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

Jian Lin; Yuanhua Lu; Bizhou Wang; Ping Jiao; Jie Ma

doi:10.1186/s12967-021-02991-3

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

J Transl Med. 2021 Jul 26;19(1):319. doi: 10.1186/s12967-021-02991-3.

Authors

Jian Lin¹, Yuanhua Lu¹, Bizhou Wang², Ping Jiao³, Jie Ma⁴

Affiliations

¹ Department of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, 1266 Fujin Road, Changchun, Jilin, 130021, P.R. China.
² Department of Prosthodontics, Hospital of Stomatology, Jilin University, Changchun, Jilin, 130021, P.R. China.
³ Department of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, 1266 Fujin Road, Changchun, Jilin, 130021, P.R. China. jiao_ping@jlu.edu.cn.
⁴ Department of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, 1266 Fujin Road, Changchun, Jilin, 130021, P.R. China. ma_jie@jlu.edu.cn.

Abstract

Background: Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease caused by severe loss of pancreatic β cells. Immune cells are key mediators of β 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⁺ T cells. Moreover, tumor necrosis factor (TNF) was significantly associated with naive B cell and naive CD4⁺ 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: Gene expression profiles; Immune cells; Peripheral blood; Type 1 diabetes mellitus.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Area Under Curve
Diabetes Mellitus, Type 1* / genetics
Gene Regulatory Networks
Humans
ROC Curve
Transcriptome