Abstract

The aim of this study was to construct a new immune-associated long non-coding RNA (lncRNA) signature to predict the prognosis of Ewing sarcoma (ES) and explore its molecular mechanisms. We downloaded transcriptome and clinical prognosis data from the Gene Expression Omnibus (GSE17679, which included 88 ES samples and 18 matched normal skeletal muscle samples), and used it as a training set to identify immune-related lncRNAs with different expression levels in ES. Univariable Cox regression was used to screen immune-related lncRNAs related to ES prognosis, and an immune-related lncRNA signature was constructed based on machine learning iterative lasso regression. An external verification set was used to confirm the predictive ability of the signature. Clinical feature subgroup analysis was used to explore whether the signature was an independent prognostic factor. In addition, CIBERSORT was used to explore immune cell infiltration in the high- and low-risk groups, and to analyze the correlations between the lncRNA signature and immune cell levels. Gene set enrichment and variation analyses were used to explore the possible regulatory mechanisms of the immune-related lncRNAs in ES. We also analyzed the expression of 17 common immunotherapy targets in the high- and low-risk groups to identify any that may be regulated by immune-related lncRNAs. We screened 35 immune-related lncRNAs by univariate Cox regression. Based on this, an immune-related 11-lncRNA signature was generated by machine learning iterative lasso regression. Analysis of the external validation set confirmed its high predictive ability. DPP10 antisense RNA 3 was negatively correlated with resting dendritic cell, neutrophil, and γδ T cell infiltration, and long intergenic non-protein coding RNA 1398 was positively correlated with resting dendritic cells and M2 macrophages. These lncRNAs may affect ES prognosis by regulating GSE17721_CTRL_VS_PAM3CSK4_12H_BMDC_UP, GSE2770_IL4_ACT_VS_ACT_CD4_TCELL_48H_UP, GSE29615_CTRL_VS_DAY3_ LAIV_IFLU_VACCINE_PBMC_UP, complement signaling, interleukin 2-signal transducer and activator of transcription 5 signaling, and protein secretion. The immune-related 11-lncRNA signature may also have regulatory effects on the immunotherapy targets CD40 molecule, CD70 molecule, and CD276 molecule. In conclusion, we constructed a new immune-related 11-lncRNA signature that can stratify the prognoses of patients with ES.

Highlights

  • Ewing sarcoma (ES) is one of the most common malignant tumors in children, young adults, and adults (Grünewald et al, 2018)

  • We have identified lncRNAs strongly related to ES prognosis and used machine learning iterative lasso regression to generate and validate an immune-related 11-lncRNA signature that can predict ES prognosis

  • We obtained 262 immunerelated lncRNAs by differential expression analysis of the high and low immune infiltration groups, and 884 differentially expressed lncRNAs in the 88 ES samples compared to the 18 matched healthy skeletal muscle samples

Read more

Summary

Introduction

Ewing sarcoma (ES) is one of the most common malignant tumors in children, young adults, and adults (Grünewald et al, 2018). ES prognosis is closely related to immune factors. CD8+ T cells can kill ES cells by recognizing the ET-derived antigens enhancer of zeste 2 polycomb repressive complex 2 subunit 666 and chondromodulin 319 (Thiel et al, 2011; Blaeschke et al, 2016). Natural killer (NK) cells do not recognize specific tumor antigens to cause an immune response, but exert a direct killing effect on ES cells. Studies have shown that allogeneic transplantation of NK cells has a more pronounced killing effect on tumors than autologous NK cells (Ljunggren and Malmberg, 2007; Verhoeven et al, 2008). Studies have shown that interleukin (IL)-6, IL-10, and killer cell lectin like receptor K1 regulate the ES tumor microenvironment and are closely related to its prognosis (Hempel et al, 1997; Berghuis et al, 2012; Lissat et al, 2015). Immune-related prediction signatures may provide accurate guidance for ES treatment

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call