Explore the possible influence of Sjogren's syndrome on thyroid cancer: A literature data mining and meta-analysis

Abstract

Objectives: To explore the potential influence of Sjogren's syndrome (SS) on thyroid cancer (TC).

Methods: First, a literature data mining (LDM) approach was used to reconstruct functional pathways connecting SS and TC. A meta-analysis was then performed to examine the expression changes of genes mediated by SS using 16 TC case/control expression datasets, with results validated through the TCGA/GTEx dataset. Finally, gene set enrichment analysis (GSEA) and survival analysis using GEPIA2 were conducted on the significant genes.

Results: Our findings indicate that SS may increase the risk of TC by activating 14 TC promoters (PDCD1, NTRK1, LGALS3, CD274, FOXP3, BCL2, CYP1A1, HMGB1, TGFB1, CCL2, PLA2G7, TFF3, LCN2, and CLDN1) and suppressing three TC inhibitors (MIR145, MIR30C1, and EP300). Four molecules (PLA2G7, TFF3, LCN2, and CLDN1) exhibited significant expression changes in TC patients (LFC > 1 or < -1; p < 2.07E-04), which were confirmed in TCGA/GTEx expression analysis. These results highlight three possible mechanisms-the SS-PLA2G7-CCL2-TC pathway, the SS-LCN2-LGALS3-TC pathway, and the SS-CLDN1-BCL2-TC pathway-that may explain how SS contributes to TC development. Enrichment analysis suggests that SS may affect TC prognosis by regulating leukocytes and tolerance induction. Survival analysis indicates that SS may enhance TC survival through the regulation of the CLDN1 and EGF pathways.

Conclusion: LDM-based pathway analysis highlighted three genetic pathways through which SS may adversely affect TC progression, while SS may enhance TC survival via the CLDN1 and EGF pathways, highlighting the need for further research.

Fig 1. The Sjogren’s syndrome-driven, literature-based molecular pathway influencing thyroid cancer.

Edges in red represent a negative relationship, while those in green represent a positive relationship. A gene in red indicates that SS exerts a detrimental effect on TC through the gene, whereas a gene in green indicates that SS exerts a beneficial effect on TC through the gene.

Fig 2. Expression and survival analysis of the 13 Sjogren’s syndrome-driven genes using THCA dataset.

(a) Gene expression of the involved genes using the THCA dataset; (b) Survival map of these genes in thyroid carcinoma. Red indicates that higher expression levels are associated with increased risk (unfavorable prognosis), while blue indicates that higher expression levels are associated with reduced risk (favorable prognosis).

Fig 3. Volcano plot of the meta-analysis results of 274 Sjogren’s syndrome-driven molecules using 16 thyroid cancer RNA expression datasets.

Fig 4. Genes show significant expression change in meta-analysis and their connection with Sjogren’s syndrome and thyroid cancer.

Edges in red represent a negative relationship, while those in green represent a positive relationship. A gene in red indicates significantly increased expression in the meta-analysis, whereas a gene in blue indicates significantly decreased expression in the meta-analysis.

Fig 5. Expression and survival analysis of meta-analysis-significant genes in the THCA dataset.

(a) Gene expression of the involved genes using the THCA dataset; (b) Survival map of these genes in thyroid carcinoma. Red indicates that higher expression levels are associated with increased risk (unfavorable prognosis), while blue indicates that higher expression levels are associated with reduced risk (favorable prognosis).

Fig 6. Enrichment results using the 21 SS-driven genes influencing the pathology of TC.

(a) The top 10 GO terms enriched; (b) a co-occurrence index map of the genes enriched within the top 10 pathways; the numbers indicate the number of shared pathways between any two genes.