Electrochemical and optical detection and machine learning applied to images of genosensors for diagnosis of prostate cancer with the biomarker PCA3

Abstract

The development of simple detection methods aimed at widespread screening and testing is crucial for many infections and diseases, including prostate cancer where early diagnosis increases the chances of cure considerably. In this paper, we report on genosensors with different detection principles for a prostate cancer specific DNA sequence (PCA3). The genosensors were made with carbon printed electrodes or quartz coated with layer-by-layer (LbL) films containing gold nanoparticles and chondroitin sulfate and a layer of a complementary DNA sequence (PCA3 probe). The highest sensitivity was reached with electrochemical impedance spectroscopy with the detection limit of 83 pM in solutions of PCA3, while the limits of detection were 2000 pM and 900 pM for cyclic voltammetry and UV–vis spectroscopy, respectively. That detection could be performed with an optical method is encouraging, as one may envisage extending it to colorimetric tests. Since the morphology of sensing units is known to be affected in detection experiments, we applied machine learning algorithms to classify scanning electron microscopy images of the genosensors and managed to distinguish those exposed to PCA3-containing solutions from control measurements with an accuracy of 99.9%. The performance in distinguishing each individual PCA3 concentration in a multiclass task was lower, with an accuracy of 88.3%, which means that further developments in image analysis are required for this innovative approach.

Electrochemical Detection of Prostate Cancer Biomarker PCA3 Using Specific RNA-Based Aptamer Labelled with Ferrocene

“…The optical detection of PCA3 at concentrations between 200 fM to 5 nM using graphene-oxide nanoparticles modified with short oligonucleotides specific to sections of PCA3 has been reported in [37]; however, the specificity of such detection was not assessed. Both electrochemical [38] and optical [38,39] detection of a short PCA3 ssDNA sequence mimicking the real lncRNA sequence of PCA3 was recently attempted. The reported detection limits were 83 pM for impedance spectroscopy, 2 nM for cyclic voltammograms and 0.9 nM for UV-vis absorption spectroscopy [39].…”

“…Both electrochemical [38] and optical [38,39] detection of a short PCA3 ssDNA sequence mimicking the real lncRNA sequence of PCA3 was recently attempted. The reported detection limits were 83 pM for impedance spectroscopy, 2 nM for cyclic voltammograms and 0.9 nM for UV-vis absorption spectroscopy [39]. However, the detection of lncRNA PCA3 was attempted only qualitatively.…”

“…However, the detection of lncRNA PCA3 was attempted only qualitatively. These sensors could distinguish between PCA3 extracted from different cell lines containing high, low and negligible concentrations of PCA3 [38,39].…”

Detection of a SARS-CoV-2 sequence with genosensors using data analysis based on information visualization and machine learning techniques

“…Machine learning for image analysis is well established, but this applies to the imaging of biological samples. Image analysis of the sensing (or biosensing) units after exposure to biological samples is an embryonic topic, with a single previous contribution reported in the literature, to our best knowledge 57 . In the latter work, the SEM images of genosensors employed for a cancer biomarker had sufficient resolution to reveal nanoscopic structures.…”

Control of Surface Properties of Hyaluronan/Chitosan Multilayered Coatings for Tumor Cell Capture

“…Prostate cancer (PCa) is a slow-growing neoplasm that causes second highest mortality among men, though presenting good chances of cure when diagnosed in its early stages [1]. Currently, the diagnosis is performed through prostate specific antigens (PSA) detection and rectal examination, which do not present the desired accuracy and thus result in many false negative results [2]. Therefore, in this context, the development and improvement of rapid detection methods and prognostic markers for prostate cancer are of great importance in order to contribute to more effective treatments [3].…”