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Development of Electrochemical Aptasensor for Lung Cancer Diagnostics in Human Blood.

ABSTRACT: We describe the preparation and characterization of an aptamer-based electrochemical sensor to lung cancer tumor markers in human blood. The highly reproducible aptamer sensing layer with a high density (up to 70% coverage) on the gold electrode was made. Electrochemical methods and confocal laser scanning microscopy were used to study the stability of the aptamer layer structure and binding ability. A new blocking agent, a thiolated oligonucleotide with an unrelated sequence, was applied to fill the aptamer layer's defects. Electrochemical aptasensor signal processing was enhanced using deep learning and computer simulation of the experimental data array. It was found that the combinations (coupled and tripled) of cyclic voltammogram features allowed for distinguishing between the samples from lung cancer patients and healthy candidates with a mean accuracy of 0.73. The capacitive component from the non-Faradic electrochemical impedance spectroscopy data indicated the tumor marker's presence in a sample. These findings allowed for the creation of highly informative aptasensors for early lung cancer diagnostics.

SUBMITTER: Shabalina AV

PROVIDER: S-EPMC8659852 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Development of Electrochemical Aptasensor for Lung Cancer Diagnostics in Human Blood.

Shabalina Anastasiia V AV Sharko Darya O DO Glazyrin Yury E YE Bolshevich Elena A EA Dubinina Oksana V OV Kim Anastasiia M AM Veprintsev Dmitry V DV Lapin Ivan N IN Zamay Galina S GS Krat Alexey V AV Zamay Sergey S SS Svetlichnyi Valery A VA Kichkailo Anna S AS Berezovski Maxim V MV

Sensors (Basel, Switzerland) 20211125 23

We describe the preparation and characterization of an aptamer-based electrochemical sensor to lung cancer tumor markers in human blood. The highly reproducible aptamer sensing layer with a high density (up to 70% coverage) on the gold electrode was made. Electrochemical methods and confocal laser scanning microscopy were used to study the stability of the aptamer layer structure and binding ability. A new blocking agent, a thiolated oligonucleotide with an unrelated sequence, was applied to fil ...[more]

PMID: 34883850

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Project description:IntroductionMinimally invasive diagnostics based on liquid biopsy makes it possible early detection of lung cancer (LC). The blood plasma circulating cell-free DNA (cfDNA) fragments reflect the genome and chromatin status and are considered as integral cancer biomarkers and the biological entities for 'cancer-of-origin' prediction. The aim of this work is to create a method for processing next-generation sequencing (NGS) data and an interpretable binary classification model (CM), which analyzed cfDNA fragmentation features for distinguishing healthy subjects and subjects with LC.Methods148 healthy subjects and 138 subjects with LC were included in the study. cfDNA fractions, isolated from blood plasma biospecimens, were used for DNA libraries preparations and NGS on the NovaSeq 6,000 Illumina system with a coverage of 100 million reads/sample. Twelve variables, describing the abundance and length distribution of cfDNA fragments within each genomic interval, and 40 variables based on the values of position-weight matrices, describing combinations of 5-bp-long terminal motifs of cfDNA fragments, were used to characterize genomic fragmentation. Classification models of the first phase of machine learning were based either on logistic regression with L1- and L2-regularization or were probabilistic CMs based on Gaussian processes. The second phase CM was based on kernel logistic regression.ResultsThe final CM can distinguish healthy subjects and subjects with LC with AUC values of 0.872-0.875. The performance of developed CM was evaluated using datum and testing sets for each LC stage category. Sensitivity values ranged from 66.7 to 85.7%, from 77.8 to 100%, and from 70 to 80% for LC stages I, II, and III, respectively. Specificity values ranged from 79.3 to 90.0%.DiscussionThus, the CM has a good diagnostic value and does not require clinical or other data on tumor-associated biomarkers. The current method for LC detection has some advantages for future clinical implementation as a decision-making support system due to the performance of the CM requires data exclusively from NGS-analysis of blood plasma cfDNA fragmentation; the accuracy of the CM does not depend on any additional clinical data; the CM is highly interpretable and traceable; CM has appropriate modular architecture.

Dataset Information

Development of Electrochemical Aptasensor for Lung Cancer Diagnostics in Human Blood.

Publications

Development of Electrochemical Aptasensor for Lung Cancer Diagnostics in Human Blood.

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