Research Paper Volume 12, Issue 4 pp 3807—3827

Risk score based on expression of five novel genes predicts survival in soft tissue sarcoma

Hui-Yun Gu 1, , Chao Zhang 2, , Jia Guo 3, , Min Yang 1, , Hou-Cheng Zhong 1, , Wei Jin 1, , Yang Liu 2, , Li-Ping Gao 4, , Ren-Xiong Wei 1, ,

  • 1 Department of Spine and Orthopedic Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
  • 2 Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China
  • 3 Department of Plastic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
  • 4 The Third Clinical School, Hubei University of Medicine, Shiyan, China

received: November 9, 2019 ; accepted: February 4, 2020 ; published: February 21, 2020 ;
How to Cite

Copyright © 2020 Gu 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.


In this study, The Cancer Genome Atlas and Genotype-Tissue Expression databases were used to identify potential biomarkers of soft tissue sarcoma (STS) and construct a prognostic model. The model was used to calculate risk scores based on the expression of five key genes, among which MYBL2 and FBN2 were upregulated and TSPAN7, GCSH, and DDX39B were downregulated in STS patients. We also examined gene signatures associated with the key genes and evaluated the model’s clinical utility. The key genes were found to be involved in the cell cycle, DNA replication, and various cancer pathways, and gene alterations were associated with a poor prognosis. According to the prognostic model, risk scores negatively correlated with infiltration of six types of immune cells. Furthermore, age, margin status, presence of metastasis, and risk score were independent prognostic factors for STS patients. A nomogram that incorporated the risk score and other independent prognostic factors accurately predicted survival in STS patients. These findings may help to improve prognostic prediction and aid in the identification of effective treatments for STS patients.


Soft tissue sarcoma: STS; The Cancer Genome Atlas: TCGA; Genotype-Tissue Expression: GTEx; University of California Santa Cruz: UCSC; Differentially expressed genes: DEGs; Gene Ontology: GO; Kyoto Encyclopedia of Genes and Genomes: KEGG; Least absolute shrinkage and selection operator: Lasso; Overall survival: OS; Receiver operating characteristic: ROC; Gene Set Enrichment Analysis: GSEA; Concordance index: C-index.