Dataset Information

Bioinformatic Identification of Neuroblastoma Microenvironment-Associated Biomarkers with Prognostic Value.

ABSTRACT: The microenvironment plays a vital role in the tumor recurrence of neuroblastoma. This research aimed at exploring prognostic genes that are involved in neuroblastoma microenvironment. We used "estimate" R package to calculate the immune/stromal/ESTIMATE scores of each sample of ArrayExpress dataset E-MTAB-8248 based on the ESTIMATE algorithm. Then we found that immune/stromal/ESTIMATE scores were not correlated with age/chromosome 11q, but tumor stage, MYCN gene amplifications, and chromosome 1p. Samples were then divided into high- and low-score groups, and 280 common differentially expressed genes (DEGs) were identified. 64 potential prognostic genes were harvested through overall survival analysis from the common DEGs. 14 prognostic genes (ABCA6, SEPP1, SLAMF8, GPR171, ABCA9, ARHGAP15, IL7R, HLA-DPB1, GZMA, GPR183, CCL19, ITK, FGL2, and CD1C) were obtained after screening in two independent cohorts. GO and KEGG analysis discovered that common DEGs and 64 potential prognostic genes are mainly involved in T-cell activation, lymphocyte activation regulation, leukocyte migration, and the interaction of cytokines and cytokine receptors. Correlation analysis showed that all prognostic genes were negatively correlated with MYCN amplification. Cox analysis identified 5 independent prognostic genes (ARHGAP15, ABCA9, CCL19, SLAMF8, and CD1C).

SUBMITTER: Wang Y

PROVIDER: S-EPMC7501561 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Bioinformatic Identification of Neuroblastoma Microenvironment-Associated Biomarkers with Prognostic Value.

Wang Yi Y Luo Huan H Cao Jing J Ma Chao C

Journal of oncology 20200910

The microenvironment plays a vital role in the tumor recurrence of neuroblastoma. This research aimed at exploring prognostic genes that are involved in neuroblastoma microenvironment. We used "estimate" R package to calculate the immune/stromal/ESTIMATE scores of each sample of ArrayExpress dataset E-MTAB-8248 based on the ESTIMATE algorithm. Then we found that immune/stromal/ESTIMATE scores were not correlated with age/chromosome 11q, but tumor stage, MYCN gene amplifications, and chromosome 1 ...[more]

PMID: 32963529

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Project description:BackgroundTo identify prognostic genes which were associated with adrenocortical carcinoma (ACC) tumor microenvironment (TME).Methods and materialsTranscriptome profiles and clinical data of ACC samples were collected from The Cancer Genome Atlas (TCGA) database. We use ESTIMATE (estimation of stromal and Immune cells in malignant tumor tissues using expression data) algorithm to calculate immune scores, stromal scores and estimate scores. Heatmap and volcano plots were applied for differential analysis. Venn plots were used for intersect genes selection. We used protein-protein interaction (PPI) networks and functional analysis to explore underlying pathways. After performing stepwise regression method and multivariate Cox analysis, we finally screened hub genes associated with ACC TME. We calculated risk scores (RS) for ACC cases based on multivariate Cox results and evaluated the prognostic value of RS shown by receiver operating characteristic curve (ROC). We investigated the association between hub genes with immune infiltrates supported by algorithm from online TIMER database.ResultsGene expression profiles and clinical data were downloaded from TCGA. Lower immune scores were observed in disease with distant metastasis (DM) and locoregional recurrence (LR) than other cases (P = .0204). Kaplan-Meier analysis revealed that lower immune scores were significantly associated with poor overall survival (OS) (P = .0495). We screened 1649 differentially expressed genes (DEGs) and 1521 DEGs based on immune scores and stromal scores, respectively. Venn plots helped us find 1122 intersect genes. After analysing by cytoHubba from Cytoscape software, 18 hub genes were found. We calculated RS and ROC showed significantly predictive accuracy (area under curve (AUC) = 0.887). ACC patients with higher RS had worse survival outcomes (P < .0001). Results from TIMER (tumor immune estimation resource) database revealed that HLA-DOA was significantly related with immune cells infiltration.ConclusionWe screened a list of TME-related genes which predict poor survival outcomes in ACC patients from TCGA database.

Project description:Background: The tumor microenvironment (TME) has been reported to have significant value in the diagnosis and prognosis of cancers. This study aimed to identify key biomarkers in the TME of luminal breast cancer (BC). Methods: We obtained immune scores (ISs) and stromal scores (SSs) for The Cancer Genome Atlas (TCGA) luminal BC cohort from the online ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) portal. The relationships between ISs and SSs and the overall survival of luminal BC patients were assessed by the Kaplan-Meier method. The differentially expressed messenger RNAs (DEmRNAs) related to the ISs and SSs were subjected to functional enrichment analysis. Additionally, a competing endogenous RNA (ceRNA) network was constructed with differentially expressed microRNAs (DEmiRNAs) and long noncoding RNAs (DElncRNAs). Furthermore, a protein-protein interaction (PPI) network was established to analyze the DEmRNAs in the ceRNA network. Then, survival analysis of biomarkers involved in the ceRNA network was carried out to explore their prognostic value. Finally, these biomarkers were validated using the luminal BC dataset from the Gene Expression Omnibus (GEO) database. Results: The results showed that ISs were significantly associated with longer survival times of luminal BC patients. Functional enrichment analysis showed that the DEmRNAs were mainly associated with immune response, antigen binding, and the extracellular region. In the PPI network, the top 10 DEmRNAs were identified as hub genes that affected the TME of luminal BC. Finally, two DEmiRNAs, two DElncRNAs, and 17 DEmRNAs of the ceRNA network associated with the TME were shown to have prognostic value. Subsequently, the expression of 15 prognostic biomarkers was validated in one additional dataset (GSE81002). In particular, one lncRNA (GVINP1) and five mRNAs (CCDC69, DOCK2, IKZF1, JCHAIN, and NCKAP1L) were novel biomarkers. Conclusions: Our studies demonstrated that ISs were associated with the survival of luminal BC patients, and a set of novel biomarkers that might play a prognostic role in the TME of luminal BC was identified.

Dataset Information

Bioinformatic Identification of Neuroblastoma Microenvironment-Associated Biomarkers with Prognostic Value.

Publications

Bioinformatic Identification of Neuroblastoma Microenvironment-Associated Biomarkers with Prognostic Value.

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