Multi-Omics Analysis of Brain Metastasis Outcomes Following Craniotomy
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA689295 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR13344000_1.fastq.gz | Fastqsanger.gz | |||
| SRR13344000_2.fastq.gz | Fastqsanger.gz | |||
| SRR13344100_1.fastq.gz | Fastqsanger.gz | |||
| SRR13344100_2.fastq.gz | Fastqsanger.gz | |||
| SRR13344001_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 376 |
Similar Datasets
Project description:Purpose: To reveal multi-omics features and disease subtypes associated with brain metastasis outcomes following craniotomy Methods: We executed a single institution retrospective collection of brain metastasis from patients who were diagnosed with lung, breast, and other primary tumors. The brain metastatic samples were sent for RNA sequencing, proteomic and metabolomic analysis of brain metastasis. The primary outcome was distant brain failure after definitive therapies that included craniotomy resection and radiation to surgical bed. Novel prognostic subtypes were discovered using transcriptomic data and sparse non-negative matrix factorization. Results: We discovered two molecular subtypes showing statistically significant differential prognosis irrespective of tumor subtype. The median survival time of the good and the poor prognostic subtypes were 7.89 and 42.27 months, respectively. Further integrated characterization and analysis of these two distinctive prognostic subtypes using transcriptomic, proteomic, and metabolomic molecular profiles of patients identified key pathways and metabolites. The analysis suggested that immune microenvironment landscape as well as proliferation and migration signaling pathways may be responsible to the observed survival difference. Conclusions: A multi-omics approach to characterization of brain metastasis provides an opportunity to identify clinically impactful biomarkers and associated prognostic subtypes and generate provocative integrative understanding of disease.
2021-01-04 | GSE164150 | GEO
Project description:BackgroundThe incidence of brain metastasis continues to increase as therapeutic strategies have improved for a number of solid tumors. The presence of brain metastasis is associated with worse prognosis but it is unclear if distinctive biomarkers can separate patients at risk for CNS related death.MethodsWe executed a single institution retrospective collection of brain metastasis from patients who were diagnosed with lung, breast, and other primary tumors. The brain metastatic samples were sent for RNA sequencing, proteomic and metabolomic analysis of brain metastasis. The primary outcome was distant brain failure after definitive therapies that included craniotomy resection and radiation to surgical bed. Novel prognostic subtypes were discovered using transcriptomic data and sparse non-negative matrix factorization.ResultsWe discovered two molecular subtypes showing statistically significant differential prognosis irrespective of tumor subtype. The median survival time of the good and the poor prognostic subtypes were 7.89 and 42.27 months, respectively. Further integrated characterization and analysis of these two distinctive prognostic subtypes using transcriptomic, proteomic, and metabolomic molecular profiles of patients identified key pathways and metabolites. The analysis suggested that immune microenvironment landscape as well as proliferation and migration signaling pathways may be responsible to the observed survival difference.ConclusionA multi-omics approach to characterization of brain metastasis provides an opportunity to identify clinically impactful biomarkers and associated prognostic subtypes and generate provocative integrative understanding of disease.
| S-EPMC8056216 | biostudies-literature
Project description:Ewing sarcoma (ES) is the second most common bone tumor affecting children and young adults, with dismal outcomes for patients with metastases at diagnosis. Mechanisms leading to metastasis remain poorly understood. To deepen our knowledge on ES progression, we have profiled 3 tumors and 5 metastases from our spontaneous metastasis ES mouse model using a multi-omics approach. Combining transcriptomics, proteomics and methylomics analyses, we identified signaling cascades and targets enriched in metastases that could be modulating aggressiveness in ES.
2024-12-03 | GSE245203 | GEO
Project description:This SuperSeries is composed of the following subset Series: GSE32530: Primary tumorgrafts as advanced models for breast cancer that authentically reflect tumor histopathology, growth, metastasis, and patient outcomes (copy number) GSE32531: Patient-derived tumor grafts authentically reflect tumor pathology, growth, metastasis, and disease outcomes (expression) Refer to individual Series
2011-10-22 | E-GEOD-32532 | biostudies-arrayexpress
Project description:The pathogenesis of Colorectal cancer (CRC) metastasis remains unclear.We collect clinical data from our center and use Integrative omics to analyze and predict candidate biomarkers of colorectal cancer and distant metastasis.
| 2743637 | ecrin-mdr-crc
Project description:As Asians are underrepresented across many omics databases limiting the potential of precision medicine of the global population. It is thus important for multi-omics derived quantitative trait loci (QTLs) to fill the knowledge gap of complex traits in the Asian ancestry. Integrating omics data from genomics and epigenomics (including DNA methylation and RNA-seq from blood), we developed iMOMdb, an open-acesss database to enhance disease prediction models and precision medicine outcomes in Asian pregnant women.
2022-10-04 | GSE182409 | GEO
Project description:Single cells RNA-Seq of CD45+ cells during Staphylococcus aureus craniotomy infection
2020-12-07 | GSE153435 | GEO
Project description:Background: A pressing challenge in treating non-small cell lung cancer (NSCLC) lies in the significant variability of patient responses to immunotherapy. This issue underscores the critical need for innovative predictive models that can navigate beyond conventional biomarkers to enhance patient outcomes, particularly by addressing immunotherapy resistance or responsiveness. Hypothesis: We hypothesize that an integrated multi-omics approach will uncover interactions within the NSCLC tumor immune microenvironment (TIME) and identify novel biomarkers that are predictive of individual immunotherapy responses, thus aiding in the development of a robust personalized treatment planning model. Objective: To develop a predictive model for NSCLC immunotherapy response/resistance by identifying new biomarkers using co-detection by indexing (CODEX) and Digital Spatial Profiling of whole transcriptome atlas (DSP-WTA). Methods: We utilized a multi-omics approach, combining CODEX for spatial mapping of protein expression at the single-cell level and DSP-WTA for comprehensive transcriptomic insights from the cell types identified by CODEX. This methodology facilitated a detailed examination of the TIME in NSCLC samples from patients undergoing first-line immunotherapy. Results: Our analysis identified three cell types, proliferating tumor cells, granulocytes, and vessels, that are associated with resistance to immunotherapy. The high proportion of these cell types demonstrated a hazard ratio (HR) of 3.8 (p = 0.004) in the training cohort (N = 33) and 1.8 (p = 0.05) in the validation cohort (N = 35). In the response cell type model, higher levels of M1 macrophages, M2 macrophages, and CD4 T cells returned a HR of 0.4 (p = 0.019) in the training set and 0.49 (p = 0.036) in the validation set. Gene signatures related to these cell types also predicted outcomes with high accuracy. The resistant gene model, which included 8 genes associated with epithelial-mesenchymal transition (EMT) and cell migration showed a HR of 5.3 (p < 0.001) in the training set and 2.2 (p = 0.036) in the validation set. The response gene model, consisting of 8 genes associated with immunomodulation, had an HR of 0.22 (p = 0.005) in the training set and 0.38 (p = 0.034) in the validation set. Conclusion: This research highlights the potential of a multi-omics strategy in advancing NSCLC treatment toward precision oncology. By offering insights into the TIME and unveiling novel biomarkers, our model seeks to define resistance and to improve prediction of response to treatment.
2024-08-22 | GSE271689 | GEO
Project description:scRNA-seq of phagocytic vs. non phagocytic immune cells during acute S. aureus craniotomy infection
2022-08-30 | GSE209622 | GEO
Project description:Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models for Non-Small Cell Lung Cancer
| PRJNA1132922 | ENA