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  • Title: Integrative miRNA analysis identifies hsa-miR-3154, hsa-miR-7-3, and hsa-miR-600 as potential prognostic biomarker for cervical cancer.
    Author: Zeng Y, Wang KX, Xu H, Hong Y.
    Journal: J Cell Biochem; 2018 Feb; 119(2):1558-1566. PubMed ID: 28771797.
    Abstract:
    MicroRNAs, which can interfere with the translation of target mRNAs, caught a particular interest as their functions are related to human cancers. As the most common cancer in women, cervical cancer remains one of the leading cancer-related causes of death. In this study, we performed an integrative miRNA analysis to identify prognosis related miRNAs for cervical cancer. In addition, we developed an n-miRNA expression signature (risk score) to comprehensively assess the prognosis of cervical cancer, especially for survival time. Furthermore, we performed target predictions and functional enrichment analyses for the identified miRNAs to investigate their potential role in the development of disease. Univariate Cox regression models were used to assess the association between miRNAs and prognosis of cervical cancer. Three miRNAs were identified to be significantly associated with survival time of patients. Hsa-miR-3154 and hsa-miR-7-3 were significantly associated with shortened survival time and more death cases, whereas expression level of hsa-miR-600 was significantly associated with prolonged survival time. The function enrichment analysis showed the target genes of poor prognosis related miRNAs were mainly enriched in the mTOR pathway, whereas target genes of positive prognosis related miRNAs were mainly enriched in the AMPK pathway. In summary, a 3-miRNA expression signature was identified which can predict cervical cancer patient survival. The potential functions of this 3-miRNA expression signature and individual miRNAs as prognostic targets of cervical cancer were revealed by this study. Furthermore, these findings may have important implications in the understanding of the potential therapeutic method for the cervical cancer patients.
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