Pharmacogenomics and Personalized Medicine (2021-01-01)

Identification of Four Genes as Prognosis Signatures in Lung Adenocarcinoma Microenvironment

  • Yao Y,
  • Zhang T,
  • Qi L,
  • Liu R,
  • Liu G,
  • Li J,
  • Sun C

Journal volume & issue
Vol. Volume 14
pp. 15 – 26

Abstract

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Yan Yao,1 Tingting Zhang,2 Lingyu Qi,2 Ruijuan Liu,3 Gongxi Liu,3 Jie Li,2 Changgang Sun3,4 1Clinical Medical Colleges, Weifang Medical University, Weifang, Shandong Province, People’s Republic of China; 2College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, People’s Republic of China; 3Department of Oncology, Weifang Traditional Chinese Hospital, Weifang, Shandong Province, People’s Republic of China; 4Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, People’s Republic of ChinaCorrespondence: Changgang SunInnovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, People’s Republic of ChinaTel +86 13583690699Email [email protected]: Tumor microenvironment (TME) cells constitute a vital element of tumor tissues. Increasing evidence has shown that immune response in the microenvironment plays an active role in tumor invasion, metastasis, and recurrence, and is an important factor affecting tumor prognosis. Our study aimed to identify the gene signatures in lung adenocarcinoma (LUAD) microenvironment for prognosis and immunotherapy.Methods: In this study, we evaluated, for the first time, the stromal and immune scores of 594 patients from The Cancer Genome Atlas (TCGA) database with LUAD using the ESTIMATE algorithm. Three hundred and sixty-seven dysregulated immune-related genes were identified. Then, we performed functional enrichment analysis of these genes, and found the best gene model and construct the signature through univariate, Lasso and multivariate COX regression analysis. To assess the independently prognostic ability of the signature, the Kaplan–Meier survival analysis and Cox’s proportional hazards model were performed.Results: Functional enrichment analysis and protein–protein interaction networks showed that the immune-related genes mainly played a role in immune response, activation/proliferation of immune-related cells, and chemokine activity. A prognostic model involving 6 genes was constructed and the signature was identified as an independent prognostic factor and significantly associated with the overall survival (OS) of LUAD. The area under curve (AUC) of the receiver operating characteristic curve (ROC curve) for the 6 genes signature in predicting the 3-year survival rate was 0.708. Finally, four genes (FOXN4, KLHL4, FAM83F and CCR2) can be used as candidate prognostic biomarkers for LUAD.Conclusion: Our findings will help evaluate the prognosis of LUAD and provide new ideas for exploring the potential relationship between TME and LUAD treatment and prognosis.Keywords: lung adenocarcinoma, tumor microenvironment, ESTIMATE algorithm, immune-related gene, prognosis signatures

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