Dataset Information

Gene signatures associated with genomic aberrations predict prognosis in neuroblastoma.

ABSTRACT: BACKGROUND:Neuroblastoma (NB) is a heterogeneous disease with respect to genomic abnormalities and clinical behaviors. Despite recent advances in our understanding of the association between the genetic aberrations and clinical features, it remains one of the major challenges to predict prognosis and stratify patients for determining personalized therapy in this disease. The aim of this study was to develop an effective prognosis prediction model for NB patients. METHODS:We integrated diverse computational analyses to define gene signatures that reflect MYCN activity and chromosomal aberrations including deletion of chromosome 1p (Chr1p_del) and chromosome 11q (Chr11q_del) as well as chromosome 11q whole loss (Chr11q_wls). We evaluated the prognostic and predictive values of these signatures in seven NB gene expression datasets (the number of samples ranges from 94 to 498, with a total of 2120) generated from both RNA sequencing and microarray platforms. RESULTS:MYCN signature was a more effective prognostic marker than MYCN amplification status and MYCN expression. Similarly, the Chr1p_del score was more prognostic than Chr1p status. The activity scores of MYCN, Chr1p_del and Chr11q_del were associated with poor prognosis, while the Chr11q_wls score was linked to good outcome. We integrated the activity scores of MYCN, Chr1p_del, Chr11q_del, and Chr11q_wls and clinical variables into an integrative prognostic model, which displayed significant performance over the clinical variables or each genomic aberration alone. CONCLUSIONS:Our integrative gene signature model shows a significantly improved forecast performance with prognostic and predictive information, and thereby can be served as a biomarker to stratify NB patients for prognosis evaluation and surveillance programs.

SUBMITTER: He X

PROVIDER: S-EPMC7163660 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Gene signatures associated with genomic aberrations predict prognosis in neuroblastoma.

He Xiaoyan X Qin Chao C Zhao Yanding Y Zou Lin L Zhao Hui H Cheng Chao C

Cancer communications (London, England) 20200301 2-3

<h4>Background</h4>Neuroblastoma (NB) is a heterogeneous disease with respect to genomic abnormalities and clinical behaviors. Despite recent advances in our understanding of the association between the genetic aberrations and clinical features, it remains one of the major challenges to predict prognosis and stratify patients for determining personalized therapy in this disease. The aim of this study was to develop an effective prognosis prediction model for NB patients.<h4>Methods</h4>We integr ...[more]

PMID: 32237073

Similar Datasets

Project description:BackgroundA high heterogeneity and activation of multiple oncogenic pathways have been implicated in failure of targeted therapies in glioblastoma (GBM).MethodsUsing The Cancer Genome Atlas data, we identified subtype-specific prognostic core genes by a combined approach of genome-wide Cox regression and Gene Set Enrichment Analysis. The results were validated with 8 combined public datasets containing 608 GBMs. We further examined prognostic chromosome aberrations and mutations.ResultsIn classical and mesenchymal subtypes, 2 receptor tyrosine kinases (RTKs) (MET and IGF1R), and the genes in RTK downstream pathways such as phosphatidylinositol-3 kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR), and nuclear factor-kappaB (NF-kB), were commonly detected as prognostic core genes. Classical subtype-specific prognostic core genes included those in cell cycle, DNA repair, and the Janus kinase/signal transducers and activators of transcription (JAK-STAT) pathway. Immune-related genes were enriched in the prognostic genes showing negative promoter cytosine-phosphate-guanine (CpG) methylation/expression correlations. Mesenchymal subtype-specific prognostic genes were those related to mesenchymal cell movement, PI3K/Akt, mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK), Wnt/β-catenin, and Wnt/Ca2+ pathways. In copy number alterations and mutations, 6p loss and TP53 mutation were associated with poor and good survival, respectively, in the classical subtype. In the mesenchymal subtype, patients with PIK3R1 or PCLO mutations showed poor prognosis. In the glioma CpG island methylator phenotype (G-CIMP) subtype, patients harboring 10q loss, 12p gain, or 14q loss exhibited poor survival. Furthermore, 10q loss was significantly associated with the recently recognized G-CIMP subclass showing relatively low CpG methylation and poor prognosis.ConclusionThese subtype-specific alterations have promising potentials as new prognostic biomarkers and therapeutic targets combined with surrogate markers of GBM subtypes. However, considering the small number of events, the results of copy number alterations and mutations require further validations.

Project description:Background: The spontaneous regression of neuroblastoma (NB) is most prevalent and well-documented in stage 4s NB patients. However, whether autophagy plays roles in the spontaneous regression of NB is unknown. Objective: This study aimed to identify autophagy-related genes (ARGs) and autophagy-related long non-coding RNAs (lncRNAs) differentially expressed in stage 4 and stage 4s NB and to build prognostic risk signatures on the basis of the ARGs and autophagy-related lncRNAs. Methods: One RNA-sequence (RNA-Seq) dataset (TARGET NBL, n = 153) was utilized as discovery cohort, and two microarray datasets (n = 498 and n = 223) were used as validation cohorts. Differentially expressed ARGs were identified by comparing stage 4s and stage 4 NB samples. An ARG signature risk score and an autophagy-related lncRNA signature risk score were constructed. The receiver operating characteristic (ROC) curve analyses were used to evaluate the survival prediction ability of the two signatures. Gene function annotation and Gene Set Enrichment Analysis (GSEA) were performed to clarify the autophagic biological processes enriched in different risk groups. Results: Nine ARGs were integrated into the ARG signature. Patients in the high-risk group of the ARG signature had significantly poorer overall survival (OS) than patients in the low-risk group. The ROC curves analyses revealed that the ARG signature performed very well in predicting OS [5-year area under the curve (AUC) = 0.81]. Seven autophagy-related lncRNAs were integrated into the autophagy-related lncRNA signature. Patients in the high-risk group of the lncRNA signature had significantly poorer OS than patients in the low-risk group. The ROC curve analyses also revealed that the lncRNA signature performed well in predicting OS (5-year AUC = 0.77). Both the ARG signature and lncRNA signature are independent with other clinical risk factors in the multivariate Cox regression survival analyses. GSEAs revealed that autophagy-related biological processes are enriched in low-risk groups. Conclusions: Autophagy-related genes and lncRNAs are differentially expressed between stage 4 and stage 4s NB. The ARG signature and autophagy-related lncRNA signature successfully stratified NB patients into two risk groups. Autophagy-related biological processes are highly enriched in low-risk NB groups.

Project description:BackgroundPolyamines metabolism is closely related to tumor development and progression, as well as tumor microenvironment (TME). In this study, we focused on exploring whether polyamines metabolism-associated genes would provide prognosis and immunotherapy response prediction in lung adenocarcinoma (LUAD).MethodsThe expression profile data of polyamines metabolism-associated genes were acquired from the Cancer Genome Atlas (TCGA) database. Utilizing the least absolute shrinkage and selection operator (LASSO) algorithm, we created a risk score model according to polyamines metabolism-associated gene signatures. Meanwhile, an independent cohort (GSE72094) was employed to validate this model. Through the univariate and multivariate Cox regression analyses, the independent prognostic factors were identified. Subsequently, quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect their expression in LUAD cells. By consensus clustering analysis, polyamines metabolism-associated subgroups were determined in LUAD patients, with differential gene expression, prognosis, and immune characteristics analyses explored.ResultsA total of 59 polyamines metabolism genes were collected for this study, of which 14 genes were identified for the construction of risk score model using LASSO method. High- and low- risk groups of LUAD patients in TCGA cohort were distinguished via this model, and high-risk group presented dismal clinical outcomes. The same prognostic prediction of this model had been also validated in GSE72094 cohort. Meanwhile, three independent prognostic factors (PSMC6, SMOX, SMS) were determined for constructing the nomogram, and they were all upregulated in LUAD cells. In addition, two distinct subgroups (C1 and C2) were identified in LUAD patients. Comparing the two subgroups, 291 differentially expressed genes (DEGs) were acquired, mainly enriching in organelle fission, nuclear division, and cell cycle. Comparing to C1 subgroup, the patients in C2 subgroup had favorable clinical outcomes, increased immune cells infiltration, and effective immunotherapy response.ConclusionThis study identified polyamines metabolism-associated gene signatures for predicting the patients' survival, and they were also linked to immune cells infiltration and immunotherapy response in LUAD patients.

Dataset Information

Gene signatures associated with genomic aberrations predict prognosis in neuroblastoma.

Publications

Gene signatures associated with genomic aberrations predict prognosis in neuroblastoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets