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Robust prognostic biomarkers for EAC identified by systems-level characterization of tumor transcriptome

ABSTRACT: Despite continual efforts to rationalize a prognostic stratification of patients with esophageal adenocarcinoma (EAC) before treatment, current staging system only shows limited success owing to the lack of molecular and genetic markers that reflect prognostic features of the tumor. To develop molecular predictors of prognosis, we used systems-level characterization of tumor transcriptome. Using DNA microarray, genome-wide gene expression profiling was performed on 75 biopsy samples from patients with untreated EAC. Various statistical and informatical methods were applied to gene expression data to identify potential biomarkers associated with prognosis. Potential marker genes were validated in an independent cohort using quantitiative RT-PCR to measure gene expression. Distinct subgroups of EAC were uncovered by systems-level characterization of tumor transcriptome. We also identified a six-gene expression signature that could be used to predict overall survival (OS) of EAC patients. In particular, expression of SPARC and SPP1 was a strong independent predictor of OS, and a combined gene expression signature with these two genes was associated with prognosis (P < 0.024), even when all relevant pathological variables were considered together in multivariate Cox hazard regression analysis. Our findings suggest that molecular features reflected in gene expression signatures may dictate the prognosis of EAC patients, and these gene expression signatures can be used to predict the likelihood of prognosis at the time of diagnosis and before treatment.

ORGANISM(S): Homo sapiens

PROVIDER: GSE13898 | GEO | 2011/01/04

SECONDARY ACCESSION(S): PRJNA110305

REPOSITORIES: GEO

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Robust prognostic biomarkers for EAC identified by systems-level characterization of tumor transcriptome

Project description:Despite continual efforts to rationalize a prognostic stratification of patients with esophageal adenocarcinoma (EAC) before treatment, current staging system only shows limited success owing to the lack of molecular and genetic markers that reflect prognostic features of the tumor. To develop molecular predictors of prognosis, we used systems-level characterization of tumor transcriptome. Using DNA microarray, genome-wide gene expression profiling was performed on 75 biopsy samples from patients with untreated EAC. Various statistical and informatical methods were applied to gene expression data to identify potential biomarkers associated with prognosis. Potential marker genes were validated in an independent cohort using quantitiative RT-PCR to measure gene expression. Distinct subgroups of EAC were uncovered by systems-level characterization of tumor transcriptome. We also identified a six-gene expression signature that could be used to predict overall survival (OS) of EAC patients. In particular, expression of SPARC and SPP1 was a strong independent predictor of OS, and a combined gene expression signature with these two genes was associated with prognosis (P < 0.024), even when all relevant pathological variables were considered together in multivariate Cox hazard regression analysis. Our findings suggest that molecular features reflected in gene expression signatures may dictate the prognosis of EAC patients, and these gene expression signatures can be used to predict the likelihood of prognosis at the time of diagnosis and before treatment. 64 primary esophageal adenocarcinoma, 15 Barrett's esophagus and 28 surrounding normal fresh frozen tissues were used for microarray. All the tissues were obtained after curative resection after pathologic confirm at UT M.D. Anderson Cancer Center (MDACC). Microarray experiment and data analysis were done in the Dept. of systems biology at MDACC DNA microarray (Illumina human V2)

2011-01-04 | E-GEOD-13898 | biostudies-arrayexpress

Integrated Analysis of Genome-Wide DNA Copy Number and Gene Expression with Patient Outcomes in Esophageal Adenocarcinoma [Copy Number]

Project description:Esophageal adenocarcinoma (EAC) is now the most common esophageal malignancy in the United States with >15,000 new cases per year. Prognosis for patients with EAC is poor due to both late diagnosis and early metastasis to lymph nodes. The goal of this study was to perform an integrated analysis of genome-wide DNA copy number changes, gene expression and patient clinical data in order to identify events and genes associated with clinical endpoints or that may play a key role in EAC development and therefore provide potential therapeutic targets. Specifically, we report on a series of 116 EAC specimens studied using Affymetrix SNP 6.0 and U133 Plus 2 GeneChip arrays plus additional cohorts of 73 and 114 EAC patients whose tumors were analyzed using Affymetrix StyI 250K arrays and fluorescence in-situ hybridization (FISH) respectively. Integration of this genomic data with clinical information and outcomes enabled identification of copy number changes and coordinate gene expression events that are associated with patient prognosis, that validate known or suspected oncogenes and tumor suppressor genes and that also facilitate identification of novel, putative driver genes in large regions of copy number gain and loss. These findings add to our understanding of the basic biology of EAC and provide a guide for development of novel prognostic tools and therapies.

2016-11-01 | GSE72660 | GEO

Methylome and transcriptome of EAC (expression)

Project description:genome-wide methylation profile of 250 samples including 125 EAC, 19 Barrett’s, 64 normal adjacent squamous and 21 normal stomach. Transcriptome data was performed for 70 samples with methylation profile (48 EAC, 4 Barrett’s and 18 normal adjacent squamous). This is the first study to use methylome, transcriptome and ENCODE data to characterize the regulatory role of methylation in EAC.

2016-05-01 | GSE72873 | GEO

Methylome and transcriptome of EAC (methylation)

2016-05-01 | GSE72872 | GEO

Gene expression profiling in HPV-infected oropharyngeal head and neck cancer

Project description:The prognosis of HPV-positive squamous oropharyngeal carcinomas (OPCs) is more favorable than that of HPV-negative OPCs. However, the prognosis of some of these tumors is dismal and to date validated survival predictors are missing in clinical practice. We designed a study on HPV-positive OPCs to determine whether a previously published gene expression tumor classification model,defined through meta-analysis of publicly available datasets (PMCID: PMC6721309), is able to predict survival in an external patient cohort. The 3 gene expression clusters were tested in a validation set of 286 cases and in a refined study population of 235 patients and in both confirmed their prognostic value. Five-year OS was 95% in the low risk cluster Cl1, 80% in the intermediate risk cluster (HR=5.74, p=.0057), and 66% in the high risk cluster (HR=9.28, p=.001). Functional/biological cluster characterization identified potentially targetable pathways in the high risk cluster. This prognostic stratification might be useful for clinical decision making and for planning future trials based on molecular tumor features.

2021-11-03 | GSE163173 | GEO

Microarray profiling analysis of whole transcriptome expression in osteosarcoma

Project description:Osteosarcoma (OS)is a rare primary malignant bone tumor in adolescents and children with a poor prognosis. Identification of prognostic genes lags far behind the advance of the treatments. We identified differential genes by microarray analysis from paired OS tissues. Hub genes, gene set enrichment analysis, and pathway analysis were performed to gain an insight into the pathway alterations of OS. These results showed CPE could be served as a prognostic factor in osteoblastic OS and should be further investigated as potential therapeutic target. The present study evaluated the whole transcriptome expression of osteosarcoma progression and provided novel therapeutic targets for advanced osteosarcoma.

2023-03-01 | GSE225588 | GEO

An mRNA Expression Signature for Prognostication in De Novo Acute Myeloid Leukemia Patients with Normal Karyotype

Project description:Although clinical features, cytogenetics, and mutations are widely used to predict prognosis in patients with acute myeloid leukemia (AML), further refinement of risk stratification is necessary for optimal treatment, especially in cytogenetically normal (CN) patients. We sought to generate a simple gene expression signature as a predictor of clinical outcome through analyzing the mRNA arrays of de novo CN-AML patients. Among 43 treatment response-related genes, expression of 11 genes was significantly associated with overall survival (OS) in univariate Cox regression analysis in 104 patients who received standard intensive chemotherapy. We integrated the z-transformed expression levels of these 11 genes to generate a risk scoring system. Higher risk scores were significantly associated with shorter OS (median 17.0 months vs. not reached, P<0.001) in ours and 3 other validation cohorts. In conclusion, we developed a simple mRNA expression signature for prognostication in CN-AML patients. This prognostic biomarker will help refine the treatment strategies for this group of patients.

2017-02-09 | GSE71014 | GEO

Novel prognostic implications of complement activation in the tumor microenvironment for de novo metastatic BRAF V600E mutant colorectal cancer

Project description:Prognosis of metastatic BRAF V600E mutant colorectal cancer (CRC) is poor, and the prognostic implications of immune contextures in the tumor microenvironment (TME) for CRC remain elusive. Complement activation in the TME was significantly associated with poor OS and was correlated with TAM M2 in patients with de novo metastatic BRAF V600E mutant CRC.

2022-11-08 | GSE211576 | GEO

Quantitative proteomics reveals that distant recurrence-associated protein R-Ras and Transgelin predict post-surgical survival of colorectal cancer Stage III patients

Project description:Surgical resection is the major clinical intervention for Stage III colorectal cancer (CRC) currently. However, as much as 30.8% of the patients who had ever taken curative resection came out of recurrence eventually. Therefore, to facilitate formulating effective treatment plans, there is an intense demand for Stage III CRC post-surgical prognostic biomarkers. In this study, we identified total 146 differentially expressed proteins (DEPs) associated with poor prognosis in Stage III CRC patients with TMT-based quantitative mass spectrometry (MS). In these DEPs, the protein expression level of R-Ras and Transgelin were tested with immunohistochemistry (IHC) of 192 individual specimens. Further Kaplan-Meier analysis revealed that the level of R-Ras and Transgelin is associated with patients’ 5-year overall survival (OS) and disease-free survival (DFS) significantly, and multivariate Cox-regression analyses revealed that R-Ras and Transgelin are independent prognostic factors for OS and DFS respectively. In conclusion, our study presents that R-Ras and Transgelin are potential post-surgical prognostic biomarkers of Stage III CRC.

2016-06-20 | PXD002903 | Pride

Development of a two-circular RNA panel as potential prognostic biomarker for gastric cancer

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

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