Research Paper Volume 15, Issue 20 pp 11412—11447

Molecular characterization of ferroptosis in soft tissue sarcoma constructs a prognostic and immunotherapeutic signature through experimental and bioinformatics analyses

Zhi-Qiang Yang1, *, , Liang-Yu Guo1, *, , Kang-Wen Xiao1,3, *, , Chong Zhang2, , Min-Hao Wu1, , Fei-Fei Yan1, , Lin Cai1, ,

  • 1 Department of Orthopedics, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, People’s Republic of China
  • 2 Department of Orthopedics, Renmin Hospital of Wuhan University, Wuhan, Hubei 430071, People’s Republic of China
  • 3 School of Medicine, Washington University, St. Louis, MO 63110, USA
* Equal contribution

Received: June 12, 2023       Accepted: October 2, 2023       Published: October 20, 2023      

https://doi.org/10.18632/aging.205133
How to Cite

Copyright: © 2023 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Ferroptosis regulators have been found to affect tumor progression. However, studies focusing on ferroptosis and soft tissue sarcoma (STS) are rare. Somatic mutation, copy number variation, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis, consensus clustering, differentially expressed genes analysis (DEGs), principal component analysis (PCA) and gene set enrichment analysis (GSEA) were used to identify and explore different ferroptosis modifications in STS. A nomogram was constructed to predict the prognosis of STS. Moreover, three immunotherapy datasets were used to assess the Fescore. Western blotting, siRNA transfection, EdU assay and reactive oxygen species (ROS) measurement were performed. 16 prognostic ferroptosis regulators were screened and significant differences were observed in somatic mutation, copy number variation (CNV) and RT-qPCR among these ferroptosis regulators. 2 different ferroptosis modification patterns were found (Fe cluster A and B). Fe cluster A with higher Fescore was correlated with p53 pathway and had better prognosis of STS (p = 0.002) while Fe cluster B with lower Fescore was correlated with angiogenesis and MYC pathway and showed a poorer outcome. Besides, the nomogram effectively predicted the outcome of STS and the Fescore could also well predict the prognosis of other 16 tumors and immunotherapy response. Downregulation of LOX also inhibited growth and increased ROS production in sarcoma cells. The molecular characterization of ferroptosis regulators in STS was explored and an Fescore was constructed. The Fescore quantified ferroptosis modification in STS patients and effectively predicted the prognosis of a variety of tumors, providing novel insights for precision medicine.

Abbreviations

STS: Soft tissue sarcoma; CNV: Copy number variation; PCA: Principal component analysis; GSEA: Gene set enrichment analysis; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus; FPKM: Fragments Per Kilobase Million; DEGs: Differentially expressed genes; cMap: Connectivity map; HR: Hazard ratio; ROC: Receiver operating characteristic; GEO: Gene expression omnibus; TME: Tumor microenvironment; ES: Enrichment score; AUC: Area under curve; GO: Gene ontology; FDR: False discovery rate; ROS: Reactive oxygen species.