Characterizing cancer phenotypes and their tumor microenvironment across malignant progression using single-cell RNA sequencing data

Abstract

Sarcoma is a rare group of cancers originating from mesenchymal lineages, with high metastatic potential and poor clinical outcomes. Previous studies have linked collagen and extracellular matrix (ECM) remodeling to sarcoma invasion, metastasis, and immune evasion. However, with bulk RNA-seq datasets, it remains unclear whether these signals come from tumor cells themselves or from the tumor microenvironment (TME). In this study, I designed and implemented a single-cell RNA sequencing (scRNA-seq) analysis pipeline to address this question, utilizing publicly available datasets from three sarcoma subtypes: undifferentiated pleomorphic sarcoma (UPS), leiomyosarcoma (LMS), and dedifferentiated liposarcoma (DDLPS). The designed pipeline identifies malignant cells without relying on marker genes by using an iterative inferCNV reference refinement strategy. The pipeline achieved good annotation concordance with original study labels in UPS/LMS samples, with reduced concordance in liposarcoma samples, potentially attributable to the focal copy number architecture characteristic of DDLPS and substantial inter-patient transcriptional heterogeneity. Downstream differential gene expression analysis showed that major fibrillar collagen genes, including COL1A1, COL1A2, and COL3A1, were upregulated in malignant cells compared with non-malignant cells across all analyzed datasets, while other ECM-related genes and collagen subtypes followed subtype- or patient-specific patterns. Collagen biosynthesis and ECM assembly pathways were upregulated in malignant cells, while Hallmark oncogenic pathways showed patient-specific patterns. In conclusion, these results support tumor-intrinsic collagen expression as a consistent feature of sarcoma malignant cell states. The study may have important implications for malignant progression, immune evasion, and the development of matrix-targeted therapeutic strategies.