Identification of a six-gene metabolic signature predicting overall survival for patients with lung adenocarcinoma

Yubo Cao; Xiaomei Lu; Yue Li; Jia Fu; Hongyuan Li; Xiulin Li; Ziyou Chang; Sa Liu

doi:10.7717/peerj.10320

Identification of a six-gene metabolic signature predicting overall survival for patients with lung adenocarcinoma

PeerJ. 2020 Dec 2:8:e10320. doi: 10.7717/peerj.10320. eCollection 2020.

Authors

Yubo Cao¹, Xiaomei Lu², Yue Li¹, Jia Fu¹, Hongyuan Li¹, Xiulin Li¹, Ziyou Chang¹, Sa Liu¹

Affiliations

¹ Department of Medical Oncology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China.
² Department of Pathophysiology, China Medical University, Shenyang, China.

Abstract

Background: Lung cancer is the leading cause of cancer-related deaths worldwide. Lung adenocarcinoma (LUAD) is one of the main subtypes of lung cancer. Hundreds of metabolic genes are altered consistently in LUAD; however, their prognostic role remains to be explored. This study aimed to establish a molecular signature that can predict the prognosis in patients with LUAD based on metabolic gene expression.

Methods: The transcriptome expression profiles and corresponding clinical information of LUAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. The differentially expressed genes (DEGs) between LUAD and paired non-tumor samples were identified by the Wilcoxon rank sum test. Univariate Cox regression analysis and the lasso Cox regression model were used to construct the best-prognosis molecular signature. A nomogram was established comprising the prognostic model for predicting overall survival. To validate the prognostic ability of the molecular signature and the nomogram, the Kaplan-Meier survival analysis, Cox proportional hazards model, and receiver operating characteristic analysis were used.

Results: The six-gene molecular signature (PFKP, PKM, TPI1, LDHA, PTGES, and TYMS) from the DEGs was constructed to predict the prognosis. The molecular signature demonstrated a robust independent prognostic ability in the training and validation sets. The nomogram including the prognostic model had a greater predictive accuracy than previous systems. Furthermore, a gene set enrichment analysis revealed several significantly enriched metabolic pathways, which suggests a correlation of the molecular signature with metabolic systems and may help explain the underlying mechanisms.

Conclusions: Our study identified a novel six-gene metabolic signature for LUAD prognosis prediction. The molecular signature could reflect the dysregulated metabolic microenvironment, provide potential biomarkers for predicting prognosis, and indicate potential novel metabolic molecular-targeted therapies.

Keywords: GEO; Lung adenocarcinoma; Metabolic signature; Overall survival; Prognostic model; TCGA.

Grants and funding

This work supported by the Science and Technology Project of Liaoning Province (No. 2019-ZD-0753 and No.2018010137-301). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.