ArticleMedicine2020
Identification of crucial genes correlated with esophageal cancer by integrated high-throughput data analysis.
Article in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- Immune cell related signature predicts prognosis in esophageal squamous cell carcinoma based on single-cell and bulk-RNA sequencing.Frontiers in oncology · 2024Article
- A pan-cancer analysis of pituitary tumor-transforming 3, pseudogene.American journal of translational research · 2023Article
- Inhibitory Effects ofEvidence-based complementary and alternative medicine : eCAM · 2022Article
- NRAGE Confers Radiation Resistance in 2D and 3D Cell Culture and Poor Outcome in Patients With Esophageal Squamous Cell Carcinoma.Frontiers in oncology · 2022Article
- Single-cell transcriptome profiling reveals intratumoural heterogeneity and malignant progression in retinoblastoma.Cell death & disease · 2021Article
- LINC00958 promotes bladder cancer carcinogenesis by targeting miR-490-3p and AURKA.BMC cancer · 2021Article
- Article
- Identification of Key Genes Associated With the Process of Hepatitis B Inflammation and Cancer Transformation by Integrated Bioinformatics Analysis.Frontiers in genetics · 2021Article
- Codon usage bias analysis of genes linked with esophagus cancer.Bioinformation · 2021Article
- Identification of Hub Genes Associated With Immune Infiltration and Predict Prognosis in Hepatocellular Carcinoma via Bioinformatics Approaches.Frontiers in genetics · 2020Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEsophageal cancer (ESCA) is one of the most deadly malignancies in the world. Although the management and treatment of patients with ESCA have improved, the overall 5-year survival rate is still very poor.
methodsThe study aimed to identify potential key genes associated with the pathogenesis and prognosis of ESCA. In the study, integrated bioinformatics methods were used to screen differentially expressed genes (DEGs) between ESCA and normal tissue in the data set of gene expression profiles. The hub gene in DEGs was further analyzed by protein-protein interaction (PPI) network and survival analysis to explore its relationship with the pathogenesis and poor prognosis of ESCA.
results134 up-regulated genes and 183 down-regulated genes were obtained in ESCA compared with normal tissues. Moreover, the PPI network was established with 176 nodes and 800 interactions. Ten hub genes (AURKA, CDC20, BUB1, TOP2A, ASPM, DLGAP5, TPX2, CENPF, UBE2C, and NEK2) were filtered out based on the degree value. Functional enrichment analysis indicated that a variety of extracellular related items and ECM-receptor interaction pathway were all correlated with the ESCA.
conclusionsThe results of this study would provide some guidance for further study of diagnostic and prognostic biomarkers to promote ESCA treatment.
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