Abstract
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.
Highlights
Lung cancer is the leading cause of cancer-related deaths [1]
In the biological process (BP), differentially expressed mRNAs (DEmRNAs) were significantly enriched in cornification, epidermis development, skin development, keratinization, extracellular structure organization, and keratinocyte differentiation
Powerful computational models to predict potential disease-related non-coding RNAs for experimental validation are helpful for in-depth interpretation of the pathogenesis and processes underlying Non-small cell lung cancer (NSCLC) development and improving related treatment strategies, and may dramatically decrease the time and expenditure on biological experiments [32,33,34,35]
Summary
Lung cancer is the leading cause of cancer-related deaths [1]. Non-small cell lung cancer (NSCLC) accounts for approximately 83% of all lung cancers [2]; its two dominant histological phenotypes include lung adenocarcinoma (LUAD, ~50%) and lung squamous cell carcinoma (LUSC, ~40%) [3]. Remarkable advances have been made for lung cancer diagnoses and treatment strategies, 60-month overall survival (OS) rate and 5-year survival rates remain poor (68% and 0%–10% at stages IB and IV, respectively) [4]. Accurate detection of NSCLC at an early stage can provide a good prognosis. Diagnostic precision can be enhanced by developing biomarkers that can accurately classify the patients according to their probable disease risk, diagnosis, and prognosis or response to treatment [8]. Functional biomarkers with known underlying mechanisms related to the disease can be used as potential therapeutic targets [8]. NSCLC patients show a positive response to immune checkpoint inhibitors (ICIs) that target programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) interaction [11]. The identification of novel molecular network biomarkers for early screen to improve prognosis and treatment in NSCLC is needed
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