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

Autor: Lizhu Lin, Leihao Hu, Wei Guo, Cai-Zhi Yang, Jietao Lin, Shan Liu, Zhong-Yu Huang, Li Deng, Hong-Xing Yang, Xi Xiao, Lingling Sun
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Male
Lung Neoplasms
Epidemiology
medicine.medical_treatment
medicine.disease_cause
Biochemistry
Lung and Intrathoracic Tumors
Mathematical and Statistical Techniques
Carcinoma
Non-Small-Cell Lung
Medicine and Health Sciences
Gene Regulatory Networks
Multidisciplinary
MiRTarBase
Cancer Risk Factors
Statistics
Middle Aged
Prognosis
Survival Rate
Nucleic acids
Oncology
Area Under Curve
Long non-coding RNA
Physical Sciences
Regression Analysis
Medicine
Female
RNA
Long Noncoding
Network Analysis
Research Article
medicine.drug
Computer and Information Sciences
Science
Antineoplastic Agents
Biology
Research and Analysis Methods
Gefitinib
Diagnostic Medicine
microRNA
Biomarkers
Tumor
Genetics
medicine
Humans
RNA
Messenger
Statistical Methods
Non-coding RNA
Lung cancer
Survival analysis
Aged
Proportional Hazards Models
Natural antisense transcripts
Biology and life sciences
Competing endogenous RNA
Cancers and Neoplasms
Immunotherapy
medicine.disease
Non-Small Cell Lung Cancer
Gene regulation
MicroRNAs
ROC Curve
Drug Resistance
Neoplasm
Medical Risk Factors
Cancer research
RNA
Gene expression
Carcinogenesis
Mathematics
Zdroj: PLoS ONE, Vol 16, Iss 12, p e0260720 (2021)
PLoS ONE
ISSN: 1932-6203
Popis: 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.
Databáze: OpenAIRE
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