ArticleAnnals of translational medicine2021
Identification of key genes associated with esophageal adenocarcinoma based on bioinformatics analysis.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- PAQR5 drives the malignant progression and shapes the immunosuppressive microenvironment of hepatocellular carcinoma by activating the NF-κB signaling.Biomarker research · 2025Article
- Article
- Nomograms for prognosis prediction in esophageal adenocarcinoma: realities and challenges.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Article
- GABRP inhibits the progression of oesophageal cancer by regulating CFTR: Integrating bioinformatics analysis and experimental validation.International journal of experimental pathology · 2024Article
- PAQR5 inhibits the growth and metastasis of clear cell renal cell carcinoma by suppressing the JAK/STAT3 signaling pathway.Cellular oncology (Dordrecht, Netherlands) · 2023Article
- Network and pathway-based analysis of candidate genes associated with esophageal adenocarcinoma.Journal of gastrointestinal oncology · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEsophageal adenocarcinoma (EAC) is an aggressive malignancy and accounts for the majority of cancer-related death worldwide. It is often diagnosed at an advanced stage and entails a poor prognosis for those afflicted. The mechanisms of its pathogenesis and progress remain unclear and require urgent elucidation. This study aimed to identify specific genes and potential pathways associated with the progression and prognosis of EAC using bioinformatics analyses.
methodsEAC microarray datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were analyzed to identify differentially expressed genes (DEGs) using bioinformatics analysis. The DEGs in TCGA were then analyzed to construct a co-expression network by weighted correlation network analysis (WGCNA), and module-clinical trait relationships were analyzed to explore the genes that associated with clinicopathological parameters of EAC. Gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were performed for the cancer-related genes, and a DEG-based protein-protein interaction (PPI) network was used to extract hub genes through Cytoscape plugins. The consensus survival analysis for EAC (OSeac) was performed to identify the prognosis-related genes. The immune infiltration was evaluated by tumor immune estimation resource (TIMER) algorithms, and a risk score prognostic model was established using univariate, multivariate Cox proportional hazards regression, and lasso regression analysis.
resultsUltimately, 190 cancer-related DEGs were identified, 6 of which were found to play vital roles in the progression of EAC, including
conclusionsThis study identified several genes with the potential to become useful targets for the diagnosis and treatment of EAC. The 6-gene-related risk score prognostic model and nomogram based on these genes may be a reliable tool for predicting the prognosis of patients with EAC.
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