Prognostic Implication of a Metabolism-Associated Gene Signature in Lung Adenocarcinoma

Lulu He; Jiaxian Chen; Feng Xu; Jun Li; Jun Li

doi:10.1016/j.omto.2020.09.011

Prognostic Implication of a Metabolism-Associated Gene Signature in Lung Adenocarcinoma

Mol Ther Oncolytics. 2020 Oct 4:19:265-277. doi: 10.1016/j.omto.2020.09.011. eCollection 2020 Dec 16.

Authors

Lulu He¹, Jiaxian Chen¹, Feng Xu², Jun Li³, Jun Li^{1

4}

Affiliations

¹ State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
² Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
³ Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
⁴ Precision Medicine Center of Taizhou Central Hospital, Taizhou University Medical School, Taizhou 318000, China.

Abstract

Lung cancer is the most common cancer worldwide, leading to high mortality each year. Metabolic pathways play a vital role in the initiation and progression of lung cancer. We aimed to establish a prognostic prediction model for lung adenocarcinoma (LUAD) patients based on a metabolism-associated gene (MTG) signature. Differentially expressed (DE)-MTGs were screened from The Cancer Genome Atlas (TCGA) LUAD cohorts. Univariate Cox regression analysis was performed on these DE-MTGs to identify genes significantly correlated with prognosis. Least absolute shrinkage and selection operator (LASSO) regression was performed on the resulting genes to establish an optimal risk model. Survival analysis was used to assess the prognostic ability of the model. The prognostic value of the gene signature was further validated in independent Gene Expression Omnibus (GEO) datasets. A gene signature with 13 metabolic genes was identified as an independent prognostic factor. Kaplan-Meier survival analysis demonstrated the good performance of the risk model in both TCGA training and GEO validation cohorts. Finally, a nomogram incorporating clinical parameters and the metabolic gene signature was constructed to help individualize outcome predictions. The calibration curves showed excellent agreement between the actual and predicted survival.

Keywords: TCGA; gene signature; lung adenocarcinoma; metabolism; prognosis.