Analysis of immune cell components and immune-related gene expression profiles in peripheral blood of patients with type 1 diabetes mellitus

• Published in: Journal of translational medicine 2021-07, Vol.19 (1), p.1-319, Article 319
• Main Authors: Lin, Jian, Lu, Yuanhua, Wang, Bizhou, Jiao, Ping, Ma, Jie
• Format: Article
• Language: English
• Subjects: Autoimmune diseases; Beta cells; Biomarkers; CD4 antigen; Cytotoxicity; Datasets; Development and progression; Diabetes; Diabetes mellitus (insulin dependent); Gene expression; Gene expression profiles; Genetic aspects; Health aspects; Immune cells; Immune system; Immunological memory; Immunotherapy; Leukocytes (neutrophilic); Lymphocytes; Lymphocytes T; Macrophages; Mast cells; Memory cells; Monocytes; Pancreas; Pancreatic beta cells; Pathogenesis; Peripheral blood; Plasma cells; Regression analysis; Tumor necrosis factor; Tumor necrosis factor-TNF; Type 1 diabetes; Type 1 diabetes mellitus
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/s12967-021-02991-3

Background Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease caused by severe loss of pancreatic [beta] cells. Immune cells are key mediators of [beta] cell destruction. This study attempted to investigate the role of immune cells and immune-related genes in the occurrence and development of T1DM. Methods The raw gene expression profile of the samples from 12 T1DM patients and 10 normal controls was obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by Limma package in R. The least absolute shrinkage and selection operator (LASSO)--support vector machines (SVM) were used to screen the hub genes. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. Correlation of the hub genes and immune cells was analyzed by Spearman, and gene-GO-BP and gene-pathway interaction networks were constructed by Cytoscape plug-in ClueGO. Receiver operating characteristic (ROC) curves were used to assess diagnostic value of genes in T1DM. Results The 50 immune-related DEGs were obtained between the T1DM and normal samples. Then, the 50 immune-related DEGs were further screened to obtain the 5 hub genes. CIBERSORT analysis revealed that the distribution of plasma cells, resting mast cells, resting NK cells and neutrophils had significant difference between T1DM and normal samples. Natural cytotoxicity triggering receptor 3 (NCR3) was significantly related to the activated NK cells, M0 macrophages, monocytes, resting NK cells, and resting memory CD4.sup.+ T cells. Moreover, tumor necrosis factor (TNF) was significantly associated with naive B cell and naive CD4.sup.+ T cell. NCR3 [Area under curve (AUC) = 0.918] possessed a higher accuracy than TNF (AUC = 0.763) in diagnosis of T1DM. Conclusions The immune-related genes (NCR3 and TNF) and immune cells (NK cells) may play a vital regulatory role in the occurrence and development of T1DM, which possibly provide new ideas and potential targets for the immunotherapy of diabetes mellitus (DM). Keywords: Type 1 diabetes mellitus, Peripheral blood, Immune cells, Gene expression profiles