Identification of a novel glycolysis-related gene signature that can predict the survival of patients with lung adenocarcinoma. Issue 5 (4th March 2019)

• Record Type: Journal Article
• Authors: Liu, Chang; Li, Yinyan; Wei, Minjie; Zhao, Lin; Yu, Yangyang; Li, Guang
• Is Part Of: Cell cycle. Volume 18:Issue 5(2019)
• Page Start: 568
• Page End: 579
• Publication Date: 2019-03-04
• Subjects: Lung cancer -- glycolysis -- prognostic -- mRNAs -- survival; Cell cycle -- Periodicals; 571.84377
• DOI: 10.1080/15384101.2019.1578146
• Languages: English
• ISSNs: 1538-4101

Abstract

ABSTRACT: Lung cancer is one of the most malignant cancers worldwide, and lung adenocarcinoma (LUAD) is the most common histologic subtype. Thousands of biomarkers related to the survival and prognosis of patients with this cancer type have been investigated through database mining; however, the prediction effect of a single gene biomarker is not satisfactorily specific or sensitive. Thus, the present study aimed to develop a novel gene signature of prognostic values for patients with LUAD. Using a data-mining method, we performed expression profiling of 1145 mRNAs in large cohorts with LUAD (n = 511) from The Cancer Genome Atlas database. Using the Gene Set Enrichment Analysis, we selected 198 genes related to GLYCOLYSIS, which is the most important enrichment gene set. Moreover, these genes were identified using Cox proportional regression modeling. We established a risk score staging system to predict the outcome of patients with LUAD and subsequently identified four genes ( AGRN, AKR1A1, DDIT4, and HMMR ) that were closely related to the prognosis of patients with LUAD. The identified genes allowed us to classify patients into the high-risk group (with poor outcome) and low-risk group (with better outcome). Compared with other clinical factors, the risk score has a better performance in predicting the outcome of patients with LUAD, particularly in the early stage of LUAD. In conclusion, we developed a four-gene signature related to glycolysis by utilizing the Cox regression model and a risk staging model for LUAD, which might prove valuable for the clinical management of patients with LUAD.