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:Genome wide DNA methylation profiling of Stage I Lung Adenocarcinoma and non-tumor adjacent tissues. The Illumina Infinium 27k Human DNA methylation Beadchip was used to obtain DNA methylation profiles across approximately 27,000 CpGs. Samples included tumor and adjacent non-tumor tissues excised from a cohort of 35 patients with Stage I Lung Adenocarcinoma. Candidate prognostic biomarkers were validated by pyrosequencing in independent cohorts.
2015-04-30 | GSE63384 | 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
Project description:Lung cancer remains the most common cause of cancer deaths worldwide, yet there is currently a lack of diagnostic noninvasive biomarkers that could guide treatment decisions. Small molecules (<1500 Da) were measured in urine collected from 469 lung cancer patients and 536 population controls using unbiased liquid chromatography-mass spectrometry. Clinical putative diagnostic and prognostic biomarkers were validated by quantitation and normalized to creatinine levels at two different time points and further validated in an independent sample set, which comprises 80 cases and 78 population controls, with similar demographic and clinical characteristics when compared to the training set. Creatine riboside (IUPAC name: 2-{2-[(2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)-oxolan-2-yl]-1-methylcarbamimidamido}acetic acid), a novel molecule identified in this study, and N-acetylneuraminic acid (NANA), were each significantly (P <0.00001) elevated in non–small cell lung cancer (NSCLC) and associated with worse prognosis (hazard ratio (HR) =1.81 [P =0.0002], and 1.54 [P =0.025], respectively). Creatine riboside was the strongest classifier of lung cancer status in all and stage I–II cases, important for early detection, and also associated with worse prognosis in stage I–II lung cancer (HR =1.71, P =0.048). All measurements were highly reproducible with intraclass correlation coefficients ranging from 0.82 – 0.99. Both metabolites were significantly (P <0.03) enriched in tumor tissue compared to adjacent non-tumor tissue (N =48), thus revealing their direct association with tumor metabolism. Creatine riboside and NANA may be robust urinary clinical metabolomic markers that are elevated in tumor tissue and associated with early lung cancer diagnosis and worse prognosis.
2014-05-06 | MTBLS28 | MetaboLights
Project description:Non-small cell lung cancer (NSCLC) is a very common solid tumor where only small advances in the reduction of relapse-free survival and overall survival have been accomplished. The issue is particularly critical for stage I patients where there are not available biomarkers, in the absence of detectable nodal or other metastatic involvement, that might indicate which high-risk patients should receive the beneficially proved adjuvant chemotherapy. We aimed to find DNA methylation markers with prognostic value that could be helpful in this regard and complement the conventional staging. A DNA methylation microarray that analyzes 450,000 CpG sites in the human genome was used to study primary tumoral DNA obtained from a multicenter cohort of 490 patients with NSCLC, corresponding to 339 adenocarcinomas, 133 squamous carcinomas and 18 large cell carcinomas, in addition to 25 normal lung epithelium samples. The obtained prognostic DNA methylation markers were validated by the development of a single methylation-pyrosequencing assay in an independent cohort of 143 patients with stage I NSCLC. The unsupervised clustering of the studied primary NSCLC distinguished two branches with distinct clinical outcomes. Those CpG sites that were the best predictors of recurrence in stage I NSCLC patients were further confirmed in the described independent cohort. Both the global DNA methylation classifier and the highly-ranked aberrantly methylated single genes improved prognostic accuracy beyond standard stagings.
2013-10-03 | GSE39279 | GEO