Identification of a prognostic classifier based on EMT-related lncRNAs and the function of LINC01138 in tumor progression for lung adenocarcinoma

Lingyan Xiao; Yongbiao Huang; Qian Li; Sheng Wang; Li Ma; Zhijie Fan; Zhe Tang; Xianglin Yuan; Bo Liu

doi:10.3389/fmolb.2022.976878

Identification of a prognostic classifier based on EMT-related lncRNAs and the function of LINC01138 in tumor progression for lung adenocarcinoma

Front Mol Biosci. 2022 Aug 17:9:976878. doi: 10.3389/fmolb.2022.976878. eCollection 2022.

Authors

Lingyan Xiao¹, Yongbiao Huang¹, Qian Li², Sheng Wang¹, Li Ma¹, Zhijie Fan¹, Zhe Tang³, Xianglin Yuan¹, Bo Liu¹

Affiliations

¹ Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
² Department of Pathophysiology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
³ Department of Thoracic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Abstract

Purpose: This study aimed to develop a prognostic indicator based on epithelial-mesenchymal transition (EMT)-related long noncoding RNAs (lncRNAs) and explore the function of EMT-related lncRNAs in malignant progression in lung adenocarcinoma (LUAD). Materials and methods: A LUAD dataset was acquired from The Cancer Genome Atlas (TCGA) to identify prognostic EMT-related lncRNAs via differential expression analysis and univariate Cox regression analysis. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis was utilized for variable selection and model construction. The EMT-related prognostic index (ERPI) was calculated according to the model and served as a classifier to divide LUAD individuals into high-ERPI and low-ERPI groups. A nomogram incorporating ERPI and clinicopathological variables was constructed. TCGA-LUAD, GSE50081, and GSE31210 were used to test the predictive capacity of the ERPI and nomogram. The characteristics of the tumor microenvironment (TME) were evaluated via the ESTIMATE, TIMER, and ssGSEA algorithms. Gene set variation analysis (GSVA) and ssGSEA were used to annotate the functions of the high-ERPI and low-ERPI groups. CCK8, transwell assay, wound-healing assay, and clone formation assay were conducted to clarify the biological functions of prognostic EMT-related lncRNAs. Results: Ninety-seven differentially expressed EMT-related lncRNAs were identified, 15 of which were related to overall survival (OS). A prognostic signature was constructed based on 14 prognostic EMT-related lncRNAs to calculate the ERPI of each patient, and the predictive ability of ERPI was verified in TCGA, GSE50081, and GSE31210. The low-ERPI group survived longer and had a lower percentage of patients in advanced stage than the high-ERPI group. The nomogram had the highest predictive accuracy, followed by ERPI and stage. Patients with low ERPI had higher infiltration degree of immune cells and stronger immune responses than those with high ERPI. A series of in vitro experiments demonstrated that knockdown of LINC01138 dampened variability, proliferation, and motility of A549 and H460 cells. Conclusion: Our study developed a prognostic classifier with robust prognostic performance and clarified the biological functions of LINC01138 in LUAD, aiding in making individual treatments for patients with LUAD and dissecting the mechanism of oncogenesis.

Keywords: epithelial-mesenchymal transition; lncRNA; lung adenocarcinoma; prognosis; tumor microenvironment.