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A signature of 29 immune-related genes pairs to predict prognosis in patients with neuroblastoma.

Abstract：

BACKGROUND:Neuroblastoma (NB) is one of the most common childhood tumors that is associated with a poor prognosis. Recently, immunotherapy has been recognized as an effective strategy in the treatment of NB patients. In this study, an immune-related gene pair (IRGP) signature was established for predicting the prognosis of NB patients. METHODS:The Treehouse Childhood Cancer Initiative dataset and the Gene Expression Omnibus (GEO) were included in this study, as the training set and testing set, respectively. Immune-related genes retrieved from the ImmPort database were used to construct the prognostic model. Least absolute shrinkage and selection operator (LASSO) regression was used to develop the prognostic signature. Kaplan-Meier survival curves and the log-rank test were used to compare the differences between high and low immune risk scores groups. Immune infiltration analysis was estimated using TIMER, quanTIseq, and MCP-counter algorithms. The Tumor ImmunoPhenotype (TIP) pipeline was used for cancer-immunity cycle studies. RESULTS:We identified an IRGP model that was significantly correlated with survival rates and the immune risk score was calculated for each sample. The low immune risk group had significantly better prognostic outcome compared with the high immune risk group. Besides, age, MYCN status, histology, Children's Oncology Group (COG) risk, mitosis-karyorrhexis index (MKI), and immune risk scores were found to be independent prognostic factors. Moreover, the low immune risk group showed a negative correlation with the immune cell infiltration, which may activate the anti-cancer immune response. CONCLUSIONS:This prognostic immune signature based on IRGP reflects the link between the NB patient outcome and immune infiltration, and provides new insights into the prediction of prognosis in NB.

journal_name

Int Immunopharmacol

journal_title

International immunopharmacology

authors

Song J,Zhao Q,Xu Y,Zhu L

doi

10.1016/j.intimp.2020.106994

subject

Has Abstract

pub_date

2020-11-01 00:00:00

pages

106994

eissn

1567-5769

issn

1878-1705

pii

S1567-5769(20)31777-X

journal_volume

88

pub_type

杂志文章

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