ArticlePeerJ2020
Identification of key genes associated with progression and prognosis for lung squamous cell carcinoma.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 33 citations in OpenAlex.
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- Advancing prognostic and therapeutic prediction in lung squamous cell carcinoma through integrated multi-omics analysis and 117 machine learning combinations.Journal of thoracic disease · 2025Article
- A protein-encoding CCDC7 circular RNA inhibits the progression of prostate cancer by up-regulating FLRT3.NPJ precision oncology · 2024Article
- Article
- Characterization of a prognostic model for lung squamous cell carcinoma based on eight stemness index-related genes.BMC pulmonary medicine · 2022Article
- Signatures of malignant cells and novel therapeutic targets revealed by single-cell sequencing in lung adenocarcinoma.Cancer medicine · 2022Article
- Article
- Establishment and validation of a prognostic signature for lung adenocarcinoma based on metabolism-related genes.Cancer cell international · 2021Article
- Identification of prognostic biomarkers for major subtypes of non-small-cell lung cancer using genomic and clinical data.Journal of cancer research and clinical oncology · 2020Article
- MMP12 is a Potential Predictive and Prognostic Biomarker of Various Cancers Including Lung Adenocarcinoma.Cancer control : journal of the Moffitt Cancer CenterArticle
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung squamous cell carcinoma (LUSC) is a major subtype of lung cancer with limited therapeutic options and poor clinical prognosis.
methodsThree datasets (GSE19188, GSE33532 and GSE33479) were obtained from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between LUSC and normal tissues were identified by GEO2R, and functional analysis was employed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool. Protein-protein interaction (PPI) and hub genes were identified via the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. Hub genes were further validated in The Cancer Genome Atlas (TCGA) database. Subsequently, survival analysis was performed using the Kapla-Meier curve and Cox progression analysis. Based on univariate and multivariate Cox progression analysis, a gene signature was established to predict overall survival. Receiver operating characteristic curve was used to evaluate the prognostic value of the model.
resultsA total of 116 up-regulated genes and 84 down-regulated genes were identified. These DEGs were mainly enriched in the two pathways: cell cycle and p53 signaling way. According to the degree of protein nodes in the PPI network, 10 hub genes were identified. The mRNA expression levels of the 10 hub genes in LUSC were also significantly up-regulated in the TCGA database. Furthermore, a novel seven-gene signature (FLRT3, PPP2R2C, MMP3, MMP12, CAPN8, FILIP1 and SPP1) from the DEGs was constructed and acted as a significant and independent prognostic signature for LUSC.
conclusionsThe 10 hub genes might be tightly correlated with LUSC progression. The seven-gene signature might be an independent biomarker with a significant predictive value in LUSC overall survival.
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