Expression data from non-small cell lung carcinoma (NSCLC)
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE63074 | GEO | 2014/11/06
SECONDARY ACCESSION(S): PRJNA266553
REPOSITORIES: GEO
Similar Datasets
Project description:Eighty-four completely resected stage I/II non-small cell lung cancer (NSCLC) without adjuvant therapy were profiled using whole genome expression microarrys in order to identify novel classifications associated with RFS. Association with relapse-free survival (RFS) and clinicopathological parameters was assessed. An external cohort of 162 tumors was used to validate results.
2014-07-29 | E-MTAB-1790 | biostudies-arrayexpress
Project description:Lung cancer remains the leading cause of cancer death worldwide. Overall 5-year survival is about 10-15% and despite curative intent surgery, treatment failure is primarily due to recurrent disease. Conventional prognostic markers are unable to determine which patients with completely resected disease within each stage group are likely to relapse. To identify a gene signature associated with recurrent squamous cell carcinoma (SCC) of lung, we analyzed primary tumour gene expression for a total of fifty-one SCCs (stage I-III) on 22,323 element microarrays, comparing expression profiles for individuals who remained disease-free for a minimum of 36 months with those from individuals whose disease recurred within 18 months of complete resection. Cox proportional hazards modeling with leave-one-out cross-validation identified a 70-gene capable of predicting the likelihood of tumor recurrence and a 79-gene signature predictive for overall survival. These two signatures were pooled to generate a 111-gene classifier which achieved an overall predictive accuracy for disease recurrence of 72% (77% sensitivity, 67% specificity) in an independent set of fifty-eight stage I-III SCCs. This classifier also predicted differences in survival (log-rank P=0.0008, hazard ratio (HR), 3.8 [95% confidence interval, 1.6-8.7]), and was superior to conventional prognostic markers such as TNM stage or N stage in predicting patient outcome. Genome-wide profiling has revealed a distinct gene expression profile for recurrent lung SCC which may be clinically useful as a prognostic tool. Expression profiling using 22K element microarrays of 51 primary lung squamous cell carcinomas.
2010-07-01 | E-GEOD-5123 | biostudies-arrayexpress
Project description:We have been attempting to stratify the stage II CRC patients into good or poor prognoses based on gene expression signatures of resected specimen. We collected more CRC stage II cases up to 300, and analyzed gene expression for those samples. We selected a set of 3,023 probes (1,978 genes) for extracting metastasis-related discriminator genes. These genes consisted of 491 prognosis related genes identical to the probes on another platform array, and other probes for hybridization and quality control. Using these 3,023 probes, we attempted to select discriminator genes, with the 150 training sample set, resulting 55 markers. We then applied the discriminator set to the second, 150 samples set in a blind test for predictive accuracy and prognostic validity. The multivariate Cox analyses showed the significant parameters to predict the prognosis with a hazard ratio of 3.5 (95% confidence interval, 0.12-0.67; p=0.004). This personalized prognosis is crucial with CRC patients, the predictive performance may deserve serious consideration for possible application to clinic.
2017-06-10 | GSE58394 | GEO
Project description:Lung cancer remains the leading cause of cancer death worldwide. Overall 5-year survival is about 10-15% and despite curative intent surgery, treatment failure is primarily due to recurrent disease. Conventional prognostic markers are unable to determine which patients with completely resected disease within each stage group are likely to relapse. To identify a gene signature associated with recurrent squamous cell carcinoma (SCC) of lung, we analyzed primary tumour gene expression for a total of fifty-one SCCs (stage I-III) on 22,323 element microarrays, comparing expression profiles for individuals who remained disease-free for a minimum of 36 months with those from individuals whose disease recurred within 18 months of complete resection. Cox proportional hazards modeling with leave-one-out cross-validation identified a 70-gene capable of predicting the likelihood of tumor recurrence and a 79-gene signature predictive for overall survival. These two signatures were pooled to generate a 111-gene classifier which achieved an overall predictive accuracy for disease recurrence of 72% (77% sensitivity, 67% specificity) in an independent set of fifty-eight stage I-III SCCs. This classifier also predicted differences in survival (log-rank P=0.0008, hazard ratio (HR), 3.8 [95% confidence interval, 1.6-8.7]), and was superior to conventional prognostic markers such as TNM stage or N stage in predicting patient outcome. Genome-wide profiling has revealed a distinct gene expression profile for recurrent lung SCC which may be clinically useful as a prognostic tool. Keywords: non-small cell lung carcinoma, squamous cell, tumor recurrence, expression profiling
2006-11-21 | GSE5123 | GEO
Project description:Epigenetic deregulation is a critical event in human malignancies. A number of DNA methylation markers are currently under evaluation as diagnostic and prognostic biomarkers for many cancers. However, its potential role in hepatocellular carcinoma (HCC) is under-explored. Aims: To develop a DNA methylation-based prognostic signature in surgically resected HCC
2015-02-13 | GSE56588 | GEO
Project description:Background: Circular RNAs (circRNAs) have attracted increasing attention in recent years for their potential application as disease biomarkers due to their high abundance and stability. In this study, we attempted to screen circRNAs that can be used to predict postoperative recurrence and survival in patients with gastric cancer (GC). Methods: High-throughput RNA sequencing was used to identify differentially expressed circRNAs in GC patients with different prognoses. The expression level of circRNAs in the training set (n=136) and validation set (n=167) was detected by quantitative real-time PCR (qRT-PCR). Kaplan–Meier estimator, receiver operating characteristic (ROC) curve and cox regression analysis were used to evaluate the prognostic value of circRNAs on recurrence-free survival (RFS) and overall survival (OS) in GC patients. CeRNA network prediction, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for the circRNAs with prognostic significance. Results: A total of 259 differentially expressed circRNAs were identified in GC patients with different RFS. We found two circRNAs (hsa_circ_0005092 and hsa_circ_0002647) that highly expressed in GC patients with good prognoses, and subsequently established a predictive model for postoperative recurrence and prognosis evaluation, named circPanel. Patients with circPanellow might have shorter recurrence-free survival (RFS) and overall survival (OS). We also performed circRNA-miRNA-mRNA network prediction and functional analysis for hsa_circ_0005092 and hsa_circ_0002647. Conclusions: CircPanel has the potential to be a prognostic biomarker in GC patients with greater accuracy than a single circRNA and certain traditional tumor markers (e.g., CEA, CA19-9 and CA724).
2021-08-12 | GSE181769 | GEO
Project description:Epigenetic deregulation is a critical event in human malignancies. A number of DNA methylation markers are currently under evaluation as diagnostic and prognostic biomarkers for many cancers. However, its potential role in hepatocellular carcinoma (HCC) is under-explored. Aims: To develop a DNA methylation-based prognostic signature in surgically resected HCC Tumors from 224 HCC resected patients, 10 normal Liver individuals and 9 Cirrhotic patients were analyzed. Methylome profiling was done with Illumina HumanMethylation450 (485,000 CpG, 96% of known CpG islands). We selected probes in CpG islands located in promoters, hypermethylated (B value higher than 50%) in at least 5% of the tumors and hypomethylated (B value lower than 33%) in more than 90% of normal liver.
2015-02-13 | E-GEOD-56588 | biostudies-arrayexpress
Project description:<h4><strong>INTRODUCTION:</strong> The identification of frequent acquired mutations shows that patients with oligodendrogliomas have divergent biology with differing prognoses regardless of histological classification. A better understanding of molecular features as well as their metabolic pathways is essential.</h4><h4><strong>OBJECTIVES: </strong>The aim of this study was to examine the relationship between the tumor metabolome, six genomic aberrations (isocitrate dehydrogenase1 [IDH1] mutation, 1p/19q codeletion, tumor protein p53 [TP53] mutation, O<sup>6</sup>-methylguanin-DNA methyltransferase [MGMT] promoter methylation, epidermal growth factor receptor [EGFR] amplification, phosphate and tensin homolog [PTEN] methylation), and the patients' survival time.</h4><h4><strong>METHODS:</strong> We applied <sup>1</sup>H high-resolution magic-angle spinning (HRMAS) nuclear magnetic resonance (NMR) spectroscopy to 72 resected oligodendrogliomas.</h4><h4><strong>RESULTS:</strong> The presence of IDH1, TP53, 1p19q codeletion, MGMT promoter methylation reduced the relative risk of death, whereas PTEN methylation and EGFR amplification were associated with poor prognosis. Increased concentration of 2-hydroxyglutarate (2HG), N-acetyl-aspartate (NAA), myo-inositol and the glycerophosphocholine/phosphocholine (GPC/PC) ratio were good prognostic factors. Increasing the concentration of serine, glycine, glutamate and alanine led to an increased relative risk of death.</h4><h4><strong>CONCLUSION:</strong> HRMAS NMR spectroscopy provides accurate information on the metabolomics of oligodendrogliomas, making it possible to find new biomarkers indicative of survival. It enables rapid characterization of intact tissue and could be used as an intraoperative method.</h4>
2021-10-06 | MTBLS869 | MetaboLights
Project description:Purpose: In this study, we have undertaken the whole proteomic analysis and got a better understanding of biological processes involved in the development and progression of ccRCC. We hope promising biomarkers can be uncovered to facilitate early diagnosis, predict the prognosis and progression, more importantly, to be applied as potential therapeutic targets. Experimental design: Fresh frozen tissue samples were surgically resected from patients with local or locally advanced ccRCC. Trypsin digested proteins were analyzed using TMT-based LC-MS/MS proteomic approach, followed by bioinformatic analysis. A potential prognostic marker FMNL1, annotated with actin cytoskeleton assembly and cell migration, was chosen to be validated in TCGA_KIRC datasets(n=597), further validation sets(n=20) and expanded validation sets(n=97). Results: A total of 657 differentially expressed proteins were identified and quantified between ccRCC and adjacent normal tissues (p-value<0.05, FC>2 or<1/2), of which 186 proteins were up-regulated and 471 proteins were down-regulated. Bioinformatic analysis showed enriched metabolic biological processes and pathways including oxidative phosphorylation, valine, leucine and isoleucine degeneration, propanoate metabolism, fatty-acid degeneration, TCA cycle, glycolysis/gluconeogenesis, etc. Univariate and multivariate analysis defined FMNL1 as an independent negative prognostic marker in the TCGA datasets. High expression of FMNL1 correlated significantly with tumor stage, T stage, lymph node metastasis(P<0.05), and distant metastasis(P<0.05) both in the TCGA-KIRC datasets and expanded validation sets. And KM survival curve illustrated that patients with high FMNL1 protein level had shorter OS time in the expanded validation sets (p=0.0273) Conclusion and clinical relevance: The proteomic results uncovered sophisticated metabolic reprogramming of ccRCC and indicated that the upregulation of rate-limiting enzymes in glycolysis and mitochondrial impairment may be the cause of metabolic reprogramming in ccRCC. Moreover, FMNL1 has been identified as a diagnostic and prognostic marker, and may be implicated in the progression of ccRCC.
2022-02-16 | PXD022919 | Pride
Project description:The aim of this study is to identify prognostic gene expression signatures associated with two molecularly distinct subtypes of colorectal cancer. Samples were taken from colorectal cancers in surgically resected specimens in 96 colorectal cancer patients. The expression profiles were determined using Affymetrix Human Genome U133Plus 2.0 arrays. This is a test set for validation of prognostic gene expression signature that was developed from GSE14333. All data were normalized by using the RMA method (affy package in R/Bioconductor).
2011-06-20 | GSE29971 | GEO