Literature DB >> 30098429

Comprehensive and in-depth analysis of microRNA and mRNA expression profile in salivary adenoid cystic carcinoma.

Nannan Han1, Hao Lu2, Zun Zhang3, Min Ruan4, Wenjun Yang2, Chenping Zhang2.   

Abstract

OBJECTIVES: To conduct an integrated analysis of microRNA and mRNA expression profile and further discover vital molecules to uncover novel pathogenic mechanisms in salivary adenoid cystic carcinoma (SACC).
MATERIALS AND METHODS: MicroRNA and mRNA expression profiles were obtained from six paired primary SACC tumors and corresponding adjacent normal glands using high-throughput next-generation sequencing technology followed by an overall integrated bioinformatics analysis and subsequently molecular biology techniques validation.
RESULTS: Compared with adjacent noncancerous normal gland, 2107 significant differentially expressed mRNA were determined in SACC. Gene ontology and KEGG pathway analysis suggested that the differentially expressed genes were relevant to many significant biological implications. Venn diagram analysis of differentially expressed genes in different group identified 29 differentially expressed overlapping mRNA. 40 differentially expressed microRNAs were also identified in SACC. Furthermore, integrated analysis of microRNA and mRNA expression profiles recognized a core microRNA-mRNA regulatory network and unmasked many novel genes including SCUBE3, CA6, hsa-miR-885-5p and other molecules which may play an essential role in the carcinogenesis of SACC. Also, Q-PCR and immunohistochemistry results reveal the high expression and distribution of SCUBE3 in SACC and dual luciferase reporter assay also preliminarily validated that SCUBE3 was a target of hsa-miR-885-5p.
CONCLUSION: Contemporary microRNA/mRNA analysis have uncovered many mRNAs and microRNAs worthy further exploration in SACC. These are bound to help us shed light on the overall genetic background of SACC and further elucidate the potential molecular mechanism of SACC.
Copyright © 2018 Elsevier B.V. All rights reserved.

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Year:  2018        PMID: 30098429     DOI: 10.1016/j.gene.2018.08.023

Source DB:  PubMed          Journal:  Gene        ISSN: 0378-1119            Impact factor:   3.688


  2 in total

1.  PretiMeth: precise prediction models for DNA methylation based on single methylation mark.

Authors:  Jianxiong Tang; Jianxiao Zou; Xiaoran Zhang; Mei Fan; Qi Tian; Shuyao Fu; Shihong Gao; Shicai Fan
Journal:  BMC Genomics       Date:  2020-05-15       Impact factor: 3.969

2.  Transcriptome analyses identify hub genes and potential mechanisms in adenoid cystic carcinoma.

Authors:  Hong-Bing Liu; Guan-Jiang Huang; Meng-Si Luo
Journal:  Medicine (Baltimore)       Date:  2020-01       Impact factor: 1.817

  2 in total

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