Integrated analyses delineate distinctive immunological pathways and diagnostic signatures for Behcet's disease by leveraging gene microarray data

Mechanism exploration and biomarker identification of glycemic deterioration in patients with diseases of the exocrine pancreas

The damage to the endocrine pancreas among patients with diseases of the exocrine pancreas (DP) leads to reduced glycemic deterioration, ultimately resulting in diabetes of the exocrine pancreas (DEP). The present research aims to investigate the mechanism responsible for glycemic deterioration in DP patients, and to identify useful biomarkers, with the ultimate goal of enhancing clinical practice awareness. Gene expression profiles of patients with DP in this study were acquired from the Gene Expression Omnibus database. The original study defines DP patients to belong in one of three categories: non-diabetic (ND), impaired glucose tolerance (IGT) and DEP, which correspond to normoglycemia, early and late glycemic deterioration, respectively. After ensuring quality control, the discovery cohort included 8 ND, 20 IGT, and 12 DEP, while the validation cohort included 27 ND, 15 IGT, and 20 DEP. Gene set enrichment analysis (GSEA) employed differentially expressed genes (DEGs), while immunocyte infiltration was determined using single sample gene set enrichment analysis (ssGSEA). Additionally, correlation analysis was conducted to establish the link between clinical characteristics and immunocyte infiltration. The least absolute shrinkage and selection operator regression and random forest combined to identify biomarkers indicating glycemic deterioration in DP patients. These biomarkers were further validated through independent cohorts and animal experiments. With glycemic deterioration, biological processes in the pancreatic islets such as nutrient metabolism and complex immune responses are disrupted in DP patients. The expression of ACOT4, B2M, and ACKR2 was upregulated, whereas the expression of CACNA1F was downregulated. Immunocyte infiltration in the islet microenvironment showed a significant positive correlation with the age, body mass index (BMI), HbA1c and glycemia at the 2-h of patients. It was a crucial factor in glycemic deterioration. Additionally, B2M demonstrated a significant positive correlation with immunocyte infiltration and clinical features. Quantitative real-time PCR (qRT-PCR) and western blotting confirmed the upregulation in B2M. Immunofluorescent staining suggested the alteration of B2M was mainly in the alpha cells and beta cells. Overall, the study showed that gradually increased immunocyte infiltration was a significant contributor to glycemic deterioration in patients with DP, and it also highlighted B2M as a biomarker.

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

PURPOSE OF REVIEW

The purpose of this review was to comprehensively summarize recent phenotype research findings in Behçet's syndrome.

RECENT FINDINGS

Cluster analysis has recently been employed as a phenotype research tool in Behçet's syndrome. Studies reported different clustering patterns caused by biological variation and some degree of artificial heterogeneity. However, some clusters were more consistent than others: 1) oral ulcers, genital ulcers, and skin lesions 2) oral ulcers, genital ulcers, skin lesions, and arthritis 3) oral ulcers, genital ulcers, skin lesions, and uveitis 4) oral ulcers, genital ulcers, skin lesions, and gastrointestinal involvement. A number of loci suggestive of differential risk for individual disease manifestations were proposed. Peripheral blood gene expression profile and plasma proteome exhibited significant differences in patients with different organ involvements and were able to differentiate between disease phenotypes. However, these observations require further validation and functional studies.

SUMMARY

Clustering patterns in Behçet's syndrome is highly heterogeneous. Artificial heterogeneity might obscure the true biological variation of disease expression. Preliminary genetic, transcriptomic and proteomic data suggest that different pathogenetic mechanisms may operate in different phenotypes of Behçet's syndrome.