| Literature DB >> 32201203 |
Chaoqi Zhang1, Zhen Zhang2, Guochao Zhang1, Zhihui Zhang1, Yuejun Luo1, Feng Wang1, Sihui Wang1, Yun Che1, Qingpeng Zeng1, Nan Sun3, Jie He4.
Abstract
The prevalence of early-stage lung adenocarcinoma (LUAD) has increased alongside increased implementation of lung cancer screenings. Robust discrimination criteria are urgently needed to identify those patients who might benefit from additional systemic therapy. Here, to develop a reliable, individualized immune gene-set-based signature to predict recurrence in early-stage LUAD, a novel recurrence-associated immune signature was identified using a least absolute shrinkage and selection operator model, and a stepwise Cox proportional hazards regression model with a training set comprised of 338 early-stage LUAD samples form TCGA, which was subsequently validated in 226 cases from GSE31210 and an independent set of 68 frozen tumor samples with qRT-PCR data. This new classification system remained strongly predictive of prognoses across clinical subgroups and mutation status. Further analysis revealed that samples from high-risk cases were characterized by active interferon signal transduction, distinctive immune cell proportions and immune checkpoint profiles. Moreover, the signature was identified as an independent prognostic factor. In conclusion, the signature is highly predictive of recurrence in patients with early-stage LUAD, which may serve as a powerful prognostic tool to further optimize immunotherapies for cancer.Entities:
Keywords: EGFR mutation; Early-stage lung adenocarcinoma; Immune checkpoints; Immune signature; Recurrence
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Year: 2020 PMID: 32201203 DOI: 10.1016/j.canlet.2020.03.016
Source DB: PubMed Journal: Cancer Lett ISSN: 0304-3835 Impact factor: 8.679