ArticleOncology letters2018
Identification of crucial genes associated with esophageal squamous cell carcinoma by gene expression profile analysis.
Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 19 citations in OpenAlex.
- Research advances of MAL family members in tumorigenesis and tumor progression (Review).Molecular medicine reports · 2024Review
- Ubiquitin‑conjugating enzymes as potential biomarkers and therapeutic targets for digestive system cancers (Review).Oncology reports · 2023Article
- CKAP2L Promotes Esophageal Squamous Cell Carcinoma Progression and Drug-Resistance by Modulating Cell Cycle.Journal of oncology · 2022Article
- Article
- Overexpression of UBE2C in esophageal squamous cell carcinoma tissues and molecular analysis.BMC cancer · 2021Article
- The MAL Protein, an Integral Component of Specialized Membranes, in Normal Cells and Cancer.Cells · 2021Review
- Coronary artery disease: differential expression of ceRNAs and interaction analyses.Annals of translational medicine · 2021Article
- Codon usage bias analysis of genes linked with esophagus cancer.Bioinformation · 2021Article
- The Xmas-2 Homologues, the Main Component of the TREX-2 mRNA Export Complex.Doklady. Biochemistry and biophysics · 2020Article
- Identification and Characterization of Copy Number-Associated Driver Genes in Esophageal Squamous Cell Carcinoma.BioMed research international · 2020Article
- Identification of Differentially Expressed Genes and miRNAs Associated with Esophageal Squamous Cell Carcinoma by Integrated Analysis of Microarray Data.BioMed research international · 2020Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To uncover the genes associated with the development of esophageal squamous cell carcinoma (ESCC), an ESCC microarray dataset was used to identify genes differentially expressed between ESCC and normal control tissues. The dataset GSE17351 was downloaded from the Gene Expression Omnibus, containing 5 tumor esophageal mucosa samples and 5 adjacent normal esophageal mucosa samples from 5 male patients with ESCC. The differentially expressed genes (DEGs) were identified using the Linear Models for Microarray Data R package. Then, a co-expression network was constructed using the Weighted Correlation Network Analysis (WGCNA) package, and co-expression network modules were obtained with a hierarchical clustering algorithm. Additionally, functional enrichment analyses for DEGs in the top 2 modules with the highest significance were respectively conducted using the WGCNA package and the cluster Profiler package. In total, 487 upregulated and 468 downregulated DEGs were identified. A total of 24 modules were obtained from the co-expression network, and the top 2 modules with the highest significance, designated as 'blue4' and 'magenta', were further analyzed. In the module blue4, DEGs were significantly enriched in a number of Gene Ontology terms, including 'spindle organization' [e.g., ubiquitin conjugating enzyme E2 C (
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