A Lipid Metabolism-Based Seven-Gene Signature Correlates with the Clinical Outcome of Lung Adenocarcinoma

Tianqi Li; Jiquan Chen; Jun Liu; Qingjie Chen; Wei Nie; Mi-Die Xu

doi:10.1155/2022/9913206

A Lipid Metabolism-Based Seven-Gene Signature Correlates with the Clinical Outcome of Lung Adenocarcinoma

J Oncol. 2022 Feb 11:2022:9913206. doi: 10.1155/2022/9913206. eCollection 2022.

Authors

Tianqi Li^{1

2

3}, Jiquan Chen⁴, Jun Liu⁵, Qingjie Chen⁶, Wei Nie⁷, Mi-Die Xu^{1

2

3}

Affiliations

¹ Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
² Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
³ Institute of Pathology, Fudan University, Shanghai 200032, China.
⁴ Department of Respiratory Medicine, The Third Affiliated Hospital of Naval Medical University, Shanghai 200438, China.
⁵ Department of Anesthesiology, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China.
⁶ Department of Nuclear Medicine, the First Affiliated Hospital of Nanchang University, Nanchang 200433, China.
⁷ Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China.

Abstract

Background: Herein, we tried to develop a prognostic prediction model for patients with LUAD based on the expression profiles of lipid metabolism-related genes (LMRGs).

Methods: Molecular subtypes were identified by non-negative matrix factorization (NMF) clustering. The overall survival (OS) predictive gene signature was developed and validated internally and externally based on online data sets. Time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier curve, nomogram, restricted mean survival time (EMST), and decision curve analysis (DCA) were used to assess the performance of the gene signature.

Results: We identified three molecular subtypes in LUAD with distinct characteristics on immune cells infiltration and clinical outcomes. Moreover, we confirmed a seven-gene signature as an independent prognostic factor for patients with LUAD. Calibration and DCA analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the gene signature. In addition, the nomogram showed higher robustness and clinical usability compared with four previously reported prognostic gene signatures.

Conclusions: Findings in the present study shed new light on the characteristics of lipid metabolism within LUAD, and the established seven-gene signature can be utilized as a new prognostic marker for predicting survival in patients with LUAD.