Multi-omics approaches including proteomics analyses are becoming an integral component of precision medicine. As clinical proteomics studies gain momentum and their sensitivity increases, research on identifying individuals based on their proteomics data is here examined for risks and ethics-related issues. A great deal of work has already been done on this topic for DNA/RNA sequencing data, but it has yet to be widely studied in other omics fields. The current state-of-the-art for the identification of individuals based solely on proteomics data is explained. Protein sequence variation analysis approaches are covered in more detail, including the available analysis workflows and their limitations. We also outline some previous forensic and omics proteomics studies that are relevant for the identification of individuals. Following that, we discuss the risks of patient reidentification using other proteomics data types such as protein expression abundance and post-translational modification (PTM) profiles. In light of the potential identification of individuals through proteomics data, possible legal and ethical implications are becoming increasingly important in the field.

/pdf/identifying-individuals-using-proteomics-are-we-there-yet-24mzbhb6.pdf

Identifying individuals using proteomics: are we there yet?

Study on the Targeted Drugs for the Treatment of Thyroid Cancer Based on Network Pharmacology

Background :Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. The primary challenge is identifying patient subgroups with PTC and choosing the most effective treatment approach. Results: To explore the differently expressed proteins (DEPs) between high and low recurrent-risk PTCs, we collected 15 tissues comprising high (n=7) and low (n=8) recurrent-risk groups from PTC. The samples were detected by tandem mass tag labeling proteomics. Using TCGA (The Cancer Genome Atlas) data on thyroid cancer, prognosis-related DEPs were identified. Furthermore, an immunohistochemistry (IHC) stain of 53 cases of PTC tumors was adopted to validate the relation of potential biomarkers with prognosis. We identified 8,958 proteins from the 15 samples, with 95 DEPs obtained by comparing high and low-recurrent-risk groups, including 38 up-regulated and 57 down-regulated proteins. Three down-regulated proteins [Protein S (PROS1), Clusterin (CLU), and Leucine-rich α-2-glycoprotein 1 (LRG1)] were found to be significantly associated with poor overall survival in thyroid cancer using differential analysis and Kaplan-Meier survival analysis. IHC results showed low or moderated expressions of PROS1, CLU, and LRG1 were significantly associated with high-risk clinicopathologic characteristics of PTC. PTC patients with higher expression of PROS1, CLU, and LRG1 had better progression-free survival than those with low or moderate expression. Conclusions: Our study identified PROS1, CLU, and LRG1 as novel prognostic biomarkers in PTC. 

Quantitative proteomics analysis of papillary thyroid carcinoma reveals PROS1, CLU, and LRG1 as potential novel prognostic protein biomarkers

/pdf/author-correction-artificial-intelligence-defines-protein-3nxcwycu.pdf

Author Correction: Artificial intelligence defines protein-based classification of thyroid nodules

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Author Correction: Artificial intelligence defines protein-based classification of thyroid nodules