J Cancer 2020; 11(24):7101-7115. doi:10.7150/jca.49266 This issue
1. Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
2. Department of Occupational and Environmental Health, School of Public Health, Qingdao University, Shandong 266021, China
3. Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
4. Hubei Key Laboratory of Tumour Biological Behaviors, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
5. Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China.
*Jianguo Zhang and Jianzhong Zhang contributed equally to this work.
Background: The incidence of lung adenocarcinoma (LUAD) increased substantially in recent years. A systematic investigation of the metabolic genomics pattern is critical to improve the treatment and prognosis of LUAD. This study aimed to analyze the relationship between tumor microenvironment (TME) and metabolism-related genes of LUAD.
Methods: The data was extracted from TCGA and GEO datasets. The metabolism-related gene expression profile and the corresponding clinical data of LUAD patients were then integrated. The survival-related genes were screened out using univariate COX regression and lasso regression analysis. The latent properties and molecular mechanisms of these LUAD-specific metabolism-related genes were investigated by computational biology.
Results: A novel prognostic model was established based on 8 metabolism-related genes, including TYMS, ALDH2, PKM, GNPNAT1, LDHA, ENTPD2, NT5E, and MAOB. The immune infiltration of LUAD was also analyzed using CIBERSORT algorithms and TIMER database. In addition, the high- and low-risk groups exhibited distinct layout modes in the principal component analysis.
Conclusions: In summary, our studies identified clinically significant metabolism-related genes, which were potential signature for LUAD diagnosis, monitoring, and prognosis.
Keywords: lung adenocarcinoma, metabolic landscape, prognostic index, bioinformatics, tumor immune microenvironment