Identification of an immune signature predicting prognosis risk of patients in lung adenocarcinoma View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Qian Song, Jun Shang, Zuyi Yang, Lanlin Zhang, Chufan Zhang, Jianing Chen, Xianghua Wu

ABSTRACT

BACKGROUND: Lung cancer has become the most common cancer type and caused the most cancer deaths. Lung adenocarcinoma (LUAD) is one of the major type of lung cancer. This study aimed to establish a signature based on immune related genes that can predict patients' OS for LUAD. METHODS: The expression data of 976 LUAD patients from The Cancer Genome Atlas database (training set) and the Gene Expression Omnibus database (four testing sets) and 1534 immune related genes from the ImmPort database were used for generation and validation of the signature. The glmnet Cox proportional hazards model was used to find the best gene model and construct the signature. To assess the independently prognostic ability of the signature, the Kaplan-Meier survival analysis and Cox's proportional hazards model were performed. RESULTS: A gene model consisting of 30 immune related genes with the highest frequency after 1000 iterations was used as our signature. The signature demonstrated robust prognostic ability in both training set and testing set and could serve as an independent predictor for LUAD patients in all datasets except GSE31210. Besides, the signature could predict the overall survival (OS) of LUAD patients in different subgroups. And this signature was strongly associated with important clinicopathological factors like recurrence and TNM stage. More importantly, patients with high risk score presented high tumor mutation burden. CONCLUSIONS: This signature could predict prognosis and reflect the tumor immune microenvironment of LUAD patients, which can promote individualized treatment and provide potential novel targets for immunotherapy. More... »

PAGES

70

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Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12967-019-1824-4

DOI

http://dx.doi.org/10.1186/s12967-019-1824-4

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https://app.dimensions.ai/details/publication/pub.1112527669

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30832680


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