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Plasma proteomic analysis on neuropathic pain in idiopathic peripheral neuropathy patients.
Journal of the Peripheral Nervous System : JPNS 2023 November 22
BACKGROUND AND AIMS: Why only half of the idiopathic peripheral polyneuropathy (IPN) patients develop neuropathic pain is unknown. By conducting a proteomics analysis on IPN patients, we aimed to discover proteins and new pathways that are associated with neuropathic pain.
METHODS: We conducted unbiased mass-spectrometry proteomics analysis on blood plasma from 31 IPN patients with severe neuropathic pain and 29 IPN patients with no pain, to investigate protein biomarkers and protein-protein interactions associated with neuropathic pain. Univariate modeling was done with Linear Mixed Modeling (LMM) and corrected for multiple testing. Multivariate modelling was performed using elastic net analysis and validated with internal cross validation and bootstrapping.
RESULTS: In the univariate analysis, 73 proteins showed a p-value <0.05 and 12 proteins showed a p-value <0.01. None were significant after Benjamini-Hochberg adjustment for multiple testing. Elastic net analysis created a model containing 12 proteins with reasonable discriminatory power to differentiate between painful and painless IPN (false negative rate 0.10, false positive rate 0.18 and an area under the curve 0.75). Eight of these 12 proteins were clustered into one interaction network, significantly enriched for the complement and coagulation pathway (Benjamini Hochberg adjusted p-value = 0.0057), with Complement Component 3 (C3) as the central node. Bootstrap validation identified insulin-like growth factor-binding protein 2 (IGFBP2), complement factor H-related protein 4 (CFHR4) and ferritin light chain (FTL), as the most discriminatory proteins of the original 12 identified.
INTERPRETATION: This proteomics analysis suggests a role for the complement system in neuropathic pain in IPN. This article is protected by copyright. All rights reserved.
METHODS: We conducted unbiased mass-spectrometry proteomics analysis on blood plasma from 31 IPN patients with severe neuropathic pain and 29 IPN patients with no pain, to investigate protein biomarkers and protein-protein interactions associated with neuropathic pain. Univariate modeling was done with Linear Mixed Modeling (LMM) and corrected for multiple testing. Multivariate modelling was performed using elastic net analysis and validated with internal cross validation and bootstrapping.
RESULTS: In the univariate analysis, 73 proteins showed a p-value <0.05 and 12 proteins showed a p-value <0.01. None were significant after Benjamini-Hochberg adjustment for multiple testing. Elastic net analysis created a model containing 12 proteins with reasonable discriminatory power to differentiate between painful and painless IPN (false negative rate 0.10, false positive rate 0.18 and an area under the curve 0.75). Eight of these 12 proteins were clustered into one interaction network, significantly enriched for the complement and coagulation pathway (Benjamini Hochberg adjusted p-value = 0.0057), with Complement Component 3 (C3) as the central node. Bootstrap validation identified insulin-like growth factor-binding protein 2 (IGFBP2), complement factor H-related protein 4 (CFHR4) and ferritin light chain (FTL), as the most discriminatory proteins of the original 12 identified.
INTERPRETATION: This proteomics analysis suggests a role for the complement system in neuropathic pain in IPN. This article is protected by copyright. All rights reserved.
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