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Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

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Updated: Aug 4, 2025

A High Throughput, Multiplexed and Targeted Proteomic CSF Assay to Quantify Neurodegenerative Biomarkers and Apolipoprotein E Isoforms Status

A High Throughput, Multiplexed and Targeted Proteomic CSF Assay to Quantify Neurodegenerative Biomarkers and Apolipoprotein E Isoforms Status

Published on: October 20, 2016

Plasma biomarker panel for major depressive disorder by quantitative proteomics using ensemble learning algorithm: A

Linna Zhang¹, Caiping Liu¹, Yan Li¹

¹Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Psychiatry Research

|April 1, 2023

Summary

This summary is machine-generated.

Researchers identified two key biomarkers, L-selectin and a Ras oncogene family isoform, in plasma to help detect major depressive disorder (MDD). This discovery aids in understanding MDD

Keywords:

Diagnostic panel Isoform of the Ras oncogene family (RAP1B)L-selectin (SELL)Major depressive disorder Proteomics

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Area of Science:

Proteomics
Biomarker Discovery
Computational Biology

Background:

Major depressive disorder (MDD) poses a significant global health challenge.
Early detection and understanding of MDD's molecular mechanisms are crucial.
Current diagnostic methods may lack sensitivity for early-stage detection.

Purpose of the Study:

To identify novel plasma protein biomarkers for early detection of MDD.
To investigate the molecular underpinnings of major depressive disorder.
To develop a predictive model for MDD diagnosis using proteomic data.

Main Methods:

Employed data-independent acquisition-mass spectrometry-based proteomics on plasma samples from MDD patients and healthy controls.
Utilized bioinformatics analyses including Gene Ontology, KEGG pathway analysis, PPI networks, and WGCNA.
Developed a diagnostic prediction model using an ensemble learning technique.

Main Results:

Identified a two-biomarker panel (L-selectin and a Ras oncogene family isoform) with high diagnostic accuracy (AUC 0.925 training, 0.901 test).
The biomarker panel effectively distinguished individuals with MDD from healthy controls.
Numerous potential protein biomarkers and their associated pathways were revealed.

Conclusions:

The identified biomarker panel shows promise for a future plasma-based diagnostic approach for MDD.
This research contributes to a deeper understanding of the molecular mechanisms underlying major depressive disorder.
Further validation could lead to improved early detection strategies for MDD.