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Identification of a glycolysis‐related gene signature associated with clinical outcome for patients with lung squamous cell carcinoma

BACKGROUND: Lung squamous cell carcinoma (LUSC), one of the main types of lung cancer, has caused a huge social burden. There has been no significant progress in its therapy in recent years, Resulting in a poor prognosis. This study aims to develop a glycolysis‐related gene signature to predict pati...

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Detalles Bibliográficos
Autores principales: Xu, Ziming, Zhang, Shiwei, Nian, Fulai, Xu, Shangyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8209576/
https://www.ncbi.nlm.nih.gov/pubmed/33991070
http://dx.doi.org/10.1002/cam4.3945
Descripción
Sumario:BACKGROUND: Lung squamous cell carcinoma (LUSC), one of the main types of lung cancer, has caused a huge social burden. There has been no significant progress in its therapy in recent years, Resulting in a poor prognosis. This study aims to develop a glycolysis‐related gene signature to predict patients’ survival with LUSC and explore new therapeutic targets. METHODS: We obtained the mRNA expression and clinical information of 550 patients with LUSC from the Cancer Genome Atlas (TCGA) database. Glycolysis genes were identified by Gene Set Enrichment Analysis (GSEA). The glycolysis‐related gene signature was established using the Cox regression analysis. RESULTS: We developed five glycolysis‐related genes signature (HKDC1, AGL, ALDH7A1, SLC16A3, and MIOX) to calculate each patient's risk score. According to the risk score, patients were divided into high‐ and low‐risk groups and exhibited significant differences in overall survival (OS) between the two groups. The ROC curves showed that the AUC was 0.707 for the training cohort and 0.651 for the validation cohort. Additionally, the risk score was confirmed as an independent risk factor for LUSC patients by Cox regression analysis. CONCLUSION: We built a gene signature to clarify the connection between glycolysis and LUSC. This model performs well in evaluating patients’ survival with LUSC and provides new biomarkers for targeted therapy.