Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA138787 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:To rapidly identify new prognostic imaging biomarkers, we propose a bioinformatics approach that integrates gene expression and image data and leverages public gene expression data. We demonstrate our approach in non-small cell lung carcinoma patients for whom CT, PET/CT and gene expression data are available but without clinical follow-up. We extracted 180 image features and 56 high quality gene expression clusters, represented by metagenes. 115 image features were expressed in terms of metagenes, using sparse linear regression and cross-validation, with an accuracy of 65-86%. After mapping the signatures to a public gene expression dataset, 26 image features were significantly associated with recurrence-free survival and 22 with overall survival. A multivariate analysis identified multiple image features that were prognostic, independent of clinical covariates. Identifying prognostic imaging biomarkers by linking images and gene expression with outcomes in public gene expression datasets promises to accelerate the role of imaging in personalized medicine.
2012-06-01 | GSE28827 | GEO
Project description:To rapidly identify new prognostic imaging biomarkers, we propose a bioinformatics approach that integrates gene expression and image data and leverages public gene expression data. We demonstrate our approach in non-small cell lung carcinoma patients for whom CT, PET/CT and gene expression data are available but without clinical follow-up. We extracted 180 image features and 56 high quality gene expression clusters, represented by metagenes. 115 image features were expressed in terms of metagenes, using sparse linear regression and cross-validation, with an accuracy of 65-86%. After mapping the signatures to a public gene expression dataset, 26 image features were significantly associated with recurrence-free survival and 22 with overall survival. A multivariate analysis identified multiple image features that were prognostic, independent of clinical covariates. Identifying prognostic imaging biomarkers by linking images and gene expression with outcomes in public gene expression datasets promises to accelerate the role of imaging in personalized medicine. We studied 26 cases of NSCLC with both PET/CT and microarray data under IRB approval from Stanford University and the Veterans Administration Palo Alto Health Care System. The collection of tissue samples consisted of a distribution of poorly- to well-differentiated adenocarcinomas and squamous cell cancers. The surgeon had removed necrotic debris during excision and sampled cavitary lesions to include as much solid component as practical. Then, from the excised tumor, he cut a 3 to 5 mm thick slice along its longest axis, and froze it within 30 minutes of excision. We retrieved the frozen tissue and extracted the RNA that was then processed by the Stanford Functional Genomics Facility using Illumina Whole Genome Bead Chips (Human HT-12 v3.0)
2012-05-31 | E-GEOD-28827 | biostudies-arrayexpress
Project description:Through multidimensional genomic/protein multiomics analysis and clinical information integration of cancer tissue samples, a prognostic method for lung cancer, including non-small cell lung cancer (NSCLC), is developed and applied to precision medical care after discovering new drug targets.
2024-11-01 | PXD053903 | JPOST Repository
Project description:Through multidimensional genomic/protein multiomics analysis and clinical information integration of cancer tissue samples, a prognostic method for lung cancer, including non-small cell lung cancer (NSCLC), is developed and applied to precision medical care after discovering new drug targets.
2024-11-01 | PXD053921 | JPOST Repository
Project description:Through multidimensional genomic/protein multiomics analysis and clinical information integration of cancer tissue samples, a prognostic method for lung cancer, including non-small cell lung cancer (NSCLC), is developed and applied to precision medical care after discovering new drug targets.
2024-11-01 | PXD053969 | JPOST Repository
Project description:The tumor microenvironment strongly influences cancer development, progression and metastasis. The role of carcinoma-associated fibroblasts (CAFs) in these processes and their clinical impact has not been studied systematically in non-small cell lung carcinoma (NSCLC). We established primary cultures of CAFs and matched normal fibroblasts (NFs) from 15 resected NSCLC. We demonstrate that CAFs have greater ability than NFs to enhance the tumorigenicity of lung cancer cell lines. Microarray gene expression analysis of the 15 matched CAF and NF cell lines identified 46 differentially expressed genes, encoding for proteins that are significantly enriched for extracellular proteins regulated by the TGF-beta signaling pathway. We have identified a subset of 11 genes that formed a prognostic gene expression signature, which was validated in multiple independent NSCLC microarray datasets. Functional annotation using protein-protein interaction analyses of these and published cancer stroma-associated gene expression changes revealed prominent involvement of the focal adhesion and MAPK signalling pathways. Fourteen (30%) of the 46 genes also were differentially expressed in laser-capture micro-dissected corresponding primary tumor stroma compared to the matched normal lung. Six of these 14 genes could be induced by TGF-beta1 in NF. The results establish the prognostic impact of CAF-associated gene expression changes in NSCLC patients. This SuperSeries is composed of the following subset Series: GSE22862: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [expression profiling_CAFs] GSE22863: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [expression profiling_NSCLC stroma] GSE27284: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [methylation profiling] GSE27289: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [genome variation profiling]
2011-04-08 | E-GEOD-22874 | biostudies-arrayexpress
Project description:Although three-dimensional (3D) genome structures are altered in cancer cells, little is known about how these changes evolve and diversify during cancer progression. Leveraging genome-wide chromatin tracing to visualize 3D genome folding directly in tissues, we generated 3D genome cancer atlases of oncogenic Kras-driven murine lung and pancreatic adenocarcinoma. Our data reveal stereotypical, non-monotonic, and stage-specific alterations in 3D genome folding compaction, heterogeneity, and compartmentalization as cancers progress from normal to preinvasive and ultimately to invasive tumors, discovering a potential structural bottleneck in early tumor progression. Remarkably, 3D genome architectures distinguish morphologic cancer states in single cells, despite considerable cell-to-cell heterogeneity. Gene-level analyses of evolutionary changes in 3D genome compartmentalization not only showed that compartment-associated genes are more homogeneously regulated but also elucidated prognostic and dependency genes in lung adenocarcinoma and a previously unappreciated role for polycomb-group protein Rnf2 in 3D genome regulation. Our results demonstrate the utility of mapping the single-cell cancer 3D genome in tissues and illuminate its potential to identify new diagnostic, prognostic, and therapeutic biomarkers in cancer.
2025-05-02 | GSE295857 | GEO
Project description:Although three-dimensional (3D) genome structures are altered in cancer cells, little is known about how these changes evolve and diversify during cancer progression. Leveraging genome-wide chromatin tracing to visualize 3D genome folding directly in tissues, we generated 3D genome cancer atlases of oncogenic Kras-driven murine lung and pancreatic adenocarcinoma. Our data reveal stereotypical, non-monotonic, and stage-specific alterations in 3D genome folding compaction, heterogeneity, and compartmentalization as cancers progress from normal to preinvasive and ultimately to invasive tumors, discovering a potential structural bottleneck in early tumor progression. Remarkably, 3D genome architectures distinguish morphologic cancer states in single cells, despite considerable cell-to-cell heterogeneity. Gene-level analyses of evolutionary changes in 3D genome compartmentalization not only showed that compartment-associated genes are more homogeneously regulated but also elucidated prognostic and dependency genes in lung adenocarcinoma and a previously unappreciated role for polycomb-group protein Rnf2 in 3D genome regulation. Our results demonstrate the utility of mapping the single-cell cancer 3D genome in tissues and illuminate its potential to identify new diagnostic, prognostic, and therapeutic biomarkers in cancer.
2025-05-24 | GSE295452 | GEO
Project description:Although three-dimensional (3D) genome structures are altered in cancer cells, little is known about how these changes evolve and diversify during cancer progression. Leveraging genome-wide chromatin tracing to visualize 3D genome folding directly in tissues, we generated 3D genome cancer atlases of oncogenic Kras-driven murine lung and pancreatic adenocarcinoma. Our data reveal stereotypical, non-monotonic, and stage-specific alterations in 3D genome folding compaction, heterogeneity, and compartmentalization as cancers progress from normal to preinvasive and ultimately to invasive tumors, discovering a potential structural bottleneck in early tumor progression. Remarkably, 3D genome architectures distinguish morphologic cancer states in single cells, despite considerable cell-to-cell heterogeneity. Gene-level analyses of evolutionary changes in 3D genome compartmentalization not only showed that compartment-associated genes are more homogeneously regulated but also elucidated prognostic and dependency genes in lung adenocarcinoma and a previously unappreciated role for polycomb-group protein Rnf2 in 3D genome regulation. Our results demonstrate the utility of mapping the single-cell cancer 3D genome in tissues and illuminate its potential to identify new diagnostic, prognostic, and therapeutic biomarkers in cancer.
2025-05-24 | GSE275588 | GEO
Project description:The prognostic factors of skull base chordoma associated with outcomes of patients after surgical resection remain poorly defined. This project aimed to identify a novel prognostic factor for patients with skull base chordoma. Using a proteomics approach, we screened tumor biomarkersthat upregulated in the rapid-recurrence group of chordoma, narrowed down by bioinformatics analysis, and finally potential biomarker was chosen for validation by immunohistochemistry using tissue microarray.
2021-11-03 | PXD025894 | Pride