An integrated transcriptomic and computational analysis for biomarker identification in gastric cancer

Cui, Juan; Chen, Yunbo; Chou, Wen Chi; Sun, Liankun; Chen, Li; Suo, Jian; Ni, Zhaohui; Zhang, Ming; Xiao-xia, Kong; Hoffman, Lisabeth L.; Kang, Jinsong; Su, Yingying; Olman, Victor; Johnson, David; Tench, Daniel W.; Amster, I. Jonathan; Orlando, Ron; Puett, David; Fan, Li; Xu, Ying

doi:10.1093/nar/gkq960

Cited by 181 publications

(140 citation statements)

References 36 publications

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“…The results of GO and KEGG analyses showed that the most enriched pathways included protein digestion and absorption, ECM-receptor interaction, and the metabolism of xenobiotics by cytochrome P450. The results were consistent with previous research (35,36), which identified DEGs with biological functions that were mainly involved in cell adhesion and ECM interactions. …”

Section: Discussionsupporting

confidence: 93%

Molecular dysexpression in gastric cancer revealed by integrated analysis of transcriptome data

Dong

et al. 2017

Oncology Letters

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Abstract. Gastric cancer (GC) is often diagnosed in the advanced stages and is associated with a poor prognosis. Obtaining an in depth understanding of the molecular mechanisms of GC has lagged behind compared with other cancers. This study aimed to identify candidate biomarkers for GC. An integrated analysis of microarray datasets was performed to identify differentially expressed genes (DEGs) between GC and normal tissues. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then performed to identify the functions of the DEGs. Furthermore, a protein-protein interaction (PPI) network of the DEGs was constructed. The expression levels of the DEGs were validated in human GC tissues using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A set of 689 DEGs were identified in GC tissues, as compared with normal tissues, including 202 upregulated DEGs and 487 downregulated DEGs. The KEGG pathway analysis suggested that various pathways may play important roles in the pathology of GC, including pathways related to protein digestion and absorption, extracellular matrix-receptor interaction, and the metabolism of xenobiotics by cytochrome P450. The PPI network analysis indicated that the significant hub proteins consisted of SPP1, TOP2A and ARPC1B. RT-qPCR validation indicated that the expression levels of the top 10 most significantly dysexpressed genes were consistent with the illustration of the integrated analysis. The present study yielded a reference list of reliable DEGs, which represents a robust pool of candidates for further evaluation of GC pathogenesis and treatment.

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Section: Discussionsupporting

confidence: 93%

Molecular dysexpression in gastric cancer revealed by integrated analysis of transcriptome data

Dong

et al. 2017

Oncology Letters

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“…Two data sets were included: GSE27342 and GSE33335. GSE27342 consisted of 80 paired gastric cancer and normal adjacent tissue, including 4 stage Ⅰ, 7 stage Ⅱ, 54 stage Ⅲ and 7 stage Ⅳ [10,11] . All samples were taken from three hospitals affiliated with Jilin University College of Medicine and Jilin Provincial Cancer Hospital, Changchun, China.…”

Section: Microarray Datamentioning

confidence: 99%

Analysis of long non-coding RNA expression profiles in gastric cancer

Cao

Wu²,

He³

et al. 2013

WJG

179

159

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AIM:To investigate the expression patterns of long non-coding RNAs (lncRNAs) in gastric cancer. METHODS:Two publicly available human exon arrays for gastric cancer and data for the corresponding normal tissue were downloaded from the Gene Expression Omnibus (GEO). We re-annotated the probes of the human exon arrays and retained the probes uniquely mapping to lncRNAs at the gene level. LncRNA expression profiles were generated by using robust multi-array average method in affymetrix power tools. The normalized data were then analyzed with a Bioconductor package linear models for microarray data and genes with adjusted P -values below 0.01 were considered differentially expressed. An independent data set was used to validate the results. RESULTS:With the computational pipeline established to re-annotate over 6.5 million probes of the Affymetrix Human Exon 1.0 ST array, we identified 136053 probes uniquely mapping to lncRNAs at the gene level. These probes correspond to 9294 lncRNAs, covering nearly 76% of the GENCODE lncRNA data set. By analyzing GSE27342 consisting of 80 paired gastric cancer and normal adjacent tissue samples, we identified 88 lncRNAs that were differentially expressed in gastric cancer, some of which have been reported to play a role in cancer, such as LINC00152, taurine upregulated 1, urothelial cancer associated 1, Pvt1 oncogene, small nucleolar RNA host gene 1 and LINC00261. In the validation data set GSE33335, 59% of these differentially expressed lncRNAs showed significant expression changes (adjusted P -value < 0.01) with the same direction. CONCLUSION:We identified a set of lncRNAs differentially expressed in gastric cancer, providing useful information for discovery of new biomarkers and therapeutic targets in gastric cancer. © 2013 Baishideng. All rights reserved.Key words: Long non-coding RNA; Gastric cancer; Microarray analysis; Data mining Core tip: Long non-coding RNAs (lncRNAs) have risen to prominence with important roles in a broad range of biological processes. LncRNA expression patterns and their biological functions in gastric cancer still remain unknown. We re-annotated the probes from an Affymetrix Human Exon 1.0 ST array and identified probes uniquely mapping to lncRNAs at the gene level. These probes correspond to 9294 lncRNAs, covering nearly 76% of the GENCODE lncRNA data set. We identified a set of lncRNAs that were differentially expressed in gastric cancer. In an independent data set, 59% of these differentially expressed lncRNAs showed significant expression changes with the same direction.

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“…Gastric cancer is the fourth most common type of cancer and the second-highest cause of cancer-related mortality, worldwide (1). Approximately 8% of newly diagnosed malignant tumors originate in the stomach, and >700,000 people succumb to gastric cancer annually (2).…”

Section: Introductionmentioning

confidence: 99%

Expression analysis of apolipoprotein E and its associated genes in gastric cancer

Shi

Wang

et al. 2015

Oncology Letters

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Abstract. Gastric cancer is a common type of cancer worldwide, and has a poor prognosis, in part due to the low rates of early diagnosis and the limited treatment methods available. Apolipoprotein E (ApoE) is involved in exogenous cholesterol transport and may be important in enabling tumor cells to fulfill their high cholesterol requirements. A number of reports have indicated that ApoE affects the development and prognosis of gastric cancer. Therefore, the aim of the present study was to investigate the genes and transcription factors that interact with ApoE during the development of gastric cancer. Using gene expression profiling, the BioGRID database and the transcriptional regulatory element database, gene expression and regulatory networks in gastric cancer tissues and adjacent normal tissues were analyzed. The data demonstrated that eight genes associated with ApoE were differentially expressed, with six of these upregulated and two downregulated. Functionally, these genes were involved in the JAK-STAT cascade, acute-phase response, acute inflammatory response, and the steroid hormone response. Among these ApoE-associated genes, expression of the signal transducer and activator of transcription 2 (STAT2) and STAT3 transcription factors was upregulated. To the best of our knowledge, this is the first study to demonstrate the network of ApoE-related genes and transcription factors in gastric cancer. Additional studies are required in order to confirm these data and to translate the results into the identification of clinical biomarkers and novel treatment strategies for gastric cancer.

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An integrated transcriptomic and computational analysis for biomarker identification in gastric cancer

Cited by 181 publications

References 36 publications

Molecular dysexpression in gastric cancer revealed by integrated analysis of transcriptome data

Molecular dysexpression in gastric cancer revealed by integrated analysis of transcriptome data

Analysis of long non-coding RNA expression profiles in gastric cancer

Expression analysis of apolipoprotein E and its associated genes in gastric cancer

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