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International Journal of Clinical and Experimental Pathology logoLink to International Journal of Clinical and Experimental Pathology
. 2017 Jul 1;10(7):7511–7527.

Lowered levels of microRNA-129 and potential signaling pathways in papillary thyroid carcinoma: a determination of microRNA sequencing in 507 patients and bioinformatics analysis

Liang Liang 1,#, Yihuan Luo 1,#, Xia Yang 3, Rui Zhang 3, Hanlin Wang 3, Hong Yang 2, Yun He 2, Gang Chen 3, Wei Ma 3,*, Junqiang Chen 1,*
PMCID: PMC6965262  PMID: 31966595

Abstract

Papillary thyroid carcinoma (PTC) is one of the most common endocrine system malignancies. However, the mechanism of tumor development is unclear. microRNA-129-5p is a microRNA that plays an important role in the development of tumors. The main purpose of our article is to find the potential target genes of microRNA-129 and their pathways based on gene array, sequencing and bioinformatics studies. We obtained microRNA-129 expression and clinical associations in the TCGA database. In addition, we found a microRNA-129-related chip GSE19933, which is overexpressing microR-129-5p in thyroid cancer cell lines. The down-regulated gene is considered to be a potential target gene for microRNA-129. The target genes were predicted through 12 online tools. We performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of all down-regulated and predicted target genes. Furthermore, protein-protein interactions (PPI) were also analyzed for all potential genes. Finally, with intersecting down-regulated genes by overexpressed microRNA-129 and predicted target genes, the 889 genes are mainly enriched in the calcium signaling pathway, cGMP-PKG signaling pathway, ErbB signaling pathway and Proteoglycans in cancer, etc. The role of ten hub genes is particularly prominent in PPI analysis. These genes are differentially expressed in the thyroid by immunohistochemistry. We confirmed that microRNA-129 may play a major role in PTC through the above pathways, but more experiments are still needed to prove our results.

Keywords: microRNA-129, papillary thyroid carcinoma, TCGA, target gene, signaling pathways

Introduction

Thyroid cancer is a common disease and its diagnosis remains challenging. The symptoms of a palpation, examination by ultrasound, detection of endocrine hormone, fine needle aspiration (FNA) and pathological observation play crucial parts in the diagnosis of thyroid carcinomas. However, the specificity and sensitivity are all required to be improved [1-4]. Papillary thyroid carcinoma (PTC) is the most common cancer originating from the thyroid. Although the prognosis of PTC is commonly good with a high 5-year survival rate, some patients have poorer prognosis. An early diagnosis is of great importance in clinical settings [5-8]. Recently, a class of non-coding single-stranded RNA molecule coded by an endogenous gene, microRNAs (miRNAs) has been reported to be evidently related to the incidence and progress of many cancers, including PTC [9-13]. Hence, miRNAs possess the potential to be trustworthy biomarkers in PTC. However, only a small number of miRNAs have been studied in PTC.

MicroRNA-129 is one of the miRNAs whose clinical role and function remain largely unknown in PTC. To the best of our knowledge, only two research groups have performed relevant studies on microRNA-129 in thyroid cancer. Brest et al. [14] found that histone deacetylase inhibitors (HDACi)s, trichostatin A and vorinostat, could enhance microRNA-129-5p expression in cultured cell lines of BCPAP, TPC-1, 8505C, and CAL62, as well as in primary cultures of PTC cells. Moreover, microRNA-129 was adequate to induce cell death and accentuate the anti-proliferative effects of other cancer drugs. However, no clinical value or molecular target genes of microRNA-129 in PTC were identified in the study [14]. Duan et al. [15] found that microRNA-129-5p was apparently down-regulated in medullary thyroid carcinomas. And microRNA-129-5p could suppress the RET proto-oncogene expression by directly binding its 3’-untranslated regions. However, the study by Duan et al. solely focused on medullary subtype of thyroid carcinoma, without mentioning the more frequent papillary subtype. Besides, only one target gene, RET, was identified.

Therefore, in the present study, the expression level of microRNA-129 was analyzed based on the microRNA sequencing data from the Cancer Genome Atlas (TCGA) Research Network databases, which has recently published a molecular signature based on of 507 PTC and 59 matched non-cancerous adjacent tissues with regard to genomic, transcriptomic and proteomic characteristics, as well as clinicopathological features including survival status [16,17]. Subsequently, the potential target genes of microRNA-129 were gathered by both microarray data and predicting platforms. Finally, a comprehensive functional annotation and validation of the proteins were also performed using different in silico tools.

Materials and methods

Clinical significance of microRNA-129 in papillary thyroid carcinoma based on microRNA sequencing data

The expression levels of microRNA-129-1 and microRNA-129-2, as well as the clinical data of papillary thyroid carcinoma patients and non-tumorous thyroid tissues were provided publically by TCGA data portal (https://gdc-portal.nci.nih.gov/). The expressions of microRNAs were log2 transformed and records were considered as censored when the expression level was less than one. Student’s t test was performed examine the difference of microRNA-129-1 and microRNA-129-2 between cancerous tissues and their non-cancerous counterparts. Receiver operating characteristic (ROC) was drawn to evaluate the diagnostic values of microRNA-129-1 and microRNA-129-2. Kaplan-Meier analysis and univariate Cox proportional hazards regression model were used to assess the prognostic value of microRNA. Overall survival (OS), relapse free survival (RFS) and metastasis free survival data were obtained from PROGmiRV2-Pan Cancer miRNA Prognostics Database (http://xvm145.jefferson.edu/progmir/index.php). SPSS 22.0 (SPSS Inc., Chicago, IL, USA) was used for the statistics and P<0.05 was regarded as being significant.

Potential target genes of microRNA-129

To collect the potential target genes of microRNA-129, we combined two parts of genes together.

The first part was the differentially expressed genes (DEGs) post pre-microRNA-129 overexpression from Gene Expression Omnibus (GEO) database. After searching for “miR-129 OR microRNA-129” in thyroid cancer in GEO, we obtained the database of GSE19933. In the study [14], three independent experiments were performed in dye-swap with TCP1 PTC cells: miRNA-129-5p versus miR-Neg. The experiments included a negative pre-miRNA as control and a synthetic pre-miRNA-129-5p as experimental group. TCP1 papillary thyroid carcinoma cells were transfected with 10 nM of a synthetic pre-miRNA-129-5p or a negative pre-miRNA using Lipofectamine RNAiMAX reagent. RNA samples were harvested at 24 and 48 hours post-transfection. The results of three experiments were merged and the down-expressed genes with a log(FC) value less than -0.5 were gathered as the genes which were directly influenced by microRNA-129 and possessed the potential to be the targets of microRNA-129 in PTC.

The second part was the predicted target genes achieved from 12 different predicting tools, including Targetscan miRWalk, miRMap, Microt4, miRanda, mirbridge, miRDB, miRNAMap, PITA, Pictar2, RNA22, and RNAhybrid. The genes co-predicted by at least six databases were selected.

Finally, the overlapped genes from the two parts were considered as potential target genes of microRNA-129 in PTC.

Functional annotation of the target genes of microRNA-129

To evaluate the function of microRNA-129 target genes in PTC, we analyzed the Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation through DAVID (http://david.abcc.ncifcrf.gov/). The GO terms with a modified Fisher Exact P-value less than 0.01 and the KEGG pathways with P-value less than 0.05 were chosen for next analysis. The GO enrichment analysis was visualized by software Cytoscape v3.5.0. Further, the protein-protein interactions network of genes was conducted through STRING v10.0 (http://string.embl.de/).

Validation of the protein expression level of potential targets

To further confirm the relationship between microRNA-129 and some of the potential target genes. The hub genes from the most significantly enriched pathway in KEGG analysis were selected randomly. We examined the protein expression of these hub genes by Proteinatlas (http://www.proteinatlas.org/). If immunohistochemistry was performed in both non-tumorous tissue and papillary thyroid carcinoma for a certain protein, the immunostaining was presented.

Results

The lowered levels of microRNA-129 in papillary thyroid carcinoma based on microRNA sequencing data

Altogether, there were 507 cases of PTC and 59 non-cancerous thyroid counterparts included in the current study. The level of microRNA-129-1 in papillary thyroid carcinoma tissues was slightly lower than that in non-tumorous tissues. However, the P-value was only 0.082. The AUC also indicated a suboptimal diagnostic value. Regarding microRNA-129-2, a significant lowered level could be observed in the cases of PTC as compared to the counterparts (P=0.049). Furthermore, the AUC was superior to that of microRNA-129-1 (0.588, P=0.027, Figure 1). The interest of the prognostic value of microRNA-129 was also followed subsequently. The K-M curves suggested that the patients with higher microRNA-129-1 and -129-2 levels tended to have a slightly better metastasis free survival (MFS) than those with lower levels, however, the P-value did not reach the significantly statistical standard. Either, no noticeable correlations could be observed between microRNA-129-1 or microRNA-129-2 and OS or RFS (Figures 2, 3 and 4).

Figure 1.

Figure 1

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Clinical value of microRNA-129 in papillary thyroid carcinoma based on microRNA sequencing data from TCGA. Expression levels of microRNA-129-1 (A) and microRNA-129-2 (B). Receiver Operating Characteristic (ROCs) curve of microRNA-129-1 (C) and microRNA-129-2 (D). NT: non-tumorous tissue, T: tumor. AUC: area under the ROC curve.

Figure 2.

Figure 2

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Correlation between microRNA-129 level and overall survival in papillary thyroid carcinoma based on microRNA sequencing data from TCGA. Expression levels of microRNA-129-1 (A) and microRNA-129-2 (B) were divided according to the median level. The analysis was performed by “PROGmiR” (http://www.compbio.iupui.edu/progmir).

Figure 3.

Figure 3

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Correlation between microRNA-129 level and relapse free survival in papillary thyroid carcinoma based on microRNA sequencing data from TCGA. Expression levels of microRNA-129-1 (A) and microRNA-129-2 (B) were divided according to the median level. The analysis was performed by “PROGmiR” (http://www.compbio.iupui.edu/progmir).

Figure 4.

Figure 4

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Correlation between microRNA-129 level and metastasis free survival in papillary thyroid carcinoma based on microRNA sequencing data from TCGA. Expression levels of microRNA-129-1 (A) and microRNA-129-2 (B) were divided according to the median level. The analysis was performed by “PROGmiR” (http://www.compbio.iupui.edu/progmir).

Identification of the potential targets of microRNA-129

To further study the potential role of microRNA-129 in PTC, we selected the overlapped genes from two main sources. The dataset GSE19933 represented DEG profiling by microarray post microRNA-129 overexpression, from which we gathered 8378 DEGs after the process described in Materials and Methods. We also obtained 3273 genes as predicted targets of microRNA-129 from at least 6 among 12 predicting platforms. Only genes overlapped by both sources were pooled for next evaluation. Finally, 889 genes were considered as potential targets of microRNA-129 in PTC.

Functional annotation of microRNA-129 target genes

To clarify the function of microRNA-129 in PTC, we conducted gene-annotation enrichment and the KEGG pathway annotation by DAVID. Regarding the biological process in GO analysis, the potential targets of microRNA-129 were significantly involved in the pathways of transcription from RNA polymerase II promoter, negative regulation of cell proliferation, and protein dephosphorylation, etc (Table 1). For cellular component, the genes were most enriched in cytoplasm, nucleus, and nucleoplasm, etc (Table 1). And for molecular function, the selected genes were markedly involved in transcription factor activity, protein binding and sequence-specific DNA binding, etc (Table 1). Regarding the KEGG pathway analysis, the potential targets of microRNA-129 were notably associated with Calcium signaling pathway, cGMP-PKG signaling pathway, ErbB signaling pathway and Proteoglycans in cancer, etc (Table 2). The network analyses of GO-terms and KEGG pathway were visualized by Cytoscape (Figures 5, 6). The protein-protein interaction network analysis was conducted based on the top four pathways from KEGG (Figure 7), which included Calcium signaling pathway, Glioma pathway, Prostate cancer pathway and cGMP-PKG signaling pathway.

Table 1.

GO functional annotation of microRNA-129-5p target genes

ID GO Term Count P Value Genes
Biological Process
    GO:0006366 Transcription from RNA polymerase II promoter 70 1.3E-14 MEF2C, MEF2A, BACH2, ARID4A, STAT5B, MITF, CCNT1, PAX2, TMF1,etc
    GO:0045944 Positive regulation of transcription from RNA polymerase II promoter 104 7.0E-14 MEF2C, MEF2A, STAT5B, RORA, JAG1, GDNF, IL11, GATA2, MYOCD, etc
    GO:0045893 Positive regulation of transcription, DNA-templated 56 3.7E-08 MEF2C, E2F3, MITF, RORA, PAX2, RFXAP, ZIC2, FUBP3, MYOCD, etc
    GO:0006351 Transcription, DNA-templated 144 2.9E-07 MEF2C, FHIT, MEF2A, JDP2, ZNF451, BBX, CNOT2, ZXDB, RORA, etc
    GO:0008285 Negative regulation of cell proliferation 43 1.8E-06 DLC1, CYP1B1, TFAP4, ERBB4, E2F7, STRN, SOX4, CBFA2T3, MYOCD, etc
    GO:0006470 Protein dephosphorylation 21 2.9E-06 PTPN9, PDP2, PTPN3, SSH1, CDC14B, EPM2A, STYX, SSH2, PTPN14, etc
    GO:0000122 Negative regulation of transcription from RNA polymerase II promoter 63 9.0E-06 MEF2C, JDP2, MEF2A, BACH2, FGF9, E2F7, DICER1, NR6A1, MITF, etc
    GO:0000186 Activation of MAPKK activity 12 9.5E-06 MAP3K7, EGFR, BRAF, ZAK, PLCG1, MAP3K1, KIDINS220, MAP3K13, etc
    GO:0030279 Negative regulation of ossification 7 6.9E-05 MEF2C, NOTCH1, SFRP1, SMAD6, BCL2, CHSY1, SOX9
    GO:0000165 MAPK cascade 29 8.3E-05 MEF2C, FGFR1, MEF2A, ERBB4, FGF9, GRB2, FGF13, IL17RD, GDNF, etc
    GO:0035335 Peptidyl-tyrosine dephosphorylation 16 8.5E-05 PTPRJ, PTPN9, PTPN3, SSH1, CDC14B, PTPN2, SSH2, PTPN14, DUSP10, etc
    GO:0006355 Regulation of transcription, DNA-templated 105 1.5E-04 MEF2C, FHIT, MEF2A, BBX, CNOT2, ZXDB, HOXD1, RORA, RCBTB1, etc
    GO:0008284 Positive regulation of cell proliferation 42 1.9E-04 FGFR1, E2F3, ERBB4, FGF9, PKHD1, NAP1L1, SOX4, ESM1, SOX9, etc
    GO:0035019 Somatic stem cell population maintenance 12 2.8E-04 ZFP36L2, GATA2, BRAF, SFRP1, EPAS1, SOX2, SMAD4, SOX4, SOX9, etc
    GO:0007399 Nervous system development 29 3.8E-04 MEF2C, ERBB4, FGF13, JAG1, NRN1, GDNF, SEMA5A, PCSK2, ROBO1, etc
    GO:0001654 Eye development 8 4.8E-04 ADAMTS18, ATF6, HMGB1, HIPK1, FGF9, SOX2, SIX3, SIPA1L3
    GO:0043410 Positive regulation of MAPK cascade 13 5.3E-04 FGFR1, HMGB1, FGF9, SOX2, CDH2, IL6R, KDR, IL11, GRM4, ADRB3, etc
    GO:0001525 Angiogenesis 24 5.7E-04 PIK3CG, FGFR1, EMCN, CYP1B1, EPAS1, FGF9, LEPR, MMP19, RORA, etc
    GO:0071364 Cellular response to epidermal growth factor stimulus 8 8.9E-04 EGFR, ZFP36L2, BAG4, PLCG1, STAT5B, PAX2, SOX9, PTPN12
    GO:0001822 Kidney development 13 9.1E-04 KIF3A, PKHD1, SOX11, ARID5B, MME, RRM2B, BCL2L11, CDKN1C, etc
    GO:0010629 Negative regulation of gene expression 17 1.0E-03 XDH, PLAG1, MEF2C, STC2, LDLR, RBL2, SOX11, CCR1, NDFIP1, etc
    GO:0003357 Noradrenergic neuron differentiation 4 1.1E-03 PHOX2B, HAND2, SOX11, SOX4
    GO:0010468 Regulation of gene expression 14 1.1E-03 PHOX2B, DNMT3A, IL7, SOX2, CDK6, FAM46A, GDNF, TAPBP, DDX46, etc
    GO:0031175 Neuron projection development 14 1.1E-03 HMGB1, GNAO1, LYN, VAPA, UBE4B, DOCK7, RB1, GDNF, CAPZB, etc
    GO:0018108 Peptidyl-tyrosine phosphorylation 18 1.3E-03 EGFR, FGFR1, IL3, LYN, ERBB4, FGF9, STAT5B, TTN, EPHB1, KDR, etc
    GO:0043066 Negative regulation of apoptotic process 38 1.6E-03 ERBB4, PKHD1, STAT5B, SOX9, PAX2, GDNF, TMF1, PHIP, IGF1R, etc
    GO:0060314 Regulation of ryanodine-sensitive calcium-release channel activity 6 1.8E-03 JPH4, NOS1, PLN, CAMK2D, PDE4D, CALM1
    GO:0046777 Protein autophosphorylation 19 1.9E-03 EGFR, FGFR1, LYN, ERBB4, MAK, TAOK3, STK17B, SMG1, PRKX, etc
    GO:0007601 Visual perception 21 2.0E-03 MYO5A, OPA3, CYP1B1, IRX5, EPAS1, PITPNA, SIX3, KCNJ10, PAX2, etc
    GO:0071560 Cellular response to transforming growth factor beta stimulus 9 2.3E-03 MEF2C, ZFP36L2, SFRP1, FYN, PDE3A, XCL1, YES1, SOX9, WWOX
    GO:0071456 Cellular response to hypoxia 13 2.4E-03 DNMT3A, ACAA2, KCND2, EPAS1, STC2, RORA, SLC2A4, SFRP1, GNB1, etc
    GO:0007169 Transmembrane receptor protein tyrosine kinase signaling pathway 13 2.4E-03 EGFR, MPZL1, LYN, ERBB4, PTPRT, KDR, IGF1R, FYN, ROR1, JAK1, etc
    GO:0045668 Negative regulation of osteoblast differentiation 8 2.5E-03 TWSG1, HDAC4, NOTCH1, SFRP1, HAND2, CDK6, TOB1, IGFBP5
    GO:0007223 Wnt signaling pathway, calcium modulating pathway 8 2.5E-03 MAP3K7, GNAO1, GNB1, GNG2, FZD3, PPP3CA, CAMK2A, CALM1
    GO:0045892 Negative regulation of transcription, DNA-templated 40 2.5E-03 ARID4A, TFAP4, PAX2, SOX9, CBFA2T3, CBFA2T2, ZIC2, MINA, etc
    GO:0043547 Positive regulation of GTPase activity 44 2.5E-03 DLC1, ALS2, SNX9, FGFR1, ERBB4, GRB2, FGF9, MYO9B, MYO9A, etc
    GO:0048706 Embryonic skeletal system development 7 2.9E-03 HOXC6, SLC35D1, FGF9, SP3, HOXB9, HOXD1, PCSK5
    GO:0038083 Peptidyl-tyrosine autophosphorylation 8 2.9E-03 IGF1R, ERBB4, LYN, FYN, JAK1, YES1, ABL2, KDR
    GO:0006357 Regulation of transcription from RNA polymerase II promoter 36 3.2E-03 GLIS3, HMGB1, JDP2, SOX21, ARID4A, KIAA1958, MITF, ARID4B, etc
    GO:0048839 Inner ear development 8 3.3E-03 MAF, PHOX2B, CDKN1B, FREM2, SOX2, PDGFRB, SLC25A27, PTPN11
    GO:0060045 Positive regulation of cardiac muscle cell proliferation 6 3.6E-03 MEF2C, FGFR1, NOTCH1, ERBB4, FGF9, FGF2
    GO:0060021 Palate development 11 3.7E-03 MEF2C, CHD7, MSX1, HAND2, BNC2, ARID5B, FOXF2, GDF11, SMAD4, etc
    GO:0010001 Glial cell differentiation 5 3.8E-03 PHOX2B, CDH2, PAX2, FGF2, NFIB
    GO:0001658 Branching involved in ureteric bud morphogenesis 8 3.8E-03 LHX1, BCL2, SMAD4, WNT9B, PAX2, SOX9, GDNF, FGF2
    GO:0010976 Positive regulation of neuron projection development 12 3.9E-03 FGFR1, LYN, FYN, AVIL, PRKCI, PTN, NFE2L2, KIDINS220, etc
    GO:0030155 Regulation of cell adhesion 8 4.4E-03 PTPRJ, EMCN, ROCK1, EPHA8, PPP1R12A, SOX9, ABL2, PRKX
    GO:0048485 Sympathetic nervous system development 5 5.0E-03 PHOX2B, HAND2, SOX11, SOX4, GDNF
    GO:0046928 Regulation of neurotransmitter secretion 5 5.0E-03 CPLX4, MEF2C, FMR1, RIMS1, CAMK2A
    GO:0060397 JAK-STAT cascade involved in growth hormone signaling pathway 5 5.0E-03 LYN, STAT5B, PTPN1, STAT3, GHR
    GO:0046903 Secretion 4 5.4E-03 NPY2R, PRKCI, TPD52, RIMS1
    GO:1903358 Regulation of Golgi organization 4 5.4E-03 MYO5A, STX18, RAB33B, USP6NL
    GO:0007173 Epidermal growth factor receptor signaling pathway 9 5.5E-03 EGFR, CBLB, CAMLG, PLCG1, GRB2, TGFA, SOCS5, SOX9, PTPN11
    GO:0046854 Phosphatidylinositol phosphorylation 12 5.9E-03 PIK3CG, EGFR, FGFR1, ERBB4, FYN, GRB2, FGF9, PDGFRB, SMG1, etc
    GO:0008584 Male gonad development 12 5.9E-03 NCOA1, TNFSF10, SFRP1, FGF9, BCL2, ARID5B, GATA4, RNF38, etc
    GO:0060216 Definitive hemopoiesis 5 6.4E-03 TAL1, ZFP36L2, GATA2, HIPK1, SP3
    GO:0030900 Forebrain development 8 6.5E-03 DLC1, TWSG1, NOTCH1, SSTR2, GNAO1, FYN, SOX2, FRS2
    GO:0007267 Cell-cell signaling 23 6.6E-03 IL3, MPZL1, FGF9, IL7, GRB2, CCR1, PCDH8, FGF13, IL11, GJA3, etc
    GO:0017148 Negative regulation of translation 9 6.8E-03 TIA1, FMR1, CNOT2, SYNCRIP, FXR2, TOB1, PURA, IGFBP5, FXR1
    GO:0001501 Skeletal system development 15 7.0E-03 HAPLN1, FGFR1, SOX11, SOX4, MEPE, SOX9, ANKH, HOXC10, CDKN1C, etc
    GO:0071407 Cellular response to organic cyclic compound 9 7.5E-03 CDKN1B, CYP1B1, ITGA6, PAK2, PAK3, SMAD5, STAT3, TMF1, IGFBP5
    GO:0010863 Positive regulation of phospholipase C activity 4 7.8E-03 FGFR1, PDGFRB, ABL2, FGF2
    GO:2000737 Negative regulation of stem cell differentiation 4 7.8E-03 ZFP36L2, NOTCH1, JAG1, STAT3
    GO:0050679 Positive regulation of epithelial cell proliferation 9 8.3E-03 EGFR, NOTCH1, KIF3A, SFRP1, FGF9, TGFA, PAX2, SOX9, FOXP1
    GO:0072332 Intrinsic apoptotic signaling pathway by p53 class mediator 6 8.9E-03 FHIT, E2F2, TP53BP2, EDA2R, PERP, WWOX
    GO:0030522 Intracellular receptor signaling pathway 7 9.5E-03 NOTCH2, NCOA1, NR4A2, RORA, HNF4G, STAT3, ARNT
Cellular Component
    GO:0005737 Cytoplasm 314 3.3E-08 MEF2C, DLC1, FHIT, MEF2A, PITPNA, DZIP1, STAT5B, BBX, AQP4, etc
    GO:0005634 Nucleus 319 2.0E-07 MEF2C, DLC1, FHIT, MEF2A, DZIP1, STAT5B, SYNCRIP, RORA, RFXAP, etc
    GO:0005654 Nucleoplasm 182 6.2E-07 MEF2C, MEF2A, DZIP1, STAT5B, IDE, BBX, SYNCRIP, RORA, IL17RD, etc
    GO:0005667 Transcription factor complex 24 3.1E-05 E2F2, E2F3, MEF2A, EPAS1, RBL2, SMAD6, E2F7, SMAD5, SOX2, etc
    GO:0043005 Neuron projection 25 2.7E-04 MYO5A, HMGB1, SLC6A2, FGF13, CXADR, HNMT, ANK3, BCL11B, AVIL, etc
    GO:0005794 Golgi apparatus 63 3.8E-04 IER3IP1, SYT4, LDLR, PAX2, IL17RD, SLC35A3, SPRY4, TMF1, etc
    GO:0043235 Receptor complex 16 7.8E-04 EGFR, FGFR1, OLR1, ERBB4, LDLR, LEPR, LRP1B, PEX5L, ITPR2, etc
    GO:0017053 Transcriptional repressor complex 10 8.7E-04 HMGB1, HDAC4, CTBP2, TFAP4, ARID4A, SP3, JAZF1, HDGF, YWHAB, PHF12
    GO:0005886 Plasma membrane 228 1.5E-03 VAPA, SYT4, AQP4, MAP3K7, ATP2B3, GRIN2D, RAPGEF4, GNG2, etc
    GO:0005829 Cytosol 186 2.7E-03 ALS2, DLC1, MEF2C, FHIT, STAT5B, EIF5, IDE, STYX, CNOT2, TTN, etc
    GO:0030425 Dendrite 28 3.8E-03 GNAZ, ALS2, SYT4, DICER1, KCNA1, MME, FGF13, EPHB1, ALCAM, etc
    GO:0005768 Endosome 21 4.0E-03 EGFR, HMGB1, SNX9, LEPROTL1, LDLR, GRB2, RAB4A, MYO1D, PRKCI, etc
    GO:0016323 Basolateral plasma membrane 18 4.2E-03 EGFR, MPZ, ERBB4, LDLR, LEPR, MYO1D, AQP4, KCNJ10, IL6R, etc
    GO:0016529 Sarcoplasmic reticulum 7 4.3E-03 XDH, ANK1, NOS1, ATP2A2, ANK3, MRVI1, CAMK2D
    GO:0045202 Synapse 18 4.4E-03 CPLX4, EGFR, NMNAT2, FLRT2, NOS1, FMR1, KCNA1, DLGAP4, MME, etc
    GO:0030054 Cell junction 35 4.8E-03 CPLX4, SYT4, KCNA1, NRN1, CXADR, RIMS1, AMPH, GRIN2D, GOPC, etc
    GO:0044853 Plasma membrane raft 4 6.7E-03 PRKAR2A, KCND2, MYO1D, CDH2
    GO:0043234 Protein complex 31 9.7E-03 MEF2C, ALS2, DZIP1, MITF, NR3C1, SOX9, PAX2, CXADR, HAND2, etc
Molecular Function
    GO:0001077 Transcriptional activator activity, RNA polymerase II core promoter proximal region sequence-specific binding 43 1.9E-13 MEF2C, CAMTA1, MEF2A, TFAP4, STAT5B, NR6A1, SOX2, MITF, SOX4, etc
    GO:0003700 Transcription factor activity, sequence-specific DNA binding 100 5.2E-13 MEF2C, MEF2A, JDP2, ZNF451, STAT5B, RORA, RFXAP, ZIC2, HOXC6, etc
    GO:0005515 Protein binding 504 1.5E-08 MEF2C, DLC1, ALS2, FHIT, MEF2A, LTBP2, SYNCRIP, STRN, RORA, etc
    GO:0043565 Sequence-specific DNA binding 54 2.2E-07 JDP2, MEF2A, NR6A1, RORA, HOXD1, HOXC6, HOXC9, GATA4, FOXF2, etc
    GO:0000978 RNA polymerase II core promoter proximal region sequence-specific DNA binding 42 2.3E-07 MEF2C, JDP2, MEF2A, TFAP4, NR6A1, MITF, CTCFL, NR3C1, MYBL1, etc
    GO:0000977 RNA polymerase II regulatory region sequence-specific DNA binding 30 2.9E-07 MEF2C, MEF2A, E2F7, RORA, TAL1, GATA2, HAND2, GATA4, FOXF2, etc
    GO:0005088 Ras guanyl-nucleotide exchange factor activity 20 2.5E-06 EGFR, FGFR1, IL3, ERBB4, FGF9, GRB2, GDNF, ADRB1, RASGRF2, etc
    GO:0004725 Protein tyrosine phosphatase activity 18 5.7E-06 PTPRJ, PTPN9, PTPN3, SSH1, CDC14B, PTPN2, EPM2A, SSH2, PTPN14, etc
    GO:0003677 DNA binding 119 1.7E-05 MEF2C, MEF2A, BBX, RORA, RFXAP, ZIC2, HOXC6, GATA4, FOXF2, etc
    GO:0044212 Transcription regulatory region DNA binding 25 1.1E-04 MEF2C, ARID4A, ERBB4, TFAP4, SMAD6, ARID5B, CCNT1, SOX2, etc
    GO:0004713 Protein tyrosine kinase activity 18 2.4E-04 EGFR, FGFR1, IL3, LYN, ERBB4, FGF9, STAT5B, TTN, KDR, IGF1R, etc
    GO:0001105 RNA polymerase II transcription coactivator activity 9 3.2E-04 MEF2A, NCOA1, MYOCD, SOX11, PYGO2, SOX4, POU3F2, ISL1, WWOX
    GO:0003682 Chromatin binding 36 3.6E-04 MEF2C, JDP2, MEF2A, CCNT1, STAT5B, SOX9, TAL1, GATA2, CHD7, etc
    GO:0019899 Enzyme binding 32 4.0E-04 HLCS, TTN, TAL1, DAOA, GSTM3, ANK1, PPP1R3B, CNTNAP2, HSPA5, etc
    GO:0046934 Phosphatidylinositol-4,5-bisphosphate 3-kinase activity 11 7.4E-04 PIK3CG, EGFR, FGFR1, ERBB4, FYN, GRB2, FGF9, PDGFRB, FGF2, etc
    GO:0042803 Protein homodimerization activity 56 7.7E-04 ALS2, SEPHS1, IDE, NR6A1, HLCS, TPD52, GDNF, GSTM3, BHLHB9, etc
    GO:0005516 Calmodulin binding 21 8.7E-04 MYO5A, EGFR, NOS1, PHKG2, MYO1D, STRN, EEA1, MYO9B, TTN, etc
    GO:0044325 Ion channel binding 15 1.1E-03 NOS1, LYN, KCNAB1, FMR1, FGF13, PDE4D, RIMS1, PPP1R9A, YWHAH, etc
    GO:0046982 Protein heterodimerization activity 38 2.2E-03 MEF2C, MEF2A, JDP2, SEPHS1, TFAP4, VAPA, SOX4, TPD52, RRAGD, etc
    GO:0001158 Enhancer sequence-specific DNA binding 6 2.2E-03 GATA2, SOX11, GATA4, ZNF395, ISL1, SOX9
    GO:0005545 1-phosphatidylinositol binding 6 2.2E-03 WDFY3, SNX9, WDFY1, SESTD1, EEA1, SNX10
    GO:0001222 Transcription corepressor binding 4 3.4E-03 SIX3, HDGF, PHF12, RORA
    GO:0004709 MAP kinase activity 6 3.5E-03 MAP3K7, EGFR, BRAF, ZAK, MAP3K1, MAP3K13
    GO:0033613 Activating transcription factor binding 6 3.5E-03 MEF2C, MEF2D, HDAC4, MEF2A, HAND2, GATA4
    GO:0001047 Core promoter binding 10 3.5E-03 E2F2, HDAC4, NOTCH1, E2F3, MTF1, ELK4, E2F7, TFAP2C, RB1, CLOCK
    GO:0042826 Histone deacetylase binding 13 3.8E-03 MEF2C, MEF2A, TFAP4, YWHAB, PKN2, SIX3, HDAC4, MEF2D, TAL1, etc
    GO:0000981 RNA polymerase II transcription factor activity, sequence-specific DNA binding 18 4.0E-03 MEF2C, MEF2A, SOX21, EPAS1, KIAA1958, SIX3, FOXJ3, SOX9, etc
    GO:0030971 Receptor tyrosine kinase binding 8 4.3E-03 CBLB, PLCG1, PTPN2, PTPN14, PTPN1, SOCS5, TOB1, PTPN11
    GO:0003705 Transcription factor activity, RNA polymerase II distal enhancer sequence-specific binding 10 4.4E-03 MEF2A, PKNOX1, TFAP4, FOXF2, RFX3, OLIG2, SOX9, FOXP1, ARNT, PURA
    GO:0005178 Integrin binding 13 4.8E-03 EGFR, LYN, ADAM23, PTPN2, EDIL3, ESM1, CXADR, ECM2, COL5A1, etc
    GO:0004726 Non-membrane spanning protein tyrosine phosphatase activity 4 5.3E-03 PTPN9, PTPN2, PTPN12, PTPN11
    GO:0004721 Phosphoprotein phosphatase activity 8 5.5E-03 PPP1R3B, SSH1, PPP2CB, SSH2, DUSP10, PPP3CA, PTPN12, PTPN11
    GO:0008134 Transcription factor binding 25 6.0E-03 E2F2, HMGB1, SP100, EPAS1, CCNT1, KEAP1, RB1, RORA, PAX2, etc
    GO:0001228 Transcriptional activator activity, RNA polymerase II transcription regulatory region Sequence-specific binding 12 6.2E-03 MAF, GATA2, MSX1, PKNOX1, MYOCD, FOXF2, GATA4, ESRRG, SMAD4, etc
    GO:0046872 Metal ion binding 124 8.8E-03 MKRN1, PDP2, SYT4, SLC6A2, DZIP1, ZNF451, IDE, ZXDB, ZIC2, etc
    GO:0003713 Transcription coactivator activity 22 9.4E-03 SP100, CTBP2, TFAP4, UBE3A, MAK, TAF4B, ARID5B, SIX3, RB1, etc
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Note: GO, Gene Ontology.

Table 2.

KEGG pathway enriched by microRNA-129-5p target genes

ID GO Term Count P Value Genes
hsa04020 Calcium signaling pathway 28 1.2E-06 ERBB4, PPP3R2, ADRB3, HRH1, ATP2B3, GRIN2D, CAMK2D, PPP3CA, etc
hsa05214 Glioma 16 1.4E-06 PIK3CG, EGFR, E2F2, E2F3, BRAF, GRB2, CDK6, RB1, IGF1R, PLCG1, etc
hsa05215 Prostate cancer 17 1.7E-05 PIK3CG, EGFR, FGFR1, E2F2, E2F3, BRAF, GRB2, CREB5, RB1, etc
hsa04022 cGMP-PKG signaling pathway 24 3.0E-05 MEF2C, PIK3CG, MEF2A, ROCK1, MRVI1, PPP3R2, PDE3A, CREB5, etc
hsa04012 ErbB signaling pathway 16 6.1E-05 PIK3CG, EGFR, ERBB4, BRAF, GRB2, STAT5B, CBLB, CDKN1B, PAK2, etc
hsa05205 Proteoglycans in cancer 26 7.9E-05 FGFR1, ERBB4, TFAP4, GRB2, SDC2, IGF1R, ANK1, ANK3, SOS2, etc
hsa05200 Pathways in cancer 41 8.1E-05 E2F2, FGFR1, E2F3, GRB2, FGF9, STAT5B, MITF, FGF13, CXCL12, etc
hsa05218 Melanoma 14 1.0E-04 PIK3CG, EGFR, FGFR1, E2F2, E2F3, BRAF, FGF9, MITF, CDK6, etc
hsa05161 Hepatitis B 21 1.1E-04 PIK3CG, E2F2, EGR3, E2F3, GRB2, STAT5B, SMAD4, YWHAB, CDK6, etc
hsa05220 Chronic myeloid leukemia 14 1.2E-04 PIK3CG, E2F2, E2F3, CBLB, CDKN1B, CTBP2, BRAF, GRB2, SOS2, etc
hsa05223 Non-small cell lung cancer 12 1.8E-04 PIK3CG, EGFR, FHIT, E2F2, E2F3, BRAF, PLCG1, GRB2, SOS2, etc
hsa04151 PI3K-Akt signaling pathway 36 2.5E-04 FGFR1, FGF9, GRB2, FGF13, IGF1R, BCL2, PPP2CB, SOS2, CREB3L2, etc
hsa04024 cAMP signaling pathway 24 4.5E-04 PIK3CG, PTGER3, ROCK1, BRAF, PDE3A, CREB5, PDE4D, GRIA4, etc
hsa04360 Axon guidance 18 5.0E-04 LRRC4, ROCK1, PLXNA2, DPYSL5, PPP3R2, CXCL12, EPHB1, SLIT3, etc
hsa05212 Pancreatic cancer 12 6.9E-04 PIK3CG, EGFR, E2F2, E2F3, BRAF, SMAD4, TGFA, BRCA2, JAK1, etc
hsa04725 Cholinergic synapse 16 9.5E-04 PIK3CG, GNAO1, CREB5, ITPR2, KCNQ5, GNB1, FYN, CHRM1, BCL2, etc
hsa04728 Dopaminergic synapse 17 1.5E-03 GNAO1, CREB5, GRIA4, ITPR2, GNAL, GNB1, PPP2CB, CAMK2D, etc
hsa04066 HIF-1 signaling pathway 14 2.4E-03 EGFR, PIK3CG, IGF1R, CYBB, CDKN1B, PLCG1, BCL2, CAMK2D, HK2, etc
hsa05211 Renal cell carcinoma 11 2.5E-03 PIK3CG, BRAF, PAK2, EPAS1, PAK3, GRB2, SOS2, TGFA, TCEB1, etc
hsa04010 MAPK signaling pathway 26 3.0E-03 MEF2C, FGFR1, ZAK, FGF9, GRB2, CACNB1, DUSP10, PPP3R2, FGF13, etc
hsa04014 Ras signaling pathway 23 5.7E-03 PIK3CG, EGFR, FGFR1, GRB2, FGF9, FGF13, KDR, PTPN11, IGF1R, etc
hsa04915 Estrogen signaling pathway 13 7.3E-03 EGFR, PIK3CG, GNAO1, SP1, FKBP5, GRB2, SOS2, CREB3L2, etc
hsa05203 Viral carcinogenesis 21 8.0E-03 PIK3CG, EGR3, SP100, RBL2, LYN, UBE3A, GRB2, STAT5B, YWHAB, etc
hsa05031 Amphetamine addiction 10 9.1E-03 GRIN2D, CREB3L2, CAMK2D, PPP3R2, CREB3L1, CREB5, GRIA4, etc
hsa04660 T cell receptor signaling pathway 13 9.9E-03 PIK3CG, GRB2, PPP3R2, MAP3K7, CBLB, PLCG1, PAK2, FYN, PAK3, etc
hsa04921 Oxytocin signaling pathway 17 1.2E-02 PIK3CG, EGFR, MEF2C, GNAO1, ROCK1, CACNB1, PPP3R2, ITPR2, etc
hsa04261 Adrenergic signaling in cardiomyocytes 16 1.3E-02 PIK3CG, MYL3, CACNB1, CREB5, TPM3, ATP2B3, ADRB1, PLN, BCL2, etc
hsa04520 Adherens junction 10 1.4E-02 MAP3K7, PTPRJ, EGFR, IGF1R, FGFR1, SORBS1, FYN, SMAD4, PTPN1, YES1
hsa04730 Long-term depression 9 1.5E-02 GNAZ, IGF1R, GNAO1, NOS1, BRAF, LYN, PPP2CB, GRM1, ITPR2
hsa04550 Signaling pathways regulating pluripotency of stem cells 15 2.0E-02 PIK3CG, FGFR1, GRB2, SMAD5, SOX2, SMAD4, FZD3, ISL1, STAT3, etc
hsa04724 Glutamatergic synapse 13 2.1E-02 GNAO1, PPP3R2, GRIA4, SHANK2, GRM1, ITPR2, GRM4, GRM8, GNB1, etc
hsa04810 Regulation of actin cytoskeleton 20 2.1E-02 PIK3CG, EGFR, FGFR1, ROCK1, SSH1, BRAF, FGF9, SSH2, FGF13, etc
hsa04720 Long-term potentiation 9 2.6E-02 BRAF, GRIN2D, CAMK2D, PPP3R2, PPP3CA, GRM1, CAMK2A, ITPR2, CALM1
hsa04722 Neurotrophin signaling pathway 13 3.0E-02 PIK3CG, PLCG1, BRAF, GRB2, BCL2, MAP3K1, SOS2, CAMK2D, etc
hsa04068 FoxO signaling pathway 14 3.1E-02 PIK3CG, EGFR, BRAF, RBL2, GRB2, SMAD4, GRM1, BCL2L11, STAT3, etc
hsa04925 Aldosterone synthesis and secretion 10 3.1E-02 LDLR, CREB3L2, CAMK2D, NR4A2, CREB3L1, CREB5, CAMK2A, CAMK1D, etc
hsa04662 B cell receptor signaling pathway 9 3.3E-02 PIK3CG, LYN, DAPP1, GRB2, SOS2, PPP3R2, PPP3CA, NFATC2, NFATC3
hsa04015 Rap1 signaling pathway 19 3.7E-02 PIK3CG, EGFR, FGFR1, GNAO1, BRAF, FGF9, PRKCI, SIPA1L3, etc
hsa04910 Insulin signaling pathway 14 3.8E-02 PIK3CG, BRAF, GRB2, PHKG2, HK2, PRKCI, PRKAR2A, CBLB, etc
hsa04350 TGF-beta signaling pathway 10 3.9E-02 INHBB, ROCK1, SP1, SMAD6, PPP2CB, SMAD5, FST, INHBC, SMAD4, TGIF2
hsa04723 Retrograde endocannabinoid signaling 11 4.8E-02 GNAO1, GABRA4, GNB1, GABRA3, GNG2, GRIA4, GRM1, RIMS1, GNG7, etc
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Note: KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 5.

Figure 5

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GO-terms network analysis enriched by microRNA-129 target genes. The analysis was visualized by Cytoscape. The color of lines represents the degree of fold enrichment, and the color of circles represents the enriched number of genes.

Figure 6.

Figure 6

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KEGG pathway network analysis enriched by microRNA-129 target genes. The analysis was visualized by Cytoscape. The color of lines represents the degree of fold enrichment, and the color of circles represents the enriched number of genes.

Figure 7.

Figure 7

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Interaction network analysis of genes from top four KEGG pathways. A. Calcium signaling pathway. B. Glioma. C. Prostate cancer. D. cGMP-PKG signaling pathway.

Validation of the potential targets from calcium signaling pathway

To validate the relationship between microRNA-129 and some of the potential target genes. We examined the protein expression of the 10 hub genes randomly selected from Calcium signaling pathway, which was demonstrated as the most significantly enriched pathway in KEGG analysis and was also confirmed to play essential role in the tumorigenesis and development of PTC [18-25]. The Human Protein Atlas showed that prominently higher expression of ERBB4, PDGFRB, PLCG1, PPP3CA, PPP3R2, CALM1, CAMK2A, CAMK2D and EGFR could be observed (Figures 8, 9), which supported the hypothesis that these genes could be the real targets of microRNA-129 in PTC. GRIN2D protein expression data was not provided by Proteinatlas.

Figure 8.

Figure 8

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Protein expression of hub genes in papillary thyroid carcinoma. NT, normal tissue; PTC, papillary thyroid carcinoma. The immunohistochemical staining of potential targets of microRNA-129-5p were provided by The Human Protein Atlas. A: Erb-b2 receptor tyrosine kinase 4 (ERBB4). B: Patelet-derived growth factor receptor, beta polypeptide (PDGFRB). C: Phospholipase C, gamma 1 (PLCG1). D: Protein phosphatase 3, catalytic subunit, alpha isozyme (PPP3CA). E: Protein phosphatase 3, regulatory subunit B, beta (PPP3R2).

Figure 9.

Figure 9

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Protein expression of hub genes in papillary thyroid carcinoma. NT, normal tissue; PTC, papillary thyroid carcinoma. The immunohistochemical staining of potential targets of microRNA-129-5p were provided by The Human Protein Atlas. A: Calmodulin 1 (phosphorylase kinase, delta) (CALM1). B: Calcium/calmodulin-dependent protein kinase II alpha (CAMK2A). C: Calcium/calmodulin-dependent protein kinase II delta (CAMK2D). D: Epidermal growth factor receptor (EGFR).

Discussion

In the present study, the significant lowered expression level of microRNA-129 in PTC was observed based on the microRNA sequencing data from TCGA with 507 patients. The potential target genes were enriched in various signaling pathways, especially calcium signaling pathway from KEGG analysis. Furthermore, nine targets, namely ERBB4, PDGFRB, PLCG1, PPP3CA, PPP3R2, CALM1, CAMK2A, CAMK2D and EGFR were randomly selected from calcium signaling pathway for the protein validation. All the nine genes were markedly overexpressed in PTC tissues based on Proteinatlas data with tissue array and immunohistochemistry. The results of current study provide new insight for the future clinical application of microRNA-129 in PTC. However, the exact molecular mechanism of microRNA-129 in PTC needs further investigation.

There is an increasing knowledge that identifies the significance of microRNA-129 during the progress of various cancers, including prostate cancer, gastric cancer, breast cancer, hepatocellular carcinoma, and glioblastoma, etc [26-38]. However, the clinical role and function of microRNA-129 remained largely unclarified in thyroid cancer. By far, only two groups performed relevant study on microRNA-129 in thyroid cancers. Brest et al. [14] found that microRNA-129-5p was obligatory for histone deacetylase inhibitor-induced cell death in thyroid cancer cells, but no clinical role and target genes were identified. Duan et al. [15] reported that microRNA-129-5p expression was remarkably lowered in medullary thyroid carcinomas, without investigating other subtypes of thyroid cancers. In the current study, we found the microRNA-129-2 was apparently down-regulated in PTC tissues, similar but not significant down-regulation was also observed with microRNA-129-1. These patterns suggested that microRNA-129 might be a suppressive microRNA in thyroid carcinoma. In the survival analysis, no significant relationship was obtained between either microRNA-129-1 or microRNA-129-2 and different prognostic parameters, including OS, RFS or MFS. However, the aforementioned data were based on a single detecting method, microRNA sequencing. The exact diagnostic and prognostic values of microRNA-129 need to be validated with other approaches, such as real time RT-qPCR or FISH, with other independent cohort.

As for the function and molecular mechanism of microRNA-129 in PTC, no study has been performed so far. Duan et al. [15] found that microRNA-129-5p could target and suppress REarranged during Transfection proto-oncogene (RET) expression. Ectopic expression of microRNA-129-5p reduced cell growth, induced apoptosis and inhibited migration capability in medullary thyroid carcinomas cells via reducing the phosphorylated level of AKT.

Mature miRNA participates in the construction of RNA-induced silencing complex, identified as the miRNA ribonucleoprotein complex. In most of the circumstances, single-stranded miRNA of compounds and 3’-UTR of its homologous mRNA share imperfect complementary pairing bases, which will thus disturb the translation of the gene and modulate the following protein expression. In some other cases, miRNA and its target mRNAs share thorough complementation, which will cause a particular degradation of target mRNA in the complementary region and finally lead to gene silencing [39,40]. This characteristics of miRNA assists the development of various predicting programs for the potential target genes of miRNAs.

The existing methodologies of miRNA target gene prediction via in silico inquiry are manufactured upon sequence similarity search and thermodynamic stability. However, it is known that the results of in silico target prediction algorithms undergo limited specificity. Hence, the combination of in silico target predictions with gene expression profiling has been demonstrated to be able to increase the identification of functional miRNA-target gene relationships. After receiving such inspiration, we combined the predicting target genes from at least 6 platforms and the DEGs post microRNA-129 transfection in vitro. The eventual 889 genes were achieved, which we believed to have high possibilities to be the real targets of microRNA-129 in PTC. To test this hypothesis, we randomly took 10 hub genes from the calcium signaling pathway for validation. Only the protein level of GRIN2D was absent in Proteinatlas. Importantly, the leaving nine genes all showed consistently higher expression level in PTC, which suggested that they had more likelihoods to be targeted by microRNA-129, as it is lowered expressed in PTC, including ERBB4, PPP3R2, PPP3CA, CAMK2D, CAMK2A, CALM1, PLCG1, PDGFRB and EGFR. However, other in-depth in vitro experiments need to be carried out to unveil the miRNA-target relationships.

Collectively, the notably lowered expression level of microRNA-129 in PTC could play a pivotal role in the tumorigenesis of PTC. The biological function of microRNA-129 might be carried out via target multiple signaling pathways, among which, calcium signaling pathway from KEGG analysis is the most significant one. Nine targets, including ERBB4, PPP3R2, PPP3CA, CAMK2D, CAMK2A, CALM1, PLCG1, EGFR and PDGFRB, could be real target of microRNA-129 in PTC. However, the clinical role and exact molecular mechanism of microRNA-129 in PTC needs further exploration.

Acknowledgements

The authors thank TCGA and Protein atlas for providing the data. The study is funded by Guangxi Scientific Research and Technology Development Plan (1598011-4), the National Natural Science Foundation of China (81560448), and Guangxi Health Bureau Research Project (Z2012053).

Disclosure of conflict of interest

None.

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