Comprehensive analysis of an immune infiltrate-related competitive endogenous RNA network reveals potential prognostic biomarkers for non-small cell lung cancer

• Published in: PloS one Vol. 16; no. 12; p. e0260720
• Main Authors: Yang, Cai-Zhi, Hu, Lei-Hao, Huang, Zhong-Yu, Deng, Li, Guo, Wei, Liu, Shan, Xiao, Xi, Yang, Hong-Xing, Lin, Jie-Tao, Sun, Ling-Ling, Lin, Li-Zhu
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.12.2021; Public Library of Science (PLoS)
• Subjects: Aged; Algorithms; Analysis; Antineoplastic Agents - therapeutic use; Apoptosis; Area Under Curve; Biological markers; Biology and life sciences; Biomarkers; Biomarkers, Tumor - genetics; Carcinoma, Non-Small-Cell Lung - drug therapy; Carcinoma, Non-Small-Cell Lung - genetics; Carcinoma, Non-Small-Cell Lung - mortality; Carcinoma, Non-Small-Cell Lung - pathology; Cell cycle; Chemotherapy; Cisplatin; Computer and Information Sciences; Dendritic cells; Disease; Drug resistance; Drug Resistance, Neoplasm - genetics; Epidermal growth factor; Female; Gefitinib; Gemcitabine; Gene expression; Gene Regulatory Networks - genetics; Genetic aspects; Genomes; Genotypes; Health risks; Hospitals; Humans; Immunosuppressive agents; Immunotherapy; Inhibitor drugs; Kinases; Lung cancer; Lung cancer, Non-small cell; Lung diseases; Lung Neoplasms - drug therapy; Lung Neoplasms - genetics; Lung Neoplasms - mortality; Lung Neoplasms - pathology; Lymphocytes; Male; Malignancy; Medical prognosis; Medicine; Medicine and Health Sciences; Microenvironments; MicroRNAs; MicroRNAs - metabolism; Middle Aged; miRNA; Modules; mRNA; Network analysis; Non-coding RNA; Non-small cell lung carcinoma; Oncology; Ontology; Paclitaxel; Patient outcomes; Physical Sciences; Prognosis; Proportional Hazards Models; Regression analysis; Research and Analysis Methods; Ribonucleic acid; Risk groups; RNA; RNA - metabolism; RNA, Long Noncoding - metabolism; RNA, Messenger - metabolism; ROC Curve; Small cell lung carcinoma; Survival; Survival Rate; Targeted cancer therapy; Tumorigenesis; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0260720

Globally, non-small cell lung cancer (NSCLC) is the most common malignancy and its prognosis remains poor because of the lack of reliable early diagnostic biomarkers. The competitive endogenous RNA (ceRNA) network plays an important role in the tumorigenesis and prognosis of NSCLC. Tumor immune microenvironment (TIME) is valuable for predicting the response to immunotherapy and determining the prognosis of NSCLC patients. To understand the TIME-related ceRNA network, the RNA profiling datasets from the Genotype-Tissue Expression and The Cancer Genome Atlas databases were analyzed to identify the mRNAs, microRNAs, and lncRNAs associated with the differentially expressed genes. Weighted gene co-expression network analysis revealed that the brown module of mRNAs and the turquoise module of lncRNAs were the most important. Interactions among microRNAs, lncRNAs, and mRNAs were prognosticated using miRcode, miRDB, TargetScan, miRTarBase, and starBase databases. A prognostic model consisting of 13 mRNAs was established using univariate and multivariate Cox regression analyses and validated by the receiver operating characteristic (ROC) curve. The 22 immune infiltrating cell types were analyzed using the CIBERSORT algorithm, and results showed that the high-risk score of this model was related to poor prognosis and an immunosuppressive TIME. A lncRNA–miRNA–mRNA ceRNA network that included 69 differentially expressed lncRNAs (DElncRNAs) was constructed based on the five mRNAs obtained from the prognostic model. ROC survival analysis further showed that the seven DElncRNAs had a substantial prognostic value for the overall survival (OS) in NSCLC patients; the area under the curve was 0.65. In addition, the high-risk group showed drug resistance to several chemotherapeutic and targeted drugs including cisplatin, paclitaxel, docetaxel, gemcitabine, and gefitinib. The differential expression of five mRNAs and seven lncRNAs in the ceRNA network was supported by the results of the HPA database and RT-qPCR analyses. This comprehensive analysis of a ceRNA network identified a set of biomarkers for prognosis and TIME prediction in NSCLC.