Label-free detection of multiple genitourinary cancers from urine by surface-enhanced Raman spectroscopy

Cui, Xiaoyu; Liu, Tao; Xu, Xiaosong; Zhao, Zeyin; Tian, Ye; Zhao, Yue; Chen, Shuo; Wang, Zhe; Wang, Yiding; Hu, Dayu; Fu, Shui; Shan, Guang-yi; Sun, Jiarun; Song, Kexing; Zeng, Yu

doi:10.1016/j.saa.2020.118543

Cited by 28 publications

(7 citation statements)

References 28 publications

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“…Next, we complemented miRNA profiling of urine with that of SERS. Spectral differences between BC and CTRL groups were prominent in SERS bands attributed to purine metabolites and creatinine, yielding an AUC of 0.84 ± 0.05, a result which is in line with previous reports (Li et al 2015;Chen et al 2019;Cui 2020;Hu 2021;Huttanus 2020). However, the present study went further in proving the clinical translatability of SERS in at least two ways.…”

Section: Discussionsupporting

confidence: 92%

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

Moisoiu

Dragomir

Iancu

et al. 2022

Mol Med

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Background Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine. Methods Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types. Results Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 ± 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 ± 0.03) or SERS data (AUC = 0.84 ± 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 ± 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 ± 0.04) or SERS (AUC = 0.92 ± 0.05) individually, although SERS alone performed better in terms of classification accuracy. Conclusion miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged.

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Section: Discussionsupporting

confidence: 92%

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

Moisoiu

Dragomir

Iancu

et al. 2022

Mol Med

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“…Almost all of the reported studies on urine used Au and Ag colloidal substrates because of their reduced complexity and simplicity of use when compared with noncolloidal substrates. As expected, the majority of studies that used urine sampling are linked with bladder 98,105 and prostate 96,97,111 cancer because they are closely linked to the urinary track; hence, any changes in their biochemical composition could be potentially detected.…”

Section: ■ Urinementioning

confidence: 62%

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

Section: Urinementioning

confidence: 92%

“…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. For the detection of lung cancer, Fe 3 O 4 /Au/Ag nanocomposites were synthesized for the development of a magnetically assisted SERS to trace adenosine in urine (acting as a cancer biomarker), as shown in Figure 6a.…”

Section: ■ Urinementioning

confidence: 99%

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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 diagnosis and treatment of different degrees of differentiation is crucial to the survival rate and survival time of patients. [11,12]. Therefore, the accurate classi cation of histopathological images of liver cancer has a decisive and irreplaceable role in the diagnosis of liver cancer with different degrees of differentiation.…”

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confidence: 99%

Classification of Multi-Differentiated Liver Cancer Pathological Images Based on Deep Learning Attention Mechanism

Chen

et al. 2022

Preprint

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Purpose Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious. Methods Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way. Results Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images, Conclusions This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images.

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Label-free detection of multiple genitourinary cancers from urine by surface-enhanced Raman spectroscopy

Cited by 28 publications

References 28 publications

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

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

Classification of Multi-Differentiated Liver Cancer Pathological Images Based on Deep Learning Attention Mechanism

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