Integrative transcriptomics and single-cell transcriptomics analyses reveal potential biomarkers and mechanisms of action in papillary thyroid carcinoma.

Abstract

Objective: Papillary thyroid carcinoma (PTC) has a high recurrence rate and lacks reliable diagnostic biomarkers. This study aims to identify robust transcriptomic biomarkers for PTC diagnosis through integrative bioinformatics approaches and elucidate the cellular mechanisms underlying PTC pathogenesis at single-cell resolution.

Methods: Based on the Gene Expression Omnibus (GEO) database, we downloaded PTC-related RNA-seq datasets (GSE3467, GSE3678, GSE33630, GSE65144, and GSE82208) and an scRNA-seq dataset (GSE191288). Among these, the RNA-seq dataset GSE3467 was used as the training dataset to perform differential gene expression analysis, GO and KEGG enrichment analyses, weighted gene co-expression network analysis (WGCNA), machine learning, ROC analysis, nomogram analysis, and GSEA for mining potential biomarkers. The remaining RNA-seq datasets (GSE3678, GSE33630, GSE65144, and GSE82208) were used as the validation datasets to validate these potential biomarkers. Based on the results from potential biomarker mining, the scRNA-seq dataset (GSE191288) was used to analyze and uncover key cell types and their mechanisms involved in the occurrence and development of PTC.

Results: This study retrieved relevant PTC datasets from the GEO database and identified three biomarkers (ENTPD1, SERPINA1, and TACSTD2) through a series of bioinformatics analyses. GSEA suggested that these biomarkers may be involved in the occurrence and development of PTC by collectively regulating the cytokine-cytokine receptor interaction pathways. scRNA-seq analysis revealed tissue stem cells, epithelial cells, and smooth muscle cells as key cell types in PTC. Cell-cell communication analysis revealed that epithelial cells primarily interact with tissue stem cells and smooth muscle cells through two ligand-receptor pairs, namely, COL4A1-CD4 and COL4A2-CD4. The collagen signaling pathway was identified as the most dominant pathway, and violin plots demonstrated that ligands COL4A1 and COL4A2 were highly expressed in epithelial cells, while the receptor CD4 showed elevated expression in both tissue stem cells and smooth muscle cells. Pseudotime analysis demonstrated that these three cell types underwent three distinct differentiation stages, during which the expression levels of the biomarkers ENTPD1, SERPINA1, and TACSTD2 showed stage-specific trends.

Conclusion: In summary, this study combines RNA-seq and scRNA-seq analysis techniques to identify ENTPD1, SERPINA1, and TACSTD2 as potential biomarkers for PTC at the transcriptomic level and tissue stem cells, epithelial cells, and smooth muscle cells as key cells in PTC at the cellular level. This study conducted in-depth research and analysis on these potential biomarkers and key cells, providing new research foundations and insights for future basic experimental research and the diagnosis and treatment of PTC in clinical settings.