ArticleBMC cancer2021
Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis.
Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 25 citations in OpenAlex.
- Prognostic biomarkers for lung adenocarcinoma based on Mendelian randomization analysis.The Journal of international medical research · 2026Article
- Identification of potential biomarkers for lung cancer using integrated bioinformatics and machine learning approaches.PloS one · 2025Article
- Hub genes identification and association of key pathways with hypoxia in cancer cells: A bioinformatics analysis.Saudi journal of biological sciences · 2023Article
- Integrative Analysis of Inflammatory Response-Related Gene for Predicting Prognosis and Immunotherapy in Glioma.Journal of molecular neuroscience : MN · 2023Article
- A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer.Scientific reports · 2023Article
- Down-regulation of FBP1 in lung adenocarcinoma cells promotes proliferation and invasion through SLUG mediated epithelial mesenchymal transformation.Translational cancer research · 2023Article
- Interfering withAnnals of translational medicine · 2022Article
- High intestinal vascular permeability in a murine model for Hirschsprung's disease: implications for postoperative Hirschsprung-associated enterocolitis.Pediatric surgery international · 2022Article
- Characteristics of circadian rhythm-related genes and establishment of a prognostic scoring system for lung adenocarcinoma with experimental verification: a bioinformatics analysis.Journal of thoracic disease · 2022Article
- Identification of differentially expressed genes at the single-cell level and prognosis prediction through bulk RNA sequencing data in breast cancer.Frontiers in genetics · 2022Article
- Integrated Bioinformatics Analysis for Identifying the Significant Genes as Poor Prognostic Markers in Gastric Adenocarcinoma.Journal of oncology · 2022Article
- Autophagy characteristics and establishment of autophagy prognostic models in lung adenocarcinoma and lung squamous cell carcinoma.PloS one · 2022Article
- miR-425 regulates lipophagy via SIRT1 to promote sorafenib resistance in liver cancer.Oncology letters · 2021Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundLung adenocarcinoma (LAC) is the predominant histologic subtype of lung cancer and has a complicated pathogenesis with high mortality. The purpose of this study was to identify differentially expressed genes (DEGs) with prognostic value and determine their underlying mechanisms.
methodsGene expression data of GSE27262 and GSE118370 were acquired from the Gene Expression Omnibus database, enrolling 31 LAC and 31 normal tissues. Common DEGs between LAC and normal tissues were identified using the GEO2R tool and Venn diagram software. Next, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to analyze the Gene Ontology and Kyoto Encyclopedia of Gene and Genome (KEGG) pathways. Then, protein-protein interaction (PPI) network of DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes and central genes were identified via Molecular Complex Detection. Furthermore, the expression and prognostic information of central genes were validated via Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier analysis, respectively. Finally, DAVID, real-time PCR and immunohistochemistry were applied to re-analyze the identified genes, which were also further validated in two additional datasets from ArrayExpress database.
resultsFirst, 189 common DEGs were identified among the two datasets, including 162 downregulated and 27 upregulated genes. Next, Gene Ontology and KEGG pathway analysis of the DEGs were conducted through DAVID. Then, PPI network of DEGs was constructed and 17 downregulated central genes were identified. Furthermore, the 17 downregulated central genes were validated via GEPIA and datasets from ArrayExpress, and 12 of them showed a significantly better prognosis. Finally, six genes were identified significantly enriched in neuroactive ligand-receptor interactions (EDNRB, RXFP1, P2RY1, CALCRL) and Rap1 signaling pathway (TEK, P2RY1, ANGPT1) via DAVID, which were further validated to be weakly expressed in LAC tissues via RNA quantification and immunohistochemistry analysis.
conclusionsThe low expression pattern and relation to prognosis indicated that the six genes were potential tumor suppressor genes in LAC. In conclusion, we identified six significantly downregulated DEGs as prognostic markers and potential tumor suppressor genes in LAC based on integrated bioinformatics methods, which could act as potential molecular markers and therapeutic targets for LAC patients.
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