Profiling analysis of long non-coding RNA and mRNA in parathyroid carcinoma

Xiang Zhang, Ya Hu, Mengyi Wang, Ronghua Zhang, Pei Pei Wang, Ming Cui, Zhe Su, Xiang Gao, Quan Liao, Yupei Zhao

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Parathyroid carcinoma (PCa) is a rare endocrine neoplasia that typically has unfavourable outcomes. The contribution of long non-coding RNAs (lncRNAs) to the development of malignant and benign parathyroid tumours remains largely unknown. In this study, we explored transcriptomic profiling of lncRNA and mRNA expression in 6 PCa, 6 parathyroid adenoma (PAd) and 4 normal parathyroid (PaN) tissues. In total, 2641 lncRNA transcripts and 2165 mRNA transcripts were differentially expressed between PCa and PAd. Enrichment analysis demonstrated that dysregulated transcripts were involved mainly in the extracellular matrix (ECM)-receptor interaction and energy metabolism pathways. Bioinformatics analysis suggested that ATF3, ID1, FOXM1, EZH2 and MITF may be crucial to parathyroid carcinogenesis. Series test of cluster analysis segregated differentially expressed lncRNAs and mRNAs into several expression profile models, among which the 'plateau' profile representing components specific to parathyroid carcinogenesis was selected to build a co-expression network. Seven lncRNAs and three mRNAs were selected for quantitative RT-PCR validation in 16 PCa, 41 PAd and 4 PaN samples. Receiver-operator characteristic curves analysis showed that lncRNA PVT1 and GLIS2-AS1 yielded the area under the curve values of 0.871 and 0.860, respectively. Higher hybridization signals were observed in PCa for PVT1 and PAd for GLIS2-AS1. In conclusion, the current evidence indicates that PAd and PCa partially share common signalling molecules and pathways, but have independent transcriptional events. Differentially expressed lncRNAs and mRNAs have intricate interactions and are involved in parathyroid tumourigenesis. The lncRNA PVT1 and GLIS2-AS1 may be new potential markers for the diagnosis of PCa.

Original languageEnglish (US)
Pages (from-to)163-176
Number of pages14
JournalEndocrine-Related Cancer
Volume26
Issue number2
DOIs
StatePublished - Feb 2019

All Science Journal Classification (ASJC) codes

  • Endocrinology, Diabetes and Metabolism
  • Oncology
  • Endocrinology
  • Cancer Research

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