乳癌および甲状腺癌における共通の病原性および予後予測バイオマーカーを明らかにする統合単一細胞およびバルクトランスクリプトーム解析

Breast cancer (BC) and thyroid cancer (TC) are two hormonally regulated malignancies with increasing evidence of significant comorbidity. However, the underlying molecular mechanisms contributing to their co-occurrence remain unclear.

This study aimed to elucidate the shared pathogenesis of BC and TC and to identify common prognostic biomarkers and therapeutic targets.

An integrative bioinformatics study combining single-cell and bulk RNA sequencing data was conducted to investigate shared molecular features between BC and TC.

Differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. Single-cell transcriptome analysis was performed to characterize tumor microenvironment composition and malignant cell heterogeneity. Copy number variation (CNV) and non-negative matrix factorization (NMF) analyses were used to identify key gene expression modules. Weighted gene co-expression network analysis (WGCNA) was applied to bulk transcriptomic data to determine critical cell populations. A prognostic signature was constructed using 101 machine learning algorithms, and functional assays were conducted to validate gene function.

Enrichment analyses indicated that the JAK-STAT signaling pathway and cytokine-cytokine receptor interaction are shared pathogenic mechanisms. Single-cell analysis revealed immune cell involvement and malignant cell heterogeneity. Modules MP2, MP4, and MP5 were identified as critical in both cancers. WGCNA highlighted SFRP2+ fibroblasts and HLA_DPB1+ myeloid cells as key players in tumorigenesis. A prognostic model was developed, and SMR3B was validated as a shared prognostic gene that influenced proliferation, migration, and invasion in both BC and TC.

This study provides comprehensive insights into the shared molecular mechanisms of BC and TC and identifies SMR3B as a promising prognostic biomarker and therapeutic target, offering new avenues for managing patients at dual risk.