Integrating plasma proteomes with genome-wide association data for causal protein identification in multiple myeloma

Abstract

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.

Adding metabolic tasks to Human GEM models to improve the study of gene targets and their associated toxicities

“…Ignoring this can lead to neglect of important genes, such as NAMPT, being left out of the calculation. NAMPT is known to be associated with myeloma multiple [12,29,30,31]. Although this gene does not appear in any gMCs related to Biomass production for myeloma lines, it is part of a gMCS related to growth under Ham's medium that is common to both MM lines.…”

Sodium-glucose cotransporter 2 inhibitors, inflammation, and heart failure: a two-sample Mendelian randomization study

“…We analyzed only genetic variants that were independently associated (LD r2 < 0.001 within a 10,000 kb range) and had genome-wide significance (P < 5e–8) at the gene level for each inflammatory biomarker. In cases where the number of SNPs for inflammatory biomarkers was less than 3, we used a suggestive genome-wide P-value threshold (P < 5e–7) to identify a sufficient number of SNPs (at least 3) between the inflammatory biomarkers and HF [ 14 , 15 ]. To address weak instrument bias in our instrumental variable analysis, we calculated the F-statistic for the selected SNPs as a test of weak instrument bias.…”

Systematic proteome-wide Mendelian randomization using the human plasma proteome to identify therapeutic targets for lung adenocarcinoma

“…GWAS of plasma proteins have revealed genetic variants linked to these proteins, known as “protein quantitative trait loci (pQTLs)” [ 14 ]. With the advancement of GWASs in investigating the human plasma proteome, an optimization framework integrating genomic and proteomic databases has emerged for biomarker discovery [ 15 ]. Although GWAS have identified many risk loci associated with lung cancer, the genetic underpinnings of the disease remain incompletely understood.…”