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Transcriptional Characterization Of The Tumor Immune Microenvironment And Its Prognostic Value For Locally Advanced Lung Adenocarcinoma In A Chinese Population

Authors Chen Y, Chen H, Mao B, Zhou Y, Shi X, Tang L, Jiang H, Wang G, Zhuang W

Received 21 March 2019

Accepted for publication 12 October 2019

Published 31 October 2019 Volume 2019:11 Pages 9165—9173

DOI https://doi.org/10.2147/CMAR.S209571

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Colin Mak

Peer reviewer comments 5

Editor who approved publication: Dr Eileen O'Reilly


Yuqiao Chen,1 Huan Chen,2 Beibei Mao,2 Yuan Zhou,1 Xinying Shi,2 Lu Tang,1 Hong Jiang,3 Guo Wang,4 Wei Zhuang1

1Department of Thoracic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, People’s Republic of China; 2Beijing Genecast Biotechnology Co., Beijing 100000, People’s Republic of China; 3Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, People’s Republic of China; 4Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, People’s Republic of China

Correspondence: Wei Zhuang
Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, People’s Republic of China
Tel +86-152-0089-9869
Fax +86-731-8432-7623
Email zhuangwei@csu.edu.cn

Objective: We investigated the relationship of the transcriptional tumor immune microenvironment with prognosis of patients with locally advanced lung adenocarcinoma (LUAD).
Materials and methods: A targeted RNA-Seq approach was used to measure the abundance of 395 immune-related transcripts of 24 formalin-fixed paraffin embedded (FFPE) tumor specimens from our institution and transcription data of 85 matched LUAD samples from The Cancer Genome Atlas (TCGA). Gene set variation analysis (GSVA) was used to identify gene sets related to prognosis, and the microenvironment cell-population (MCP)-counter method was used to quantify infiltrated immune cells. Survival analysis with the log rank test was used to determine the relationships of different immune-related transcripts with prognosis. Cox proportional hazards models were also used to identify risk factors associated with poor prognosis.
Results: Among our patients, GSVA and the log rank test demonstrated that enrichment of the antigen processing pathway (P = 0.01) correlated with a favorable prognosis. MCP-counter and survival analysis demonstrated that greater CD8 T cell infiltration correlated with a favorable prognosis (P = 0.05), but greater infiltration of neutrophils (P = 0.014) and NK cells (P = 0.015) correlated with poor prognoses. Cox hazard analysis showed that greater infiltration of neutrophils was an independent risk factor for poor prognosis. These results were consistent with LUAD data from TCGA.
Conclusion: When integrated with computational bioinformatics methods, targeted RNA-Seq from FFPE specimens provides profiles of the tumor immune microenvironment that have prognostic value for patients with locally advanced LUAD.

Keywords: NSCLC, lung adenocarcinoma, RNA-Seq, tumor immune microenvironment, transcriptomic markers

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