ArticleOncology letters2017
Molecular dysexpression in gastric cancer revealed by integrated analysis of transcriptome data.
Article in Oncology letters, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Prognostic value of gastric cancer-associated gene signatures: Evidence based on a meta-analysis using integrated bioinformatics methods.Journal of cellular and molecular medicine · 2018Pooled it
- Pan-cancer identified ARPC1B as a promising target for tumor immunotherapy and prognostic biomarker, particularly in READ.Heliyon · 2024Article
- Network-based identification of signature genes KLF6 and SPOCK1 associated with oral submucous fibrosis.Molecular and clinical oncology · 2020Article
- Article
- Identification of Potential Key Genes Associated With the Pathogenesis and Prognosis of Gastric Cancer Based on Integrated Bioinformatics Analysis.Frontiers in genetics · 2018Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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