ArticleFrontiers in oncology2021
Development and Validation of an E2F-Related Gene Signature to Predict Prognosis of Patients With Lung Squamous Cell Carcinoma.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 8 citations in OpenAlex.
- Article
- Systematic integration of machine learning algorithms to develop immune escape-related signatures to improve clinical outcomes in lung adenocarcinoma patients.Frontiers in immunology · 2023Article
- Deep learning reveals cuproptosis features assist in predict prognosis and guide immunotherapy in lung adenocarcinoma.Frontiers in endocrinology · 2022Article
- Sphingolipid metabolism is associated with osteosarcoma metastasis and prognosis: Evidence from interaction analysis.Frontiers in endocrinology · 2022Article
- Development and validation of a robust necroptosis related classifier for colon adenocarcinoma.Frontiers in genetics · 2022Article
- Development and validation of a combined glycolysis and immune prognostic signature for lung squamous cell carcinoma.Frontiers in genetics · 2022Article
- Machine learning reveals two heterogeneous subtypes to assist immune therapy based on lipid metabolism in lung adenocarcinoma.Frontiers in immunology · 2022Article
- Metabolic reprogramming and crosstalk of cancer-related fibroblasts and immune cells in the tumor microenvironment.Frontiers in endocrinology · 2022Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung squamous cell carcinoma (LUSC) generally correlates with poor clinical prognoses due to the lack of available prognostic biomarkers. This study is designed to identify a potential biomarker significant for the prognosis and treatment of LUSC, so as to provide a scientific basis for clinical treatment decisions.
methodsGenomic changes in LUSC samples before and after radiation were firstly discussed to identify E2 factor (E2F) pathway of prognostic significance. A series of bioinformatics analyses and statistical methods were combined to construct a robust E2F-related prognostic gene signature. Furthermore, a decision tree and a nomogram were established according to the gene signature and multiple clinicopathological characteristics to improve risk stratification and quantify risk assessment for individual patients.
resultsIn our investigated cohorts, the E2F-related gene signature we identified was capable of predicting clinical outcomes and therapeutic responses in LUSC patients, besides, discriminative to identify high-risk patients. Survival analysis suggested that the gene signature was independently prognostic for adverse overall survival of LUSC patients. The decision tree identified the strong discriminative performance of the gene signature in risk stractification for overall survival while the nomogram demonstrated a high accuracy.
conclusionThe E2F-related gene signature may help distinguish high-risk patients so as to formulate personalized treatment strategy in LUSC patients.
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