Dataset Information

Identification of gene signatures used to recognize biological characteristics of gastric cancer upon gene expression data.

ABSTRACT: High-throughput gene expression microarrays can be examined by machine-learning algorithms to identify gene signatures that recognize the biological characteristics of specific human diseases, including cancer, with high sensitivity and specificity. A previous study compared 20 gastric cancer (GC) samples against 20 normal tissue (NT) samples and identified 1,519 differentially expressed genes (DEGs). In this study, Classification Information Index (CII), Information Gain Index (IGI), and RELIEF algorithms are used to mine the previously reported gene expression profiling data. In all, 29 of these genes are identified by all three algorithms and are treated as GC candidate biomarkers. Three biomarkers, COL1A2, ATP4B, and HADHSC, are selected and further examined using quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) staining in two independent sets of GC and normal adjacent tissue (NAT) samples. Our study shows that COL1A2 and HADHSC are the two best biomarkers from the microarray data, distinguishing all GC from the NT, whereas ATP4B is diagnostically significant in lab tests because of its wider range of fold-changes in expression. Herein, a data-mining model applicable for small sample sizes is presented and discussed. Our result suggested that this mining model may be useful in small sample-size studies to identify putative biomarkers and potential biological features of GC.

SUBMITTER: Yan Z

PROVIDER: S-EPMC4149392 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Identification of gene signatures used to recognize biological characteristics of gastric cancer upon gene expression data.

Yan Zhi Z Luke Brian T BT Tsang Shirley X SX Xing Rui R Pan Yuanming Y Liu Yixuan Y Wang Jinlian J Geng Tao T Li Jiangeng J Lu Youyong Y

Biomarker insights 20140814

High-throughput gene expression microarrays can be examined by machine-learning algorithms to identify gene signatures that recognize the biological characteristics of specific human diseases, including cancer, with high sensitivity and specificity. A previous study compared 20 gastric cancer (GC) samples against 20 normal tissue (NT) samples and identified 1,519 differentially expressed genes (DEGs). In this study, Classification Information Index (CII), Information Gain Index (IGI), and RELIEF ...[more]

PMID: 25210421

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Project description:BackgroundGastric cancer (GC) is the most common type of gastrointestinal cancer, and has been studied extensively. However, resistance to chemotherapeutic agents has become a major problem, leading to treatment failure. This study aimed to investigate the molecular mechanisms mediating acquired resistance to cisplatin and fluorouracil (CF) combination-based chemotherapy in GC patients.MethodsThe microarray datasets (GSE14209, GSE30070) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) using the limma package in R/Bioconductor. Possible targets of the DEMs were predicted using miRWalk, and the putative miRNA-mRNA regulatory network was constructed using Cytoscape software. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) analyses were then conducted and visualized using the Search Tool for Retrieval of Interacting Genes (STRING) and Cytoscape. The prognostic value of hub genes was revealed by Kaplan-Meier Plotter. The causal relationships and interactions between proteins were displayed using DisNor. Finally, similarity analysis was conducted using the Connectivity Map (CMap) profiles to predict a group of small molecules in GC treatment.ResultsA total of 394 DEGs and 31 DEMs were identified after analysis of pre- and post-treatment samples of clinical responders to CF therapy. TM9SF4, hsa-miR-185-5p, and hsa-miR-145-5p were found to be critical in the miRNA-mRNA regulatory network. The DEGs were found to be mainly enriched in the processes of ribonucleoprotein complex assembly, catalytic activity acting on RNA, mitochondrial matrix, and thermogenesis. The DEMs were predominantly found to be involved in single-stranded RNA binding and endoplasmic reticulum lumen. HDAC5, DDX17, ILF3, and SDHC were identified as hub genes in the PPI network. Of these, HDAC5, DDX17, and ILF3 were found to be closely related to the overall survival of GC patients. DisNor identified the first neighbors of the key genes. Furthermore, CMap profiles predicted a group of small molecules, including several histone deacetylase inhibitors (HDACIs), menadione, and mibefradil, which could serve as promising therapeutic agents to reverse acquired resistance to CF therapy.ConclusionsOur findings reveal new targets and alternative therapies to overcome the acquired resistance of GC patients to CF treatment.

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Dataset Information

Identification of gene signatures used to recognize biological characteristics of gastric cancer upon gene expression data.

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Identification of gene signatures used to recognize biological characteristics of gastric cancer upon gene expression data.

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