Psoriasis has been associated with an increased risk of various cancers, including thyroid cancer (TC), yet the molecular mechanisms linking these two diseases remain unclear.
Objective
This study aimed to identify and analyze the differentially expressed genes (DEGs) between TC and psoriasis using bioinformatics approaches to explore potential molecular mechanisms and shared pathways. To the best of our knowledge, this is the first bioinformatics-based study to systematically identify and validate shared hub genes between thyroid cancer and psoriasis.
Methods
A TC dataset from the TCGA database and five GEO datasets (GSE35570, GSE13355, GSE14905, GSE53431, and GSE29265) were analyzed, with GSE53431 and GSE29265 serving as validation sets. Differential expression was identified using Xiantao and GEO2R, followed by a series of bioinformatics analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) enrichment, protein-protein interaction (PPI) network construction, transcription factor (TF)-gene interaction, TF-miRNA coregulatory network analysis, and drug molecule prediction.
Results
A total of 79 DEGs associated with TC were identified. Key Enrichr KEGG pathways included a response to the bacterium, NABA MATRISOME ASSOCIATED, negative regulation of cell population proliferation, response to wounding, and HALLMARK KRAS SIGNALING UP. Six hub genes (SERPINA1, S100A9, CCL20, SLPI, LCN2, and CXCL8) were identified from the PPI network, with three genes (SERPINA1, CCL20, and LCN2) showing high diagnostic value for both TC and psoriasis. TF gene and miRNA interactions involving these hub genes and potential drug molecules were also identified.
Conclusion
This study provides insight into potential biomarkers and therapeutic targets relevant to TC and psoriasis, identifying shared molecular pathways and hub genes that may guide future diagnostic and therapeutic approaches for these diseases.
