ArticlePathology oncology research : POR2017
Key Genes in Stomach Adenocarcinoma Identified via Network Analysis of RNA-Seq Data.
Article in Pathology oncology research : POR, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Article
- Use of Genome-Scale Integrated Analysis to Identify Key Genes and Potential Molecular Mechanisms in Recurrence of Lower-Grade Brain Glioma.Medical science monitor : international medical journal of experimental and clinical research · 2019Article
- Genome-wide analysis to identify a novel microRNA signature that predicts survival in patients with stomach adenocarcinoma.Journal of Cancer · 2019Article
- Comparison of tumor related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma.Thoracic cancer · 2018Article
- Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA-seq data of stomach adenocarcinoma (STAD) were analyzed to identify critical genes in STAD. Meanwhile, relevant small molecule drugs, transcription factors (TFs) and microRNAs (miRNAs) were also investigated. Gene expression data of STAD were downloaded from The Cancer Genome Atlas (TCGA). Differential analysis was performed with package edgeR. Relationships with correlation coefficient > 0.6 were retained in the gene co-expression network. Functional enrichment analysis was performed for the genes in the network with DAVID and KOBASS 2.0. Modules were identified using Cytoscape. Relevant small molecules drugs, transcription factors (TFs) and microRNAs (miRNAs) were revealed by using CMAP and WebGestalt databases. A total of 520 DEGs were identified between 285 STAD samples and 33 normal controls, including 244 up-regulated and 276 down-regulated genes. A gene co-expression network containing 53 DEGs and 338 edges was constructed, the genes of which were significantly enriched in focal adhesion, ECM-receptor interaction and vascular smooth muscle contraction pathways. Three modules were identified from the gene co-expression network and they were associated with skeletal system development, inflammatory response and positive regulation of cellular process, respectively. A total of 20 drugs, 9 TFs and 6 miRNAs were acquired that may regulate the DEGs. NFAT-COL1A1/ANXA1, HSF2-FOS, SREBP-IL1RN and miR-26-COL5A2 regulation axes may be important mechanisms for STAD.
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