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This article explores the potential of plasma extrachromosomal circular DNA (eccDNA) as a diagnostic biomarker for distinguishing between benign and malignant thyroid nodules, specifically papillary thyroid carcinoma (PTC). The study utilizes Circle-seq technology and machine learning to analyze eccDNA profiles in plasma samples from healthy controls, nodular thyroid goitre patients, and PTC patients. The results show significant differences in eccDNA counts and genomic characteristics between the groups, suggesting the potential of eccDNA as a non-invasive biomarker for diagnosing thyroid cancer. However, the study acknowledges the limitation of a small sample size and the need for further research to validate the findings. [Extracted from the article]

Motivation The peptide-centric identification methodologies of data-independent acquisition (DIA) data mainly rely on scores for the mass spectrometric signals of targeted peptides. Among these scores, the coelution scores of peak groups constructed by the chromatograms of peptide fragment ions have a significant influence on the identification. Most of the existing coelution scores are achieved by artificially designing some functions in terms of the shape similarity, retention time shift of peak groups. However, these scores cannot characterize the coelution robustly when the peak group is in the circumstance of interference. Results On the basis that the neural network is more powerful to learn the implicit features of data robustly from a large number of samples, and thus minimizing the influence of data noise, in this work, we propose Alpha-XIC, a neural network-based model to score the coelution. By learning the characteristics of the coelution of peak groups derived from the being analyzed DIA data, Alpha-XIC is capable of yielding robust coelution scores even for peak groups with interference. With this score appending to initial scores generated by the accompanying identification engine DIA-NN, the ensuing statistical validation can report the identification result and recover the misidentified peptides. In our evaluation of the HeLa dataset with gradient lengths ranging from 0.5 to 2 h, Alpha-XIC delivered 9.4–16.2% improvements in the number of identified precursors at 1% false discovery rate. Furthermore, Alpha-XIC was tested on LFQbench, a mixed-species dataset with known ratios, and increased the number of peptides and proteins fell within valid ratios by up to 16.4% and 17.8%, respectively, compared to the initial identification by DIA-NN. Availability and implementation Source code is available at https://github.com/YuAirLab/Alpha-XIC. [ABSTRACT FROM AUTHOR]