Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA95773 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:A Gene Expression Signature Predicts Survival of Patients with Stage I Non-Small Cell Lung Cancer
| PRJNA99683 | ENA
Project description:Breast cancer relapse free survival and lung metastasis free survival
| PRJNA96341 | ENA
Project description:We systematically profiled the genome-wide alternative splicing events in Lung Adenocarcinoma (LUAD) and Lung Squomous Cell Carcinoma (LUSC) against Lung Control through Human Transcriptome Array2.0. Non-invasive Stage IIIA non-small cell lung cancer (NSCLC) is heterogeneous in nature which makes it difficult to predict, diagnose and prognose owing to lower 5-year survival rate and 75-85% brain or bone metastasis. Hence, we hypothesized to develop transcript-based signature to categorize Stage IIIA-NSCLC-LUAD and LUSC, as well as identify markers which could indicate towards prognosis of disease. We were able to molecularly-categorize LUAD- and LUSC-tissue more precisely through HTA array2.0.
2020-09-21 | GSE138682 | GEO
Project description:Molecular Classification and Prediction of Survival in Non-Small-Cell Lung Cancer
| PRJNA107013 | ENA
Project description:Background: Current histopathological methods are inadequate for predicting outcome and recurrence in patients with non-small cell lung carcinoma (NSCLC) after surgery. In this study, we investigated the use of gene expression signatures to predict outcome and metastasis in lung cancer patients. Methods: Gene expression was studied by microarray and the real-time reverse transcriptase polymerase chain reaction (RT-PCR) in normal and lung tumor tissue of 188 NSCLC patients who underwent surgical resection. The 5 cancer-related genes and 1 reference gene expression levels measureed by real-time RT-PCR were used in a prospectively defined algorithm to determine the risk for each patient. Finally, we used an independent cohort to verify the 5 gene-based predictive model derived from decision tree analysis. Results: The 5 gene-based decision tree model was able to predict the prognosis. The recurrence rate at 36 months was 53% in the low-risk group versus 83% in the high-risk group (P=0.002). The 5 gene-based model could also predict overall survival (P<0.001). In multivariate analysis, the decision tree model predicted that high-low dichotomy and stage were both significant for recurrence. In addition, it could also predict metastasis and survival of NSCLC patients within the stage I-II subgroups. A similar result was found using an independent cohort of NSCLC patients. The high-risk patients had a significantly poorer overall survival than the low-risk patients (P=0.005). We also found distinct gene signatures which could distinguish between NSCLC, and normal tissue and histology subtypes. Conclusions: A gene expression signature can predict metastasis and survival of NSCLC patients. Keywords: Survival and metastasis analysis
2006-06-07 | GSE4882 | GEO
Project description:DUSP1/MKP1 promotes angiogenesis, invasion and metastasis in non-small cell lung cancer
| PRJNA122373 | ENA
Project description:A six-gene signature predicting breast cancer lung metastasis
| PRJNA107071 | ENA
Project description:Signature of MicroRNAs with Impact on Angiogenesis in Non-Small Cell Lung Cancer
| PRJNA138125 | ENA
Project description:The lungs are a frequent target of metastatic breast cancer cells, but the underlying molecular mechanisms are unclear. All existing data were obtained either using statistical association between gene expression measurements found in primary tumors and clinical outcome, or using experimentally derived signatures from mouse tumor models. Here, we describe a distinct approach that consists to utilize tissue surgically resected from lung metastatic lesions and compare their gene expression profiles with those from non-pulmonary sites, all coming from breast cancer patients. We demonstrate that the gene expression profiles of organ-specific metastatic lesions can be used to predict lung metastasis in breast cancer. We identified a set of 21 lung metastasis-associated genes. Using a cohort of 72 lymph node-negative breast cancer patients, we developed a six-gene prognostic classifier that discriminated breast primary cancers with a significantly higher risk of lung metastasis. We then validated the predictive ability of the six-gene signature in 3 independent cohorts of breast cancers consisting of a total of 721 patients. Finally, we demonstrated that the signature improves risk stratification independently of known standard clinical parameters and a previously established lung metastasis signature based on an experimental breast cancer metastasis model. Experiment Overall Design: We used microarrays to identify lung metastasis-related genes in a series of 23 patients with breast cancer metastases. No replicate, no reference sample.
2009-03-07 | E-GEOD-11078 | biostudies-arrayexpress