Integrative analysis of key candidate genes and signaling pathways in autoimmune thyroid dysfunction related to anti-CTLA-4 therapy by bioinformatics

Abstract

Cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), the first immune checkpoint to be targeted clinically, has provided an effective treatment option for various malignancies. However, the clinical advantages associated with CTLA-4 inhibitors can be offset by the potentially severe immune-related adverse events (IRAEs), including autoimmune thyroid dysfunction. To investigate the candidate genes and signaling pathways involving in autoimmune thyroid dysfunction related to anti-CTLA-4 therapy, integrated differentially expressed genes (DEGs) were extracted from the intersection of genes from Gene Expression Omnibus (GEO) datasets and text mining. The functional enrichment was performed by gene ontology (GO) annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis. Protein-protein interaction (PPI) network, module enrichment, and hub gene identification were constructed and visualized by the online Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. A total of 22 and 17 integrated human DEGs in hypothyroidism and hyperthyroidism group related to anti-CTLA-4 therapy were identified, respectively. Functional enrichment analysis revealed 24 GO terms and 1 KEGG pathways in the hypothyroid group and 21 GO terms and 2 KEGG pathways in the hyperthyroid group. After PPI network construction, the top five hub genes associated with hypothyroidism were extracted, including ALB, MAPK1, SPP1, PPARG, and MIF, whereas those associated with hyperthyroidism were ALB, FCGR2B, CD44, LCN2, and CD74. The identification of the candidate key genes and enriched signaling pathways provides potential biomarkers for autoimmune thyroid dysfunction related to anti-CTLA-4 therapy and might contribute to the future diagnosis and management of IRAEs for cancer patients.

Keywords: Autoimmune thyroid dysfunction; CTLA-4; Differentially expressed genes; Immune checkpoint blockade; Signaling pathway.

Fig. 1

Normalization of gene expression profile matrix. a, b Before and after normalization of the hypothyroidism GSE32445 dataset. c, d Before and after normalization of the hyperthyroidism GSE58062 dataset

Fig. 2

Differentially expressed genes between hypothyroid/hyperthyroid and control groups. a, b Volcano plot and cluster heat map of the top 20 differentially expressed genes from GSE32445. c, d Volcano plot and cluster heat map of the top 20 differentially expressed genes from GSE58062. Red represents the upregulated genes based on |log2FC|>1 and P value < 0.05 and green represents the downregulated genes based on the same statistical requirements

Fig. 3

Venn diagram of DEGs from microarray data and genes list from text mining. a Intersection of genes between DEGs generated from GSE32445 and anti-CTLA-4 gene list from text mining. b Intersection of genes between DEGs generated from GSE58062 and anti-CTLA-4 gene list from text mining. DEGs, differentially expressed genes

Fig. 4

GO analysis of common genes associated with hyperthyroidism and anti-CTLA-4 therapy. a biological process. b cell component. c molecular function

Fig. 5

GO analysis of common genes associated with hyperthyroidism and anti-CTLA-4 therapy. a biological process. b cell component. c molecular function

Fig. 6

KEGG pathway enrichment of the integrated DEGs associated with autoimmune thyroid dysfunction and anti-CTLA-4 therapy. a hypothyroidism. b hyperthyroidism. DEGs, differentially expressed genes

Fig. 7

PPI network and highly connected modules of integrated genes. a, b PPI network in hypothyroidism and hyperthyroidism group. c, d Modules from the PPI network in hypothyroidism and hyperthyroidism group generatedby the MCODE algorithm in Cytoscape. Red indicates relative upregulated genes and green indicates relative downregulated genes. The size of the edges represents the strength of the interactions based on the combined score