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Journal of Cancer Research and Clinical Oncology

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23.09.2020 | Original Article – Cancer Research

An immune-related gene signature for determining Ewing sarcoma prognosis based on machine learning

verfasst von: En-hui Ren, Ya-jun Deng, Wen-hua Yuan, Zuo-long Wu, Guang-zhi Zhang, Qi-qi Xie

Erschienen in: Journal of Cancer Research and Clinical Oncology | Ausgabe 1/2021

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Abstract

Purpose

Ewing sarcoma (ES) is one of the most common malignant bone tumors in children and adolescents. The immune microenvironment plays an important role in the development of ES. Here, we developed an optimal signature for determining ES patient prognosis based on immune-related genes (IRGs).

Methods

We analyzed the ES gene expression profile dataset, GSE17679, from the GEO database and extracted differential expressed IRGs (DEIRGs). Then, we conducted functional correlation and protein–protein interaction (PPI) analyses of the DEIRGs and used the machine learning algorithm-iterative Lasso Cox regression analysis to build an optimal DEIRG signature. In addition, we applied ES samples from the ICGC database to test the optimal gene signature. We performed univariate and multivariate Cox regressions on clinicopathological characteristics and optimal gene signature to evaluate whether signature is an important prognostic factor. Finally, we calculated the infiltration of 24 immune cells in ES using the ssGSEA algorithm, and analyzed the correlation between the DEIRGs in the optimal gene signature and immune cells.

Results

A total of 249 DEIRGs were screened and an 11-gene signature with the strongest correlation with patient prognoses was analyzed using a machine learning algorithm. The 11-gene signature also had a high prognostic value in the ES external verification set. Univariate and multivariate Cox regression analyses showed that 11-gene signature is an independent prognostic factor. We found that macrophages and cytotoxic, CD8 T, NK, mast, B, NK CD56bright, TEM, TCM, and Th2 cells were significantly related to patient prognoses; the infiltration of cytotoxic and CD8 T cells in ES was significantly different. By correlating prognostic biomarkers with immune cell infiltration, we found that FABP4 and macrophages, and NDRG1 and Th2 cells had the strongest correlation.

Conclusion

Overall, the IRG-related 11-gene signature can be used as a reliable ES prognostic biomarker and can provide guidance for personalized ES therapy.

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Titel: An immune-related gene signature for determining Ewing sarcoma prognosis based on machine learning
verfasst von: En-hui Ren
Ya-jun Deng
Wen-hua Yuan
Zuo-long Wu
Guang-zhi Zhang
Qi-qi Xie
Publikationsdatum: 23.09.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Cancer Research and Clinical Oncology / Ausgabe 1/2021
Print ISSN: 0171-5216
Elektronische ISSN: 1432-1335
DOI: https://doi.org/10.1007/s00432-020-03396-3

Springer Medizin

An immune-related gene signature for determining Ewing sarcoma prognosis based on machine learning

Abstract

Purpose

Methods

Results

Conclusion

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Springer Medizin

Abstract

Purpose

Methods

Results

Conclusion

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