Prognostic implications of immune-related eight-gene signature in pediatric brain tumors

Wang, Yi; Zhou, Chuan; Luo, Huan; Cao, Jing; Ma, Chi; Cheng, Lulu; Yang, Yang

doi:10.1590/1414-431x2020e10612

Cited by 8 publications

(9 citation statements)

References 33 publications

(44 reference statements)

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“…Then, Kaplan–Meier plotter, Cox and ROC analyses confirmed the reliable and superior prognostic ability of GESBN models. AUC can be used to assess the accuracy and predictive capacity of biomarkers in diagnostic tests [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

Nomogram Models Based on the Gene Expression in Prediction of Breast Cancer Bone Metastasis

Fan

Bei

2022

Journal of Healthcare Engineering

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Objective. The aim of this study is to design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients. Methods. Dataset GSE124647 was used as a training set, while GSE16446, GSE45255, and GSE14020 were taken as validation sets. In the training cohort, the limma package in R was adopted to obtain differentially expressed genes (DEGs) between BC nonbone metastasis and bone metastasis patients, which were used for functional enrichment analysis. After weighted co-expression network analysis (WGCNA), univariate Cox regression and Kaplan–Meier plotter analyses were performed to screen potential prognosis-related genes. Then, GESBN models were constructed and evaluated. The prognostic value of the GESBN models was investigated in the GSE124647 dataset, which was validated in GSE16446 and GSE45255 datasets. Further, the expression levels of genes in the models were explored in the training set, which was validated in GSE14020. Finally, the expression and prognostic value of hub genes in BC were explored. Results. A total of 1858 DEGs were obtained. The WGCNA result showed that the blue module was most significantly related to bone metastasis and prognosis. After survival analyses, GAJ1, SLC24A3, ITGBL1, and SLC44A1 were subjected to construct a GESBN model for overall survival (OS). While GJA1, IGFBP6, MDFI, TGFBI, ANXA2, and SLC24A3 were subjected to build a GESBN model for progression-free survival (PFS). Kaplan–Meier plotter and receiver operating characteristic analyses presented the reliable prediction ability of the models. Cox regression analysis further revealed that GESBN models were independent prognostic predictors for OS and PFS in BC patients. Besides, GJA1, IGFBP6, ITGBL1, SLC44A1, and TGFBI expressions were significantly different between the two groups in GSE124647 and GSE14020. The hub genes had a significant impact on patient prognosis. Conclusion. Both the four-gene signature and six-gene signature could accurately predict patient prognosis, which may provide novel treatment insights for BC bone metastasis.

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Section: Discussionmentioning

confidence: 99%

Nomogram Models Based on the Gene Expression in Prediction of Breast Cancer Bone Metastasis

Fan

Bei

2022

Journal of Healthcare Engineering

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“…Wang Y et al. found that C15ORF42 is closely related to the prognosis of pediatric brain tumors, and may affect the prognosis of children by changing the tumor microenvironment ( 60 ). At present, there are no studies on the effects of C15ORF42 in the microenvironment of children with NB, and no researches on the correlation between MYCN and C15ORF42 .…”

Section: Discussionmentioning

confidence: 99%

Identification of hub genes and their correlation with infiltration of immune cells in MYCN positive neuroblastoma based on WGCNA and LASSO algorithm

Chen

Sun²,

Chen³

et al. 2022

Front. Immunol.

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BackgroundThe prognosis of MYCN positive NB is poor, and there is no targeted drug for N-myc at present. This study aims to screen out hub genes closely related to MYCN, analyze the relationship between hub genes and NB microenvironment, and provide basis for molecular targeted therapy of MYCN positive NB.MethodsWe combined the microarray data of GSE45547 (n=649) and GSE49710 (n=498), screened the DEGs between MYCN positive (n=185) and MYCN negative NB (n=951), performed WGCNA, Lasso regression and Roc analyses on the merged matrix, and obtained the hub genes related to MYCN in the training group. We performed ssGSEA on the experimental group to calculate the infiltration level of 28 kinds of immune cells in each sample, compared the differences of immune cell infiltration between MYCN positive and MYCN negative group. The influences of hub genes on the distribution of each immune cell were also analyzed by ssGSEA. The expression differences of the three hub genes were verified in the E-MTAB-8248 cohort (n=223), and the correlation between hub genes and prognosis of NB was calculated by Kaplan-Meier method in GSE62564 (n=498) and the validation group. We also verified the expression differences of hub genes by qRT-PCR in SK-N-BE(2), SKNDZ, Kelly and SH-SY5Y cell lines.ResultsHere were 880 DEGs including 420 upregulated and 460 downregulated genes in MYCN positive NB in the training group. Overlap of the DEGs and WGCNA networks identified four shared genes, namely, ZNF695, CHEK1, C15ORF42 and EXO1, as candidate hub genes in MYCN positive NB. Three core genes, ZNF695, CHEK1 and C15ORF42, were finally identified by Lasso regression and Roc analyses. ZNF695, CHEK1 and C15ORF42 were highly expressed in MYCN positive NB tissues and cell lines. These three genes were closely related to the prognosis of children with NB. Except that Activated CD4 T cell and Type2 T helper cell increased, the infiltration levels of the other 26 cells decreased significantly in MYCN positive NB tissues. The infiltration levels of Type2 T helper cell and Activated CD4 T cell were also significantly positively correlated with the expression levels of the three hub genes.ConclusionZNF695, CHEK1 and C15ORF42 are highly expressed in MYCN positive NB, and their expression levels are negatively correlated with the prognosis of children with NB. The infiltration levels of Activated CD4 T cell and Type2 T helper cell increased in the microenvironment of MYCN positive NB and were significantly positively correlated with the expression levels of the three hub genes. The results of this study provide that ZNF695, CHEK1 and C15ORF42 may be potential prognostic markers and immunotherapy targets for MYCN positive NB.

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“…Furthermore, several studies examined the influence of infiltrating immune cells on patients’ prognosis [ 18 , 19 , 20 , 22 , 65 ] ( Table 1 ). For example, among MB subgroups TAMs are enriched in SHH-MB patients, having an antitumoral role [ 66 ].…”

Section: Targeting Immune Cells In Pediatric Brain Tumorsmentioning

confidence: 99%

“…However, recent efforts have shown that antitumor immune control exists in pediatric brain tumors, e.g., rhabdoid tumors and LGG showed strong T cell infiltration [ 15 , 16 , 17 ]. Moreover, several studies present prognostications of specific immune cell infiltrates on overall survival [ 18 , 19 , 20 , 21 , 22 ] ( Table 1 ). Thus, the indications that host immunity may already impact patients’ survival in pediatric brain tumors suggests that immune profiling can add valuable information in diagnostics and assessment of immunotherapeutic therapies for individual patients.…”

Section: Introductionmentioning

confidence: 99%

The Growing Relevance of Immunoregulation in Pediatric Brain Tumors

Melcher

Kerl

2021

Cancers

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Pediatric brain tumors are genetically heterogeneous solid neoplasms. With a prevailing poor prognosis and widespread resistance to conventional multimodal therapy, these aggressive tumors are the leading cause of childhood cancer-related deaths worldwide. Advancement in molecular research revealed their unique genetic and epigenetic characteristics and paved the way for more defined prognostication and targeted therapeutic approaches. Furthermore, uncovering the intratumoral metrics on a single-cell level placed non-malignant cell populations such as innate immune cells into the context of tumor manifestation and progression. Targeting immune cells in pediatric brain tumors entails unique challenges but promising opportunities to improve outcome. Herein, we outline the current understanding of the role of the immune regulation in pediatric brain tumors.

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Prognostic implications of immune-related eight-gene signature in pediatric brain tumors

Cited by 8 publications

References 33 publications

Nomogram Models Based on the Gene Expression in Prediction of Breast Cancer Bone Metastasis

Nomogram Models Based on the Gene Expression in Prediction of Breast Cancer Bone Metastasis

Identification of hub genes and their correlation with infiltration of immune cells in MYCN positive neuroblastoma based on WGCNA and LASSO algorithm

The Growing Relevance of Immunoregulation in Pediatric Brain Tumors

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