Antigen-Mimic Nanoparticles in Ultrasensitive on-Chip Integrated Anti-p53 Antibody Quantification

As a tumor-suppressing protein, p53 plays a crucial role in preventing cancer development. Its utility as an early cancer detection tool is significant, potentially enabling clinicians to forestall disease advancement and improve patient prognosis. In response to the pathological overexpression of this antigen in tumors, the prevalence of anti-p53 antibodies increases in serum, in a manner quantitatively indicative of cancer progression. This spike can be detected through techniques, such as Western blotting, immunohistochemistry, and immunoprecipitation. In this study, we present an electrochemical approach that supports ultrasensitive and highly selective anti-p53 autoantibody quantification without the use of an immuno-modified electrode. We specifically employ antigen-mimicking and antibody-capturing peptide-coated magnetic nanoparticles, along with an AC magnetic field-promoted sample mixing, prior to the presentation of Fab-captured targets to simple lectin-modified sensors. The subfemtomolar assays are highly selective and support quantification from serum-spiked samples within minutes

Biomarkers and Clinical Labs Across Cohorts.

<p>Note: Mean difference reflects the mean difference between cases and controls divided by the its standard deviation.</p

The density plot the Pearson's correlation coefficients between serum and plasma in TARC cohort.

<p>We used Mclust (model-based clustering algorithm <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0028092#pone.0028092-Fraley1" target="_blank">[21]</a>) package in R to fit the data and discovered two clusters in the correlation coefficients: one (red) corresponding to low correlation and the other (blue) corresponding to high correlation. The threshold value that separated these two clusters most effectively is 0.75. The black line is the density plot of all biomarkers. The dots represent the correlation coefficients of the biomarkers and the color indicates the cluster membership.</p

Outline of methods.

<p>Outline of methods.</p

ROC curve for serum-plasma based biomarker algorithm.

<p>Each line represents the AUC of the respective portions of the algorithm with the yellow line reflecting chance.</p

Diagnostic accuracy of the serum-plasma algorithm.

<p>Note: AUC = area under the receiver operating characteristic curve; SN = sensitivity; SP = specificity; CI = confidence interval; demographic = age, gender, education, <i>APOE*E4</i> status (presence/absence); clinical = glucose, triglycerides, total cholesterol, homocysteine.</p