Comprehensive RNA Sequencing Analysis Identifies Network Hub Genes and Biomarkers Differentiating Desmoid-type Fibromatosis From Reactive Fibrosis.

Abstract

Desmoid-type fibromatosis (DTF) is a benign but locally aggressive neoplasm characterized by persistent fibroblast activation, unlike reactive fibrosis (RF), where fibroblast activation is transient. Although the Wnt/β-catenin signaling pathway is known to play a role in DTF pathogenesis, the specific genetic drivers contributing to this abnormal fibroblast activation are not fully understood. To identify additional driver genes that underlie the persistent activation of fibroblasts in DTF, we conducted a comparative transcriptome analysis between 29 DTF and 14 RF tissue samples, identifying 4267 differentially expressed genes (DEGs) specific to DTF. These DTF-specific DEGs were significantly associated with pathways involved in embryonic limb morphogenesis and muscle contraction, whereas RF-specific DEGs were linked to immune response and apoptosis. Using weighted gene coexpression network analysis to further elucidate the key regulatory circuits associated with persistent activation of DTF fibroblasts, we identified a highly DTF-specific gene module comprising 120 genes. This module was also significantly enriched in other fibroproliferative conditions showing persistent fibroblast activation, such as keloid disease and idiopathic pulmonary fibrosis. Subsequent analyses identified 7 driver transcription factors (ZNF536, IRX5, TWIST2, NKD2, PAX9, SHOX2, and SALL4) within this DTF-specific module that may contribute to the sustained activation of DTF fibroblasts. We further assessed the utility of 5 key genes from this module (TWIST2, LRRC15, CTHRC1, SHOX2, and SALL4) as potential biomarkers to distinguish DTF from RF using immunohistochemistry. All markers demonstrated excellent diagnostic performance, with TWIST2 showing exceptionally high sensitivity and specificity, surpassing β-catenin, the current standard biomarker for DTF. In conclusion, our study identifies gene modules and driver transcription factors that are highly specific to DTF, offering new insights into the genetic underpinnings of abnormal fibroblast activation in DTF. We also propose novel biomarkers that could improve the diagnostic accuracy and clinical management of DTF.