Identification of the immune-related biomarkers in Behcet’s disease by plasma proteomic analysis

Abstract

Background This study aimed to investigate the expression profile of immune response-related proteins of Behcet’s disease (BD) patients and identify potential biomarkers for this disease. Methods Plasma was collected from BD patients and healthy controls (HC). Immune response-related proteins were measured using the Olink Immune Response Panel. Differentially expressed proteins (DEPs) were used to construct prediction models via five machine learning algorithms: naive Bayes, support vector machine, extreme gradient boosting, random forest, and neural network. The prediction performance of the five models was assessed using the area under the curve (AUC) value, recall (sensitivity), specificity, precision, accuracy, F1 score, and residual distribution. Subtype analysis of BD was performed using the consensus clustering method. Results Proteomics results showed 43 DEPs between BD patients and HC (P < 0.05). These DEPs were mainly involved in the Toll-like receptor 9 and NF-κB signaling pathways. Five models were constructed using DEPs [interleukin 10 (IL10), Fc receptor like 3 (FCRL3), Mannan-binding lectin serine peptidase 1 (MASP1), NF2, moesin-ezrin-radixin like (MERLIN) tumor suppressor (NF2), FAM3 metabolism regulating signaling molecule B (FAM3B), and O-6-methylguanine-DNA methyltransferase (MGMT)]. Among these models, the neural network model showed the best performance (AUC = 0.856, recall: 0.692, specificity: 0.857, precision: 0.900, accuracy: 0.750, F1 score: 0.783). BD patients were divided into two subtypes according to the consensus clustering method: one with high disease activity in association with higher expression of tripartite motif-containing 5 (TRIM5), SH2 domain-containing 1A (SH2D1A), phosphoinositide-3-kinase adaptor protein 1 (PIK3AP1), hematopoietic cell-specific Lyn substrate 1 (HCLS1), and DNA fragmentation factor subunit alpha (DFFA) and the other with low disease activity in association with higher expression of C–C motif chemokine ligand 11 (CCL11). Conclusions Our study not only revealed a distinctive immune response-related protein profile for BD but also showed that IL10, FCRL3, MASP1, NF2, FAM3B, and MGMT could serve as potential immune biomarkers for this disease. Additionally, a novel molecular disease classification model was constructed to identify subsets of BD.

Cluster analysis as a clinical and research tool in Behçet's syndrome

“…Although disease manifestations were not statistically different between the two clusters, the results might be prone to type II error due to a small sample size. The cluster with male predominance, younger age and a shorter disease duration tended to have more frequent oral ulcers, genital ulcers, skin lesions, a higher disease activity, and less arthritis [30 ▪▪ ]. Among a number of proteins with differential expression in patients with and without uveitis, plexin A4, a neuropilin-binding protein and a semaphorin receptor implicated in the regulation of axonogenesis, was the most prominent and the difference was even more marked in the validation cohort [30 ▪▪ ].…”

“…The cluster with male predominance, younger age and a shorter disease duration tended to have more frequent oral ulcers, genital ulcers, skin lesions, a higher disease activity, and less arthritis [30 ▪▪ ]. Among a number of proteins with differential expression in patients with and without uveitis, plexin A4, a neuropilin-binding protein and a semaphorin receptor implicated in the regulation of axonogenesis, was the most prominent and the difference was even more marked in the validation cohort [30 ▪▪ ]. In the other study [31 ▪▪ ], researchers identified 220 out of 759 plasma proteins differentially expressed in 98 patients with Behçet's syndrome compared to 31 healthy controls.…”

“…Two recent studies from China investigated the association between plasma proteomics profile and disease manifestations in Behçet's syndrome [30 ▪▪ ,31 ▪▪ ]. With six out of 43 differentially expressed immune response-related proteins, it was possible to differentiate 27 patients with Behçet's syndrome from 25 healthy controls via a neural network model with high accuracy [30 ▪▪ ]. A clustering algorithm was applied based on clinical findings, age, sex and 43 differentially expressed proteins and revealed two clusters with distinct protein expression profiles.…”

Exploration of effective biomarkers for venous thrombosis embolism in Behçet’s disease based on comprehensive bioinformatics analysis

“…Microarray-based gene expression profiling has emerged as a prominent tool in biomedical research, enabling the identification of potential biomarkers. Studies have identified candidate biomarkers for both BD and VTE, including IL10, FCRL3, MASP1, NF2, FAM3B, and MGMT for BD 11 , and ISG15 , RPS15A , MRPL13 , ICT1 , MRPL15 , and RPLP0 for VTE 12 . Additionally, research suggests that circulating microRNAs (miR-20a-3p, miR-134, miR-522-3p, and miR-874) may regulate genes encoding exosome components ( TLN1 , LAMTOR3 , STX7 , and IMPA1 ), potentially influencing exosome involvement in VTE pathophysiology 13 .…”