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09 February 2020 : Laboratory Research  

Small Nucleolar RNA Host Gene 1 (SNHG1) and Chromosome 2 Open Reading Frame 48 (C2orf48) as Potential Prognostic Signatures for Liver Cancer by Constructing Regulatory Networks

Hui Zhang1A, Changhua Zhuo2A, Dong Zhou1AG*, Mingji Zhang1B, Fan Zhang1C, Minyong Chen1D, Shaohua Xu1E, Zhaoshuo Chen1F

DOI: 10.12659/MSM.920482

Med Sci Monit 2020; 26:e920482

Abstract

BACKGROUND: Liver cancer is a common malignant tumor with poor prognosis. The present study sought to identify potential signatures that can predict the prognosis of patients with liver cancer.

MATERIAL AND METHODS: The RNA sequencing (RNA-seq) and clinical information of liver cancer patients were obtained from the Cancer Genome Atlas (TCGA) database. Differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) were identified between liver cancer and adjacent normal tissues. After predicting lncRNA–miRNA and miRNA–mRNA pairs using online databases, the competing endogenous RNA (ceRNA) networks were constructed. Furthermore, the prognostic value of these differentially expressed genes was evaluated using univariate and multivariate Cox regression analyses.

RESULTS: After constructing the ceRNA network, 2 lncRNAs small nucleolar RNA host gene 1 (SNHG1) and chromosome 2 open reading frame 48 (C2orf48) with the most nodes were identified. Correlation analysis revealed that SNHG1 was correlated with miR-195 and C2orf48 was correlated with miR-195 and miR-93. High expression of SNHG1, C2orf48, and miR-93 can contribute to poorer clinical outcomes compared to low expression. Furthermore, low miR-195 expression was correlated with shorter survival time than was high expression. SNHG1 and C2orf48 were closely associated with histology grade. Univariate and multivariate Cox regression analyses confirmed that SNHG1 and C2orf48 are risk factors for liver cancer.

CONCLUSIONS: Our findings revealed that SNHG1 and C2orf48 possess potential prognostic value and should be considered as possible biomarkers for predicting clinical outcomes for patients with liver cancer.

Keywords: Gene Regulatory Networks, Liver Neoplasms, Prognosis, RNA, Long Noncoding, Cohort Studies, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, gene ontology, Kaplan-Meier Estimate, MicroRNAs, Middle Aged, Multivariate Analysis, RNA, Messenger, Risk Factors, Survival Analysis, Up-Regulation

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750