Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA95773 | ENA |
REPOSITORIES: ENA
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Project description:A Gene Expression Signature Predicts Survival of Patients with Stage I Non-Small Cell Lung Cancer
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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
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Project description:miRNAs related to metastasis of Small Cell Lung Cancer
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