Related Experiment Video
Updated: Jul 15, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Integrating plasma proteomes with genome-wide association data for causal protein identification in multiple myeloma
Qiangsheng Wang1, Qiqin Shi2, Zhenqian Wang3
1Department of Hematology, Ningbo Hangzhou Bay Hospital, Ningbo, 315000, Zhejiang, China.
Background:
Multiple myeloma (MM) is a severely debilitating and fatal B-cell neoplastic disease. The discovery of disease-associated proteins with causal genetic evidence offers a chance to uncover novel therapeutic targets.
Methods:
First, we comprehensively investigated the causal association between 2994 proteins and MM through two-sample mendelian randomization (MR) analysis using summary-level data from public genome-wide association studies of plasma proteome (N = 3301 healthy individuals) and MM (598 cases and 180,756 controls). Sensitivity analyses were performed for these identified causal proteins. Furthermore, we pursued the exploration of enriched biological pathways, prioritized the therapeutic proteins, and evaluated their druggability using the KEGG pathway analysis, MR-Bayesian model averaging analysis, and cross-reference with current databases, respectively.
Results:
We identified 13 proteins causally associated with MM risk (false discovery rate corrected P < 0.05). Six proteins were positively associated with the risk of MM, including nicotinamide phosphoribosyl transferase (NAMPT; OR [95% CI]: 1.35 [1.18, 1.55]), tyrosine kinase with immunoglobulin-like and EGF-like domains 1 (TIE1; 1.14 [1.06, 1.22]), neutrophil cytosol factor 2 (NCF2; 1.27 [1.12, 1.44]), carbonyl reductase 1, cAMP-specific 3',5'-cyclic phosphodiesterase 4D (PDE4D), platelet-activating factor acetylhydrolase IB subunit beta (PAFAH1B2). Seven proteins were inversely associated with MM, which referred to suppressor of cytokine signaling 3 (SOCS3; 0.90 [0.86, 0.94]), Fc-gamma receptor III-B (FCGR3B; 0.75 [0.65,0.86]), glypican-1 (GPC1; 0.69 [0.58,0.83]), follistatin-related protein 1, protein tyrosine phosphatase non-receptor type 4 (PTPN4), granzyme B, complement C1q subcomponent subunit C (C1QC). Three of the causal proteins, SOCS3, FCGR3B, and NCF2, were enriched in the osteoclast differentiation pathway in KEGG enrichment analyses while GPC1 (marginal inclusion probability (MIP):0.993; model averaged causal effects (MACE): - 0.349), NAMPT (MIP:0.433; MACE: - 0.113), and NCF2 (MIP:0.324; MACE:0.066) ranked among the top three MM-associated proteins according to MR-BMA analyses. Furthermore, therapeutics targeting four proteins are currently under evaluation, five are druggable and four are future breakthrough points.
Conclusions:
Our analysis revealed a set of 13 novel proteins, including six risk and seven protective proteins, causally linked to MM risk. The discovery of these MM-associated proteins opens up the possibility for identifying novel therapeutic targets, further advancing the integration of genome and proteome data for drug development.
More Related Videos
09:52A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
Published on: January 10, 2025
10:37Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
Related Concept Videos
Proteomics
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...
Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...