Integrated Genome-Wide Analysis of Gene Expression and DNA Copy Number Variations Highlights Stem Cell-Related Pathways in Small Cell Esophageal Carcinoma

Abstract

Purpose/Objectives. Primary small cell esophageal carcinoma (SCEC) represents a rare and aggressive malignancy without any prospective clinical trial or established treatment strategy at present. Although previous studies have indicated similarities between SCEC and small cell lung cancer (SCLC) in terms of their clinical manifestations, pathology, and morphology, very little genetic information is available on this highly malignant tumor. At present, patients with SCEC are staged and treated according to the guidelines established for SCLC. However, early recurrence and distant metastasis are common, and long-time survivors are rare. Current options available for patients with relapsed SCEC are fairly unsatisfactory, and their prognosis is generally poor. Novel therapeutic approaches against SCEC are therefore urgently needed and require a deeper understanding of the underlying genetic mechanisms. The current investigation aims to characterize the gene expression profile and copy number variations (CNVs) in SCEC to clarify molecular markers and pathways that may possess clinical significance. Materials/Methods. De novo expression array was carried out on three matched sets of primary SCEC and adjacent normal tissue samples procured from the institutional tissue bank, utilizing the Affymetrix HG U133 Plus 2.0 Array. After individual tissue normalization, the statistical software GeneSpring GX 12.5 was used to determine differentially expressed genes (DEGs) in the tumors relative to their paired normal tissues. Gene enrichments in addition to functional annotation and gene interaction networks were performed using DAVID 6.8 and STRING 10.0, respectively. A gene alteration was determined to be recurrent if it was observed in at least 2 samples. Chromosomes X and Y were not included in calculations as gender differences are a known source of analysis bias. The DEGs of at least one SCEC sample could be mapped to the CNV regions (fold change (FC) ≥ 2 and false discovery rate (FDR) < 0.01) after gene expression profiling by RefSeq Transcript ID. These overlapped genes were subjected to the functional annotation using DAVID 6.8. In order to elucidate the effect of CNV on mRNA expression, we integrated the genome-wide copy number data and gene expression in 3 paired samples. CNV-associated gene expression aberration (CNV-FC) was calculated for the recurrent DEGs using previously published integrated microarray data as reference. Pearson's correlation coefficient was employed to determine if there was a statistical correlation between the gene expression log2 ratios and their copy numbers using the SPSS 19.0 software. Genes that possessed CNV-FC ≥ 2 and r ≥ 0.6 (p < 0.05) were determined to be genes potentially associated with cancer. Results. High-quality DNA and total RNA were first extracted from both SCEC and normal tissues. Microarray data showed significant upregulation in WNT gene sets and downregulation in the PTEN and notch gene sets in SCEC. Functional annotation showed that genes associated with DNA replicatio...

The Pharmacological Effects of Spatholobi Caulis Tannin in Cervical Cancer and Its Precise Therapeutic Effect on Related circRNA

“…The KEGG analysis identified that the differentially expressed genes were enriched in the following pathways: cell cycle, DNA replication, p53-signaling pathway, and other pathways. These signaling pathways are important in multicellular eukaryotes, and they are not only involved in the processes of cell proliferation, differentiation, and apoptosis but also in the regulation of gynecological diseases, viral infections, and other physiological processes 30 …”

Integrated Analysis of Hub Genes and Pathways In Esophageal Carcinoma Based on NCBI’s Gene Expression Omnibus (GEO) Database: A Bioinformatics Analysis

“…After an overall scrutiny, the datasets of GSE100942 [ 15 ] and GSE111044 [ 16 ] were selected as they suited the inclusion standards we set up. Moreover, both of them had been created recently, in the past 5 years, which met the need for timeliness.…”

Genomic and transcriptomic characterization of the human glioblastoma cell line AHOL1

“…Slides were scanned using the Agilent G2565CA scanner. Data were extracted with Feature Extraction software (v9.1 Agilent Technologies) and analyzed with Genomic Work Bench 11.0.1, Agilent Cytogenomics 5.0 and GeneSpring GX 14.5, as described elsewhere ( 5 ). The algorithm used was Aberration Detection Method 2 (ADM-2), applying the following filters: threshold=6; minimum number of probes in region=3; and Log2Ratio >0.25 and log2Ratio <-0.25 were defined as copy number gains and losses, respectively.…”

“…To identify the significant genes that exhibited CNA and gene expression alterations, we integrated the significant CNAs and DEGs using GeneSpring software GX 14.5 (Agilent Technologies) as described elsewhere ( 5 ).…”