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

WanChen CaoKai GaoYi Zhao^*

• Beijing University of Chinese Medicine, Beijing, China

Based on the GEO database, we downloaded PTC-related RNA-seq datasets (G SE3467, GSE3678, GSE33630, GSE65144, GSE82208) and scRNA-seq data (GSE1 91288). Among them, the RNA-seq dataset (GSE3467) was used as the training dataset to perform differential gene expression analysis, as well as GO and KEG G enrichment analysis, weighted gene co-expression network analysis (WGCNA), machine learning, ROC analysis, Nomogram analysis, and GSEA analysis for mi ning potential biomarkers. The other RNA-seq datasets (GSE3678, GSE33630, GS E65144, GSE82208) were used as the validation datasets to validate these potential biomarkers. Based on the results of the potential biomarker mining, the scRNAseq data (GSE191288) was used to analyze and uncover key cells and their me chanisms involved in the occurrence and development of PTC.This study retrieved relevant papillary thyroid carcinoma (PTC) datasets from the Gene Expression Omnibus (GEO) database and identified three biomarkers (ENTPD1, SERPINA1, and TACSTD2) through a series of bioinformatics analyses. GSEA analysis 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 communication analysis revealed that epithelial cells primarily interact with tissue stem cells and smooth muscle cells through two ligand-receptor pairs: COL4A1-CD44 and COL4A2-CD44. 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 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. Conclusions In summary, this study combines RNA-seq analysis technique and scRNA-seq analysis technique 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.