Fucosylated exosomal miRNAs as promising biomarkers for the diagnosis of early lung adenocarcinoma

Chen, Xiongfeng; Yu, Lili; Hao, Kun; Yin, Xiaoxing; Tu, Min; Cai, Liangchun; Zhang, Liangming; Pan, Xiaojie; Qi, Guo-Jun; Huang, Yi

doi:10.3389/fonc.2022.935184

Cited by 8 publications

(10 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MIHA cells were normal hepatocellular cells, HepG2 cells were hepatocellular carcinoma. The protocol for isolation of EVs by GlyExo-Capture method referred to the manuscripts of Li et al and Chen et al , We differentiated cancer cells from normal cells. Here, we aimed to extend in vitro diagnosis methods, previous studies demonstrated that SERS reveal logical progression biomarkers for the detection of EVs in cancers diagnosis. − …”

Section: Resultsmentioning

confidence: 99%

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification

Chen,

Shen,

Liu

et al. 2024

Anal. Chem.

Self Cite

View full text Add to dashboard Cite

Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback−Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA−SVM, SVM, UMAP−SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.

show abstract

Section: Resultsmentioning

confidence: 99%

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification

Chen,

Shen,

Liu

et al. 2024

Anal. Chem.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hepatoma Extracellular Vesicles (EVs) Detection: The second dataset was from fucosylated extracellular vesicles from MIHA and HepG2 cells for extracellular vesicles test with SERS spectra. The protocol for isolation of extracellular vesicles by GlyExo-Capture method refers to the manuscripts of Li et al and Chen et al 31,32 . We differentiated the cancer cells from normal cells.…”

Section: Resultsmentioning

confidence: 99%

Applications of Data Characteristic AI-assisted Raman Spectroscopy in Pathological Classification

Chen,

Shen,

Liu

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Raman spectroscopy has been widely used for label-free biomolecular analysis of cell and tissue for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy including machine learning (PCA and SVM), manifold learning (UMAP) and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral datasets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and key Raman shifts, explore the performance of different AI models (e.g. PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). Tissue sites mean that spectral collection sites from sample, KL divergence means the divergence between spectra of different types. We found that for dataset of large spectral data size, Resnet performed better than PCA-SVM and UMAP, for dataset of small spectral data size, PCA-SVM or UMAP performed better. We also optimized the network parameters (e.g. principal components, activation function, and loss function) of AI model based on data characteristics. Using AI classification models, the mean area under receiver operating characteristic curves (AUC) for representative datasets reached 0.966, with mean sensitivity of 89.6%, mean specificity of 95.4%, mean accuracy of 93.4%, and mean time expense of 5 seconds. By using data characteristic assisted AI classification model, the accuracy improves from 85.1% to 94.6% for endometrial carcinoma grading, from 77.l% to 90.7% for hepatoma extracellular vesicles detection, from 89.3% to 99.7% for melanoma cell detection, from 88.1% to 97.9% for bacterial identification, from 53.7% to 85.5% for diabetic skin screening. Furthermore, according to the saliency maps, we found classification-associated biomolecules (e.g. nucleic acid, tyrosine, tryptophan, cholesteryl ester, fatty acid, and collagen), which contribute to the pathological diagnosis classification. Data characteristic assisted AI classification model was demonstrated to improve the robustness and accuracy of Raman spectroscopy in pathological classification. Collectively, this study opens up new opportunities for accurate and rapid Raman optical biopsy.

show abstract

“…More precisely, miR-4732-5p showed a significant over expression in the first trimester maternal plasma of women destined to deliver sPTD compared to those with uncomplicated pregnancies. miR4732-5p has a proven role in tumorigenesis [ 19 , 20 ]. However, our GO analysis revealed that its downstream targets include members of suppressors of the cytokine signaling (SOCS) family, namely SOCS1-SOCS7.…”

Section: Discussionmentioning

confidence: 99%

First Trimester Maternal Plasma Aberrant miRNA Expression Associated with Spontaneous Preterm Birth

Mavreli

Theodora

Avgeris

et al. 2022

IJMS

View full text Add to dashboard Cite

Spontaneous Preterm Delivery (sPTD) is one of the leading causes of perinatal mortality and morbidity worldwide. The present case–control study aims to detect miRNAs differentially expressed in the first trimester maternal plasma with the view to identify predictive biomarkers for sPTD, between 320/7 and 366/7 weeks, that will allow for timely interventions for this serious pregnancy complication. Small RNA sequencing (small RNA-seq) of five samples from women with a subsequent sPTD and their matched controls revealed significant down-regulation of miR-23b-5p and miR-125a-3p in sPTD cases compared to controls, whereas miR-4732-5p was significantly overexpressed. Results were confirmed by qRT-PCR in an independent cohort of 29 sPTD cases and 29 controls. Statistical analysis demonstrated that miR-125a is a promising early predictor for sPTL (AUC: 0.895; 95% CI: 0.814-0.972; p < 0.001), independent of the confounding factors tested, providing a useful basis for the development of a novel non-invasive predictive test to assist clinicians in estimating patient-specific risk.

show abstract

Fucosylated exosomal miRNAs as promising biomarkers for the diagnosis of early lung adenocarcinoma

Cited by 8 publications

References 42 publications

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification

Applications of Data Characteristic AI-assisted Raman Spectroscopy in Pathological Classification

First Trimester Maternal Plasma Aberrant miRNA Expression Associated with Spontaneous Preterm Birth

Contact Info

Product

Resources

About