ArticleBioscience reports2021
Integrated analysis of ceRNA network and tumor-infiltrating immune cells in esophageal cancer.
Article in Bioscience reports, 2021. 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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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- An anoikis-related lncRNA signature may predict the prognosis, immune infiltration, and drug sensitivity in esophageal cancer.Heliyon · 2024Article
- Long Non-coding RNA LINC00342 Promotes the Proliferation, Invasion, and Migration of Primary Hepatocellular Carcinoma Cells by Regulating the Expression of miRNA-19a-3p, miRNA-545-5p, and miRNA-203a-3p.Biochemical genetics · 2024Article
- Immune-Related Long Non-Coding RNA Signature Determines Prognosis and Immunotherapeutic Coherence in Esophageal Cancer.Cancer informatics · 2024Article
- Kelch-like proteins in the gastrointestinal tumors.Acta pharmacologica Sinica · 2023Review
- Identification of CDK2-Related Immune Forecast Model and ceRNA in Lung Adenocarcinoma, a Pan-Cancer Analysis.Frontiers in cell and developmental biology · 2021Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEsophageal cancer (ESCA) is one of the most commonly diagnosed cancers in the world. Tumor immune microenvironment is closely related to tumor prognosis. The present study aimed at analyzing the competing endogenous RNA (ceRNA) network and tumor-infiltrating immune cells in ESCA.
methodsThe expression profiles of mRNAs, lncRNAs, and miRNAs were downloaded from the Cancer Genome Atlas database. A ceRNA network was established based on the differentially expressed RNAs by Cytoscape. CIBERSORT was applied to estimate the proportion of immune cells in ESCA. Prognosis-associated genes and immune cells were applied to establish prognostic models basing on Lasso and multivariate Cox analyses. The survival curves were constructed with Kaplan-Meier method. The predictive efficacy of the prognostic models was evaluated by the receiver operating characteristic (ROC) curves.
resultsThe differentially expressed mRNAs, lncRNAs, and miRNAs were identified. We constructed the ceRNA network including 23 lncRNAs, 19 miRNAs, and 147 mRNAs. Five key molecules (HMGB3, HOXC8, HSPA1B, KLHL15, and RUNX3) were identified from the ceRNA network and five significant immune cells (plasma cells, T cells follicular helper, monocytes, dendritic cells activated, and neutrophils) were selected via CIBERSORT. The ROC curves based on key genes and significant immune cells all showed good sensitivity (AUC of 3-year survival: 0.739, AUC of 5-year survival: 0.899, AUC of 3-year survival: 0.824, AUC of 5-year survival: 0.876). There was certain correlation between five immune cells and five key molecules.
conclusionThe present study provides an effective bioinformatics basis for exploring the potential biomarkers of ESCA and predicting its prognosis.
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