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

• Published in: Aging (Albany, NY.) Vol. 12; no. 22; pp. 22906 - 22926
• Main Authors: Li, Long-Qing, Zhang, Liang-Hao, Zhang, Yan, Lu, Xin-Chang, Zhang, Yi, Liu, Yong-Kui, Khader, Manhas Abdul, Jia-Wen, Tao-Liu, Li, Jia-Zhen
• Format: Journal Article
• Language: English
• Published: United States Impact Journals 16.11.2020
• Subjects: Adolescent; Adult; Antineoplastic Agents, Immunological - therapeutic use; Biomarkers, Tumor - genetics; Bone Neoplasms - genetics; Bone Neoplasms - immunology; Bone Neoplasms - mortality; Bone Neoplasms - therapy; Clinical Decision-Making; Databases, Genetic; Female; Gene Expression Profiling; Humans; Immune Checkpoint Inhibitors - therapeutic use; Immunotherapy; Male; Nomograms; Osteosarcoma - genetics; Osteosarcoma - immunology; Osteosarcoma - mortality; Osteosarcoma - therapy; Predictive Value of Tests; Research Paper; Retrospective Studies; Risk Assessment; Risk Factors; RNA-Seq; Transcriptome; Tumor Microenvironment; Young Adult; tumor immunology; immune related gene; prognosis; TCGA; osteosarcoma
• ISSN: 1945-4589; 1945-4589
• DOI: 10.18632/aging.104017

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.