ArticleOncology letters2019
Screening and identification of key biomarkers in lung squamous cell carcinoma by bioinformatics analysis.
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 15 citations in OpenAlex.
- Article
- Liquid biopsy-derived extracellular vesicle protein biomarkers for diagnosis and prognostic assessment of lung squamous cell carcinoma.Cancer cell international · 2025Article
- SEC24D gene as a biomarker in human cancers and its association with CD8+ T cell immune cell infiltration.American journal of translational research · 2023Article
- Identification of prognosis-related lncRNAs and cell validation in lung squamous cell carcinoma based on TCGA data.Frontiers in oncology · 2023Article
- LINC00152 induced by TGF-β promotes metastasis via HuR in lung adenocarcinoma.Cell death & disease · 2022Article
- Sputum Microbiome Composition in Patients with Squamous Cell Lung Carcinoma.Life (Basel, Switzerland) · 2022Article
- [European journal of nuclear medicine and molecular imaging · 2022Article
- Evaluation of the Prognostic Value of Long Noncoding RNAs in Lung Squamous Cell Carcinoma.Journal of oncology · 2022Article
- Identification of prognostically relevant gene signatures in lung squamous cell carcinoma by Lung Cancer Explorer.Translational cancer research · 2021Article
- Article
- Identification of Core Prognosis-Related Candidate Genes in Chinese Gastric Cancer Population Based on Integrated Bioinformatics.BioMed research international · 2020Article
- Identification of Key Genes in Gastric Cancer by Bioinformatics Analysis.BioMed research international · 2020Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The high mortality rate of lung squamous cell carcinoma (LUSC) is in part due to the lack of early detection of its biomarkers. The identification of key molecules involved in LUSC is therefore required to improve clinical diagnosis and treatment outcomes. The present study used the microarray datasets GSE31552, GSE6044 and GSE12428 from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were conducted to construct the protein-protein interaction network of DEGs and hub genes module using STRING and Cytoscape. The 67 DEGs identified consisted of 42 upregulated genes and 25 downregulated genes. The pathways predicted by KEGG and GO enrichment analyses of DEGs mainly included cell cycle, cell proliferation, glycolysis or gluconeogenesis, and tetrahydrofolate metabolic process. Further analysis of the University of California Santa Cruz and ONCOMINE databases identified 17 hub genes. Overall, the present study demonstrated hub genes that were closely associated with clinical tissue samples of LUSC, and identified TYMS, CCNB2 and RFC4 as potential novel biomarkers of LUSC. The findings of the present study contribute to an improved understanding of the molecular mechanisms of carcinogenesis and progression of LUSC, and assist with the identification of potential diagnostic and therapeutic targets of LUSC.
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