprehensive Multiple Indices Can Achieve Better Resultsmentioning

confidence: 99%

“…The sensitivity of the traditional GC biomarker pepsinogen was 69% [32]. Lin et al used urinary surface-enhanced Raman spectroscopy (SERS) to diagnose GC from healthy sample based on gold nanoparticles, with a sensitivity of 90% and specificity of 93.8% [33]. However, Lin's study was not efficient at distinguishing between GC and breast cancer, with specificity of 81.4% and a lower sensitivity of 62.0%.…”

Section: A Advantages Of This Studymentioning

confidence: 99%

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Identification Tool for Gastric Cancer Based on Integration of 33 Clinical Available Blood Indices Through Deep Learning

et al. 2022

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Gastric cancer (GC) is one of the most common cancers in the world. In cancer detection, liquid biopsy, as a noninvasive and rapid method, is growing in importance. Different from traditional liquid biopsy using a single biomarker, this study integrated a variety of blood biochemical indices and established an identification system by means of deep learning under the H2O framework method. Based on data from 2951 samples, 58 routine blood biochemical indices, age and gender were collected as comprehensive indices to establish the identification model. Then, the number of indices was reduced to simplify the model, and 33 indices were utilized to build the final identification tool. A tenfold crossvalidation technique was used to evaluate the performance of the proposed method. The sensitivity, specificity, accuracy, and area under the ROC curve on the cross-validation set were 85.44%, 83.82%, 84.54% and 0.9165, respectively. The identification tool is built free online at http://www.cppdd.cn/GC2. The proposed system provides a new approach to identify GC with advantages of being efficient, noninvasive and economical. The deep learning of the integration of these blood biochemical indices will bring insights into the comprehensive understanding of GC pathology, as well as the prevention, screening, diagnosis, and prognosis of GC. INDEX TERMSClinical available blood indices, deep learning, gastric cancer, identification, online server LONG CHENG is currently pursuing the Ph.D. degree in the Department of Gastroenterology, Lanzhou University Second Hospital. His current research interests include digestive system diseases such as ulcerative colitis. YUZHEN NIU received the Ph.D. degree from the

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Section: Comprehensive Multiple Indices Can Achieve Better Resultsmentioning

confidence: 99%

Section: A Advantages Of This Studymentioning

confidence: 99%

Identification Tool for Gastric Cancer Based on Integration of 33 Clinical Available Blood Indices Through Deep Learning

et al. 2022

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“…The group used this technique and reported diagnostic accuracies of over 95% with PLS-DA. 102 Similar studies using colloidal NPs were conducted for the discrimination of other types of cancers including gastric−breast 103,104 and bladder−prostate. 105 Noncolloidal Substrates.…”

Section: ■ Urinementioning

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.

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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.

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“…The potential of blood-based vibrational spectroscopy in the diagnosis of neurodegenerative diseases such as Alzheimer's and Parkinson's has been demonstrated ( 63 – 66 ). Additionally, other sample types such as saliva and urine have shown diagnostic capabilities in other disease types such as gastric cancer ( 67 ) and oral disease ( 68 ).…”

Section: Application Of Raman Spectroscopy In Disease Diagnosticsmentioning

confidence: 99%

Vibrational Spectroscopy: A Valuable Screening and Diagnostic Tool for Obstetric Disorders?

Richards

Jenkins

Griffiths

et al. 2021

Front. Glob. Womens Health

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Preeclampsia (PE) is a common obstetric disorder typically affecting 2–8% of all pregnancies and can lead to several adverse obstetric outcomes for both mother and fetus with the greatest burden of severe outcomes in low middle-income countries (LMICs), therefore, screening for PE is vital. Globally, screening is based on maternal characteristics and medical history which are nonspecific for the disorder. In 2004, the World Health Organization acknowledged that no clinically useful test was able to predict the onset of PE, which prompted a universal search for alternative means of screening. Over the past decade or so, emphasis has been placed on the use of maternal characteristics in conjunction with biomarkers of disease combined into predictive algorithms, however these are yet to transition into the clinic and are cost prohibitive in LMICs. As a result, the screening paradigm for PE remains unchanged. It is evident that novel approaches are needed. Vibrational spectroscopy, specifically Raman spectroscopy and Fourier-transform infrared spectroscopy (FTIR), could provide better alternatives suited for implementation in low resource settings as no specialized reagents are required for conventional approaches and there is a drive to portable platforms usable in both urban and rual community settings. These techniques are based on light scattering and absorption, respectively, allowing detailed molecular analysis of samples to produce a unique molecular fingerprint of diseased states. The specificity of vibrational spectroscopy might well make it suited for application in other obstetric disorders such as gestational diabetes mellitus and obstetric cholestasis. In this review, we summarize current approaches sought as alternatives to current screening methodologies and introduce how vibrational spectroscopy could offer superior screening and diagnostic paradigms in obstetric care. Additionally, we propose a real benefit of such tools in LMICs where limited resources battle the higher prevalence of obstetric disorders.

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Label‐free liquid biopsy based on urine analysis using surface‐enhanced Raman spectroscopy for noninvasive gastric and breast cancer detection

Cited by 20 publications

References 43 publications

Identification Tool for Gastric Cancer Based on Integration of 33 Clinical Available Blood Indices Through Deep Learning

Identification Tool for Gastric Cancer Based on Integration of 33 Clinical Available Blood Indices Through Deep Learning

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

Vibrational Spectroscopy: A Valuable Screening and Diagnostic Tool for Obstetric Disorders?

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