ArticleThe Journal of international medical research2020
Bioinformatics analysis reveals 6 key biomarkers associated with non-small-cell lung cancer.
Article in The Journal of international medical research, 2020. 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.
- In silico design of the multi-epitope vaccine for lung adenocarcinoma based on hub gene-derived neoantigens.BMC cancer · 2026Article
- Targeting Kinesins for Therapeutic Exploitation of Chromosomal Instability in Lung Cancer.Cancers · 2025Review
- Prediction of lung cancer risk in Chinese population with genetic-environment factor using extreme gradient boosting.Cancer medicine · 2022Article
- Nonnegative matrix factorization-based bioinformatics analysis reveals that TPX2 and SELENBP1 are two predictors of the inner sub-consensuses of lung adenocarcinoma.Cancer medicine · 2021Article
- Identification of molecular biomarkers and pathways of NSCLC: insights from a systems biomedicine perspective.Journal, genetic engineering & biotechnology · 2021Article
- A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study.The Lancet. Digital health · 2020Article
- Bioinformatics analysis of the circRNA-miRNA-mRNA network for non-small cell lung cancer.The Journal of international medical research · 2020Article
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Authors and funding
4 authors.
Funding
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Abstract
objectiveNon-small-cell lung cancer (NSCLC) accounts for >85% of lung cancers, and its incidence is increasing. We explored expression differences between NSCLC and normal cells and predicted potential target sites for detection and diagnosis of NSCLC.
methodsThree microarray datasets from the Gene Expression Omnibus database were analyzed using GEO2R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were conducted. Then, the String database, Cytoscape, and MCODE plug-in were used to construct a protein-protein interaction (PPI) network and screen hub genes. Overall and disease-free survival of hub genes were analyzed using Kaplan-Meier curves, and the relationship between expression patterns of target genes and tumor grades were analyzed and validated. Gene set enrichment analysis and receiver operating characteristic curves were used to verify enrichment pathways and diagnostic performance of hub genes.
resultsIn total, 293 differentially expressed genes were identified and mainly enriched in cell cycle, ECM-receptor interaction, and malaria. In the PPI network, 36 hub genes were identified, of which 6 were found to play significant roles in carcinogenesis of NSCLC:
conclusionThe identified target genes can be used as biomarkers for the detection and diagnosis of NSCLC.
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