Integrated single-cell and bulk RNA-seq analysis reveals prognostic stemness genes in leiomyosarcoma.

ABSTRACT:

Introduction

Leiomyosarcoma (LMS) is a rare and aggressive soft tissue sarcoma with limited therapeutic options and poor prognosis. Identifying reliable prognostic markers and therapeutic targets is critical for improving personalized treatment strategies.

Methods

We integrated single-cell RNA sequencing (scRNA-seq) data of LMS from the Gene Expression Omnibus (GEO) with bulk RNA-seq and clinical data from The Cancer Genome Atlas (TCGA). Malignant cells were identified using machine learning approaches, and their stemness index was calculated. Cells were stratified into high and low stemness index groups, and differential gene expression analysis was performed. Prognostic markers were identified through a sequential pipeline: univariate Cox regression to screen candidate gen

SUBMITTER: Lou A 

PROVIDER: S-EPMC12436121 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature