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Unique metastasis-associated lncRNA signature optimizes prediction of tumor relapse in lung adenocarcinoma.

ABSTRACT: BACKGROUND:Local relapses and metastases are primary causes of death in lung cancer patients. In the present study, we aimed to develop a prognostic signature based on metastasis-associated lncRNAs in patients with lung adenocarcinoma (LUAD). METHODS:Firstly, the potential metastasis-associated lncRNAs were identified by analyzing high-throughput data from The Cancer Genome Atlas (TCGA), and based on which, an lncRNA signature was constructed for prediction of relapse in LUAD patients using Cox proportional hazards regression analysis. Moreover, the prognostic performance of the lncRNA signature was evaluated using Kaplan-Meier survival analysis, time-dependent receiver operating characteristic (ROC) curve and Cox analysis, respectively. In addition, the potential metastasis-associated function of these six lncRNAs was confirmed by lncRNA over-expression or depletion and in vitro transwell assays in LUAD cells. RESULTS:An lncRNA signature consisting of six most important prognostic factors (LINC01819, ZNF649-AS1, HNF4A-AS1, FAM222A-AS1, LINC02323 and LINC00672) was developed. The signature was an independent predictor for patients' relapse-free survival (RFS), which could provide higher tumor relapse prediction capability compared with the TNM staging system at three years and five years, respectively (P = 0.0209 and P = 0.0468). Furthermore, the combination of this lncRNA signature and TNM stage had better prognostic value than TNM stage alone at three and five years, respectively (P = 0.0006 and P = 0.0096). Additionally, all the lncRNAs of the signature had a regulatory role in the LUAD cell mobility. CONCLUSIONS:This novel six-lncRNA signature had considerable prognostic value for prediction of relapse in LUAD patients. KEY POINTS:Significant findings of the study The unique metastasis-associated lncRNA signature was related to tumor metastasis and prognosis in LUAD patients. What this study adds This signature had considerable prognostic value for prediction of relapse in LUAD patients.

SUBMITTER: Zhang X

PROVIDER: S-EPMC7049496 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Unique metastasis-associated lncRNA signature optimizes prediction of tumor relapse in lung adenocarcinoma.

Zhang Xiaoshi X Han Jingyi J Du Lutao L Li Xiaoli X Hao Jing J Wang Lili L Zheng Guixi G Duan Weili W Xie Yujiao Y Zhao Yinghui Y Zhang Xin X Zou Mingjin M Wang Chuanxin C

Thoracic cancer 20200128 3

<h4>Background</h4>Local relapses and metastases are primary causes of death in lung cancer patients. In the present study, we aimed to develop a prognostic signature based on metastasis-associated lncRNAs in patients with lung adenocarcinoma (LUAD).<h4>Methods</h4>Firstly, the potential metastasis-associated lncRNAs were identified by analyzing high-throughput data from The Cancer Genome Atlas (TCGA), and based on which, an lncRNA signature was constructed for prediction of relapse in LUAD pati ...[more]

PMID: 31994347

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Project description:Pyroptosis, defined as programmed cell death, results in the release of inflammatory mediators. Recent studies have revealed that pyroptosis plays essential roles in antitumor immunity and immunotherapy efficacy. Long noncoding RNAs (lncRNAs) are involved in a variety of biological behaviors in tumor cells, although the roles and mechanisms of lncRNAs in pyroptosis are rarely studied. Our study aimed to establish a novel pyroptosis-related lncRNA signature as a forecasting tool for predicting prognosis and ascertaining immune value. Based on lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA), we performed Pearson's correlation analysis to identify pyroptosis-related lncRNAs. After differentially expressed gene analysis and univariate Cox regression analysis, we selected prognosis-related and differentially expressed lncRNAs. Finally, we performed multivariate Cox regression analysis to establish the three pyroptosis-related lncRNA signature. Kaplan-Meier (KM) survival analyses and receiver operating characteristic (ROC) curves indicated the excellent performance for predicting the prognosis of LUAD patients. At the same time, we applied multidimensional approaches to further explore the functional enrichment, tumor microenvironment (TME) landscape, and immunotherapy efficacy among the different risk groups. A nomogram was constructed by integrating risk scores and clinical characteristics, which was validated using calibrations and ROC curves. Three lncRNAs, namely, AC090559.1, AC034102.8, and AC026355.2, were involved in this signature and used to classify LUAD patients into low- and high-risk groups. Overall survival time (OS) was higher in the low-risk group than in the high-risk group, which was also validated in our LUAD cohort from Shandong Provincial Hospital. TME landscape analyses revealed that a higher abundance of infiltrating immune cells and a greater prevalence of immune-related events existed in the low-risk group. Meanwhile, higher expression of immune checkpoint (ICP) genes, higher immunophenoscore (IPSs), and greater T cell dysfunction in the low-risk group demonstrated a better response to immunotherapy than the high-risk group. Combined with predictions from the Tumor Immune Dysfunction and Exclusion (TIDE) website, we found that LUAD patients in the low-risk group significantly benefited from programmed cell death 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA4) immune checkpoint blockade (ICB) therapy compared with those in the high-risk group. Furthermore, drug susceptibility analysis identified potential sensitive chemotherapeutic drugs for each risk group. In this study, a novel three pyroptosis-related lncRNA signature was constructed, which could accurately predict the immunotherapy efficacy and prognosis in LUAD patients.

Dataset Information

Unique metastasis-associated lncRNA signature optimizes prediction of tumor relapse in lung adenocarcinoma.

Publications

Unique metastasis-associated lncRNA signature optimizes prediction of tumor relapse in lung adenocarcinoma.

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