Multi-omics analysis of neuroblastoma cells reveals a diversity of malignant transformations
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ABSTRACT:
PROVIDER: PRJNA540775 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR14724371.fastq.gz | Fastqsanger.gz | |||
| SRR14724372.fastq.gz | Fastqsanger.gz | |||
| SRR14724373.fastq.gz | Fastqsanger.gz | |||
| SRR14724374.fastq.gz | Fastqsanger.gz | |||
| SRR14724375.fastq.gz | Fastqsanger.gz |
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Project description:Neuroblastoma (NB) is a pediatric cancer of the developing sympathetic nervous system that exhibits significant variation in the stage of differentiation and cell composition of tumours. Global loss of DNA methylation and genomic 5-hydroxymethylcytosine (5hmC) is a hallmark of human cancers. Here, we used our recently developed single base resolution approaches, hmTOP-seq and uTOP-seq, for construction of 5hmC maps and identification of large hypomethylated domains (PMDs) in different NB cell subpopulations. The 5hmC profiles revealed distinct signatures characteristic of different cell lineages and stages of malignant transformation of NB cells in a conventional and oxygen depleted environment, which often occurs in tumours. The analysis of the cell type-specific PMD distribution highlighted differences in global genome organization among NB cells that were ascribed to the same lineage identity by transcriptome assays. Collectively, we demonstrated a high informativeness of the integrative epigenomic and transcriptomic research in relation to large scale genome structure in investigating the mechanisms that regulate cell identities and developmental stages of NB cells. Such multi-omics analysis, as compared to mutational studies, open new ways for identification of novel disease-associated features which bring prognostic and therapeutic value in treating this aggressive pediatric disease.
2021-08-19 | GSE130612 | GEO
Project description:Neuroblastoma (NB) is a pediatric cancer of the developing sympathetic nervous system that exhibits significant variation in the stage of differentiation and cell composition of tumors. Global loss of DNA methylation and genomic 5-hydroxymethylcytosine (5hmC) is a hallmark of human cancers. Here, we used our recently developed single-base resolution approaches, hmTOP-seq and uTOP-seq, for construction of 5hmC maps and identification of large partially methylated domains (PMDs) in different NB cell subpopulations. The 5hmC profiles revealed distinct signatures characteristic to different cell lineages and stages of malignant transformation of NB cells in a conventional and oxygen-depleted environment, which often occurs in tumors. The analysis of the cell-type-specific PMD distribution highlighted differences in global genome organization among NB cells that were ascribed to the same lineage identity by transcriptomic networks. Collectively, we demonstrated a high informativeness of the integrative epigenomic and transcriptomic research and large-scale genome structure in investigating the mechanisms that regulate cell identities and developmental stages of NB cells. Such multiomics analysis, as compared with mutational studies, open new ways for identification of novel disease-associated features which bring prognostic and therapeutic value in treating this aggressive pediatric disease.
| S-EPMC8452964 | biostudies-literature
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Project description:Malignant Schwann cell precursors mediate intratumoral plasticity in human neuroblastoma
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Project description:Joint profiling of chromatin accessibility and gene expression from the same single cell provides critical information about cell types in a tissue and cell states during a dynamic process. These emerging multi-omics techniques help the investigation of cell-type resolved gene regulatory mechanisms. Here, we developed in situ SHERRY after ATAC-seq (ISSAAC-seq), a highly sensitive and flexible single cell multi-omics method to interrogate chromatin accessibility and gene expression from the same single cell. We demonstrated that ISSAAC-seq is sensitive and provides high quality data with orders of magnitude more features than existing methods. Using the joint profiles from thousands of nuclei from the mouse cerebral cortex, we uncovered major and rare cell types together with their cell-type specific regulatory elements and expression profiles. Finally, we revealed distinct dynamics and relationships of transcription and chromatin accessibility during an oligodendrocyte maturation trajectory.
2022-06-15 | E-MTAB-11264 | biostudies-arrayexpress
Project description:Malignant Schwann cell precursors mediate intratumoral plasticity in human neuroblastoma [Array]
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Project description:BackgroundNeuroblastoma is the most common extracranial solid tumor of childhood, arising from the sympathetic nervous system. High-risk neuroblastoma (HRNB) remains a major therapeutic challenge with low survival rates despite the intensification of therapy. This study aimed to develop a malignant-cell marker gene signature (MMGS) that might serve as a prognostic indicator in HRNB patients.MethodsMulti-omics datasets, including mRNA expression (single-cell and bulk), DNA methylation, and clinical information of HRNB patients, were used to identify prognostic malignant cell marker genes. MMGS was established by univariate Cox analysis, LASSO, and stepwise multivariable Cox regression analysis. Kaplan-Meier (KM) curve and time-dependent receiver operating characteristic curve (tROC) were used to evaluate the prognostic value and performance of MMGS, respectively. MMGS further verified its reliability and accuracy in the independent validation set. Finally, the characteristics of functional enrichment, tumor immune features, and inflammatory activity between different MMGS risk groups were also investigated.ResultsWe constructed a prognostic model consisting of six malignant cell maker genes (MAPT, C1QTNF4, MEG3, NPW, RAMP1, and CDT1), which stratified patients into ultra-high-risk (UHR) and common-high-risk (CHR) group. Patients in the UHR group had significantly worse overall survival (OS) than those in the CHR group. MMGS was verified as an independent predictor for the OS of HRNB patients. The area under the curve (AUC) values of MMGS at 1-, 3-, and 5-year were 0.78, 0.693, and 0.618, respectively. Notably, functional enrichment, tumor immune features, and inflammatory activity analyses preliminarily indicated that the poor prognosis in the UHR group might result from the dysregulation of the metabolic process and immunosuppressive microenvironment.ConclusionThis study established a novel six-malignant cell maker gene prognostic model that can be used to predict the prognosis of HRNB patients, which may provide new insight for the treatment and personalized monitoring of HRNB patients.
| S-EPMC9687102 | biostudies-literature
Project description:Multi-omics molecular profiling was performed on post-radical prostatectomy material from a cohort of 132 patients with localized prostate adenocarcinoma. Unsupervised classification techniques were used to build a comprehensive classification of prostate tumours based on three molecular levels: DNA copy number, DNA methylation, and mRNA expression.
2018-07-09 | E-MTAB-6127 | biostudies-arrayexpress
Project description:BACKGROUND:High-throughput methodologies such as microarrays and next-generation sequencing are routinely used in cancer research, generating complex data at different omics layers. The effective integration of omics data could provide a broader insight into the mechanisms of cancer biology, helping researchers and clinicians to develop personalized therapies. RESULTS:In the context of CAMDA 2017 Neuroblastoma Data Integration challenge, we explore the use of Integrative Network Fusion (INF), a bioinformatics framework combining a similarity network fusion with machine learning for the integration of multiple omics data. We apply the INF framework for the prediction of neuroblastoma patient outcome, integrating RNA-Seq, microarray and array comparative genomic hybridization data. We additionally explore the use of autoencoders as a method to integrate microarray expression and copy number data. CONCLUSIONS:The INF method is effective for the integration of multiple data sources providing compact feature signatures for patient classification with performances comparable to other methods. Latent space representation of the integrated data provided by the autoencoder approach gives promising results, both by improving classification on survival endpoints and by providing means to discover two groups of patients characterized by distinct overall survival (OS) curves. REVIEWERS:This article was reviewed by Djork-Arné Clevert and Tieliu Shi.
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