Deciding on the neoadjuvant approach for esophageal adenocarcinomas.
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ABSTRACT:
SUBMITTER: Xu C
PROVIDER: S-EPMC5542969 | biostudies-literature | 2017 Jul
REPOSITORIES: biostudies-literature
Publications
Deciding on the neoadjuvant approach for esophageal adenocarcinomas.
Xu Cai C Lin Steven Hsesheng SH
Journal of thoracic disease 20170701 7
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Project description:The incidence of esophageal and junctional adenocarcinoma has increased 6 fold in the last 30 years and 5 year survival remains less than 14%. Most patients present with advanced disease and current staging is limited in its ability to predict survival. We aimed to generate and validate a molecular prognostic signature for esophageal adenocarcinoma. Gene expression profiling was performed and the resulting signatures correlated with clinical features for 91 snap frozen esophageal and junctional resection specimens. Gene expression profiles were obtained from 76/91 of the samples (82%). 119 genes were significantly associated with survival and 270 genes with the number of involved lymph nodes. Three genes were prognostic at the protein level in the external validation dataset. This three gene signature outperformed all the pathological features at predicting survival in this independent cohort (p<0.001). Total RNA isolated from human tissue sections was used to make fluorescently labeled cRNA that was hybridized to DNA oligonucleotide. Briefly, 4 µg of total RNA was used to synthesize dsDNA through reverse transcription. cRNA was produced by in vitro transcription and labeled postsynthetically with Cy3 or Cy5. Two populations of labeled cRNA, a reference population and an experimental population, were compared with each other by competitive hybridization to microarrays. Two hybridizations were done with each cRNA sample pair using a fluorescent dye reversal strategy. Human microarrays contained oligonucleotide probes corresponding to approximately 21,000 genes. All oligonucleotide probes on the microarrays were synthesized in situ with inkjet technology (Agilent Technologies, Palo Alto, CA). After hybridization, arrays were scanned and fluorescence intensities for each probe were recorded. Ratios of transcript abundance (experimental to control) were obtained following normalization and correction of the array intensity data. Gene expression data analysis was done with the Rosetta Resolver gene expression analysis software (version 7.0, Rosetta Biosoftware, Seattle, WA).
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