Gene Co-Expression Network Analysis Reveals Relationship Between Leukocyte Fraction and Genomic Instability in Dedifferentiated Liposarcoma Publisher



Darzi M ; Shokrollahi Barough M ; Nazeri E ; Majidzadehardebili K ; Esmaeili R
Source: Molecular Biology Research Communications Published:2025

Abstract

Dedifferentiated Liposarcoma (DDLPS) is one of the common subtypes of liposarcoma that is considered a highly malignant category. This study aims to investigate DDLPS through a system biology approach. The gene expression profiles and clinical traits of the DDLPS were acquired from The Cancer Genome Atlas (TCGA). The identification of co-expressed modules was conducted using the weighted gene co-expression network analysis. The immune cell-related gene function was studied by a web-based tool, TIMER, and, the survival analysis was performed at both the module and single-gene levels through Cox Regression analysis. Gene enrichment analysis was also conducted using the DAVID tool. One of the nine co-expressed DDLPS modules was significantly correlated with leukocyte fraction, hyper/hypo methylation, tumor purity, and chromosome instability (CIN). Based on the biological processes used to classify genes, the hub genes in a particular module play important roles in DNA repair, microtubule organizing clusters, mitotic checkpoint dysregulation, and cell proliferation signaling pathways. After screening the genes based on intra-module connectivity, module membership, and gene significance RAD54L was selected as one of the important hub genes. RAD54L showed poor prognosis to the overall survival (OS) analysis (HR=1.6, 95% CI=1.1–2.4, p=0.02). No co-expressed modules had relationship with OS. Through DDLPS traits, CIN and hyper/hypo methylation had significant negative relationship with OS. Our achievement confirmed the inverse association between tumor purity for DDLPS gene profiles and leukocyte fraction and negative leukocyte fraction (LF) gene significance in some genes was justified according to the sub-population analyses of immune cells in TIMER. © 2025 Elsevier B.V., All rights reserved.