microRNA expression profile and differentially-expressed genes in prolactinomas following bromocriptine treatment

Abstract

Abstract.Little is known about the function of microRNAs in prolactinomas treated with bromocriptine. The aim of the study was to explore the microRNAs associated with bromocriptinetreated prolactinomas. Six prolactinoma samples were selected according to whether they received bromocriptine treatment or not before microsurgery, and microRNA expression profiles of bromocriptine-treated and untreated prolactinomas were screened by the miRCURY LNA Array. The differentially expressed microRNAs in microarrays were further validated by stem-loop real-time PCR and subjected to gene ontology analysis and KEGG pathway analysis. In addition, related genes of microRNAs were analyzed by qRT-PCR in 15 prolactinoma samples. The initial analysis by microarrays generated a list of 80 upregulated microRNAs and 71 downregulated microRNAs in treated prolactinomas compared to untreated prolactinomas. miR-206, miR-516b and miR-550 were confirmed to be significantly upregulated, while miR-671-5p was confirmed to be significantly downregulated in treated prolactinomas by qRT-PCR. microRNA-mRNA network analysis integrating GO and KEGG pathway annotation displayed some critical factors. Platelet-derived growth factor α polypeptide (PDGFA) and bone morphogenetic protein 4 (BMP4), were verified to be differentially expressed between the two groups. PDGFA was significantly upregulated in treated prolactinomas, while BMP4 was significantly downregulated in treated prolactinomas. Our study reveals differential expression of microRNAs in prolactinoma after pharmacotherapy. Specific microRNAs may be involved in the inhibition or promotion of prolactinoma tumor growth impacted by bromocriptine pharmacotherapy. PDGFA and BMP4 may be involved in the pharmacotherapy mechanism of prolactinoma. IntroductionDopamine agonist medication is a first-line therapy in the treatment of prolactinomas. Bromocriptine can normalize the serum prolactin (PRL) level in more than 90% of cases and reduce tumor volume in approximately 85% of cases (1). Its primary mechanism of action is through the dopamine 2 (D2) receptor on the cell membrane of prolactin cells, which is selectively activated. Thus, the transcription and expression of the PRL gene and the metabolism of prolactin cells are inhibited, leading to decreased synthesis and secretion of PRL. In addition, the involution of the endoplasmic reticulum and Golgi apparatus, and the suppression of cell proliferation lead to a reduction in tumor volume (2). The subsequent pharmacotherapeutic effect of prolactinoma acts through the TGF-β and MAPK signaling pathway to inhibit lactotroph cell proliferation and induce apoptosis through D2 receptors (3-5).microRNAs are approximately 22 nucleotide, non-coding RNA generated from endogenous hairpin-shaped transcripts which post-transcriptionally regulate the expression of protein-coding genes (6) and are thought to be involved in several cellular processes regulating differentiation and development (7,8). microRNAs may regulate gene expression at the post-transcriptional l...

MicroRNA-550a Acts as a Pro-Metastatic Gene and Directly Targets Cytoplasmic Polyadenylation Element-Binding Protein 4 in Hepatocellular Carcinoma

“…Together with our results, these findings indicated that the deregulation of miR-550a could be common to several cancers and may have a functional role. Additionally, miR-550 has been found to be upregulated in prolactinomas following bromocriptine treatment [30]. Furthermore, miR-550 is among the eight microRNAs that predict sensitivity to prednisone in childhood acute lymphoblastic leukemia [29].…”

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“…In contrast to previous studies based only on miRNA or mRNA expression profiles, examining both miRNA and mRNA expression profiles enables us not only to study miRNA and mRNA expression profiles separately but also to examine miRNA-mRNA regulatory pairs together [8–12]. Nevertheless, in many cancer studies based on miRNA and mRNA expression profiles, instead of considering miRNA-mRNA regulatory pairs together, the tendency is to examine either an miRNA or mRNA first and then apply strategies such as computational miRNA target gene prediction algorithms, sequence homology analysis, or expression correlation indexes to identify the corresponding counterpart of the miRNA (mRNA) and, hence, accomplish the integration of the miRNA-mRNA pair [12, 13]. Interestingly, many of these studies share the common assumption that the regulatory relationship between an miRNA and its target mRNAs is negative, and a great deal of research is therefore based on this assumption [8–12].…”

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“…Total proteins from individual adult mice were extracted using radioimmunoassay protein extraction buffer system with protease/phosphatase inhibitors (Santa Cruz Biotechnology), denatured, separated on 12 % polyacrylamide gels and transferred to PVDF membranes (Bio- Rad) as described (Wang et al, 2015, Wang, Su, Sanai et al, 2012). Western blot analysis was performed by published methods (Wang et al, 2015, Wang et al, 2012) using primary antibodies specific to FSHβ (1:2,000; goat polyclonal antibody against human FSHβ), LHβ (1:2,000; goat polyclonal antibody against human LHβ ; Santa Cruz Biotechnology), β - tubulin (1: 2,000; rabbit polyclonal antibody, Santa Cruz Biotechnology) and appropriate secondary antibodies conjugated with HRP (Santa Cruz Biotechnology).…”

“…Western blot analysis was performed by published methods (Wang et al, 2015, Wang et al, 2012) using primary antibodies specific to FSHβ (1:2,000; goat polyclonal antibody against human FSHβ), LHβ (1:2,000; goat polyclonal antibody against human LHβ ; Santa Cruz Biotechnology), β - tubulin (1: 2,000; rabbit polyclonal antibody, Santa Cruz Biotechnology) and appropriate secondary antibodies conjugated with HRP (Santa Cruz Biotechnology). The antigen-antibody complexes were detected with an enhanced chemiluminescence kit (GE Healthcare) and the signals visualized on Biomax films (ISC Biosciences).…”