Research Paper Advance Articles

Construction of immune-related gene pairs signature to predict the overall survival of osteosarcoma patients

Long-Qing Li1, *, , Liang-Hao Zhang2, *, , Yan Zhang1, , Xin-Chang Lu1, , Yi Zhang1, , Yong-Kui Liu1, , Manhas Abdul Khader1, , Jia-Wen1, , Tao-Liu1, , Jia-Zhen Li1, ,

  • 1 Department of Orthopaedic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, PR China
  • 2 Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, PR China
* Equal contribution

Received: May 6, 2020       Accepted: August 19, 2020       Published: November 16, 2020
How to Cite

Copyright: © 2020 Li 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.


The purpose of this study is to establish the prognosis of osteosarcoma patients based on the characteristics of immune-related gene pairs. We used the lasso Cox regression model to construct and verify the signature consisting of 14 immune-related gene pairs. This signature can accurately predict the overall survival of osteosarcoma patients and is an independent prognostic factor for osteosarcoma patients. For this we constructed a signature-based nomogram. The results of the nomogram show that our signature can bring clinical net benefits. We then assessed the abundance of infiltrating immune cells in each sample, and combine the results of the gene set enrichment analysis of a single sample to explore the differences in the immune microenvironment between IRPG signature groups. The result of gene set enrichment analysis shows the strong relationship between signature and immune system. Finally, we evaluated the relationship between signature and immunotherapy efficiency using algorithms such as TIMI and SubMap to explore patients who might benefit from immunotherapy. In conclusion, our signature can predict the overall survival rate of osteosarcoma patients and provide potential guidance for exploring patients who may benefit from immunotherapy.


OS: osteosarcoma; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus; TARGET: Therapeutically Applicable Research To Generate Effective Treatments; MAD: Median absolute deviation; MCP-counter: microenvironment cell population count; CIBERSORT: Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts; ROC: receiver operating characteristic; AUC: area under the curve; GSEA: gene set enrichment analysis; TIL: Tumor infiltrating lymphocytes; PD-L1: Programmed Cell Death-Ligand 1.