Epithelial-mesenchymal transition-related lncRNAs associated with prognosis and immune cell infiltration in lung adenocarcinoma

Quncheng Zhang; Xuan Wu; Ya Sun; Li Yang; Ziqi Wang; Yuanjian Yang; Xingru Zhao; Xiaoju Zhang

Epithelial-mesenchymal transition-related lncRNAs associated with prognosis and immune cell infiltration in lung adenocarcinoma

Am J Transl Res. 2022 Oct 15;14(10):7308-7323. eCollection 2022.

Authors

Quncheng Zhang^{1

2}, Xuan Wu^{1

2

3}, Ya Sun⁴, Li Yang^{1

2

3}, Ziqi Wang^{1

2

3}, Yuanjian Yang^{1

2}, Xingru Zhao^{1

2}, Xiaoju Zhang^{1

2}

Affiliations

¹ Department of Respiratory and Critical Care Medicine, Zhengzhou University People's Hospital China.
² Henan Provincial People's Hospital Zhengzhou, China.
³ Academy of Medical Science, Zhengzhou University Zhengzhou, China.
⁴ Xinxiang Medical University Xinxiang, China.

PMID: 36398231
PMCID: PMC9641461

Abstract

Background: Lung adenocarcinoma (LUAD) remains the most common type of lung cancer and is associated with distant metastasis and poor prognosis. Epithelial-mesenchymal transition (EMT) plays crucial roles in carcinogenesis, embryogenesis, and wound healing. EMT-related molecules may be adopted for early diagnosis and prognosis of cancer and targeting them may constitute an attractive strategy for treatment. This study aims to identify the EMT-related long non-coding RNAs (lncRNAs) and develop a risk signature to accurately predict the prognosis of LUAD patients.

Methods: The RNA-seq data and corresponding clinical profiles were obtained from LUAD cohort of The Cancer Genome Atlas (TCGA) database. EMT-related lncRNAs significantly associated with prognosis were identified by Pearson correlation analysis and univariate regression analysis. Subsequently, an EMT-related prognostic risk signature was developed through LASSO and multivariate regression analyses. Kaplan Meier and receiver operating characteristic curve analysis were implemented to assess the predictive performance of the signature. The nomogram was constructed to predict the 1-year, 3-year, and 5-year overall survival of LUAD patients. Additionally, enrichment analyses were carried out to identify probable biologic processes and cellular pathways involved in the signature. The correlation of immune cell infiltration and risk score was also evaluated by CIBERSORT algorithm. Finally, we constructed a ceRNA network to further study possible downstream targets and molecular mechanisms of EMT-related lncRNAs in LUAD.

Results: Eight EMT-related lncRNAs were identified to develop a prognostic risk signature in LUAD. Patients with high-risk scores had worse survival outcomes than those with low-risk scores. The signature showed robust predictive potential, and was verified to be an independent prognostic factor. Moreover, the risk score based on the signature was significantly correlated with immune cell infiltration in LUAD.

Conclusions: We established and validated a prognostic signature that reflects the tumor microenvironment characteristics and predicts the outcomes for LUAD.

Keywords: EMT-related lncRNAs; immune cell infiltration; lung adenocarcinoma; prognosis; signature.