MicroRNA‑mRNA integrated analysis based on a case of well‑differentiated thyroid cancer with both metastasis and metastatic recurrence

  • Authors:
    • Yun‑Jie Zhang
    • Yu‑Shui Ma
    • Qing Xia
    • Fei Yu
    • Zhong‑Wei Lv
    • Cheng‑You Jia
    • Xiao‑Xin Jiang
    • Liang Zhang
    • Yun‑Chao Shao
    • Wen‑Ting Xie
    • Gai‑Xia Lu
    • Xia‑Qing Yv
    • Peng Zhong
    • Da Fu
    • Xiao‑Feng Wang
  • View Affiliations

  • Published online on: September 26, 2018     https://doi.org/10.3892/or.2018.6739
  • Pages: 3803-3811
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Abstract

The incidence of well‑differentiated thyroid cancer (WDTC) is rapidly increasing. Poor survival follows distant metastasis (DM) and recurrence. In the present study, we aimed to analyze the expression alterations in different stages of WDTC and the regulatory mechanism of DM and the recurrence of DM. A male patient diagnosed with follicular thyroid cancer and distant metastasis in the eleventh thoracic vertebrae received total thyroidectomy and the removal of a metastatic lesion. A local relapse was found in the vertebrae after four‑time iodine‑131 treatment. We performed mRNA and microRNA microarray on the paracancerous, cancerous, metastatic and metastatic recurrent tissue. In combination with the data of The Cancer Genome Atlas (TCGA), we used bioinformatics approaches to analyze the common alterations and microRNA‑mRNA interactions among the processes of tumorigenesis and metastasis. Metastatic lesions and recurrent lesions were used to investigate the molecular mechanism of tumor evolution and recurrence in this case. A total of four mRNAs and two microRNAs were newly found to be related to patient survival in WDTC. The microRNA‑mRNA interactions were predicted for the overlapped mRNAs and microRNAs. Lineage deregulation of genes, such as C‑X‑C motif chemokine receptor 4 (CXCR4) and thyroglobulin (TG) were found from the tumorigenic stage to the metastatic stage. The ribosome pathway was highly enriched in the bone metastasis compared with the cancerous tissue. The downstreaming effects of p53 were impaired in the recurrent lesion due to deregulation of several functional genes. The integrated analysis with TCGA data indicated several prognostic markers and regulatory networks for potential treatment. Our results also provided possible molecular mechanisms in which the ribosome and p53 pathways may respectively contribute to bone metastasis and local recurrence of metastasis.

Introduction

Thyroid cancer, the most common endocrine malignancy worldwide, has increased 3-fold in the past 3 decades (1,2). Well-differentiated thyroid cancer (WDTC), including papillary and follicular thyroid cancer, represents >90% of all thyroid cancers (3), and patients with WDTC have a 10-year survival of 80–95%. Many studies have profiled WDTC genes and rearrangements of RET/PTC and PAX8/PPARγ (4), and frequent (70%) point mutations of BRAF and RAS genes, which alter the mitogen-activated protein kinase (MAPK) signaling pathway (57) were documented in WDTC. Several genes, such as MUC1, PD-L1, DPP4, and mutations of the BRAFV600E and TERT promoter, have been reported as prognostic markers (811).

For WDTC, 10-year survival is reduced to 14–40% when distant metastasis (DM) occurs (1214). For patients with metastases, radioactive iodine (131I) has been the mainstay of treatment, selectively combined with surgical intervention, external beam radiation and chemotherapy (15). Metastatic lesions are sometimes resistant to 131I, so treatment options are limited and survival is poor (10-year survival, 10%) (14,16). Recurrence also greatly contributes to the morbidity of WDTC (17,18), and recurrence at 30 years is 35%, most being local with 32% being DM (17). Thus, we must identify potential targets to improve therapeutic strategies to prevent metastasis and recurrence.

MicroRNAs (miRNAs) are a class of endogenous small (18–22 nucleotide) non-coding RNAs. Gene expression regulation by miRNAs is of interest because many miRNAs in WDTC, such as miR-221, miR-222 and miR-146 (1921), have been reported to have critical roles in the regulation of apoptosis, proliferation, the cell cycle and epithelial-mesenchymal transition by negatively regulating mRNAs post-transcriptionally. Other studies on mRNA and miRNA expression indicate a miRNA-mRNA regulatory network that may provide clues for genetic deregulation in WDTC (22,23). Although many studies have focused on the tumorigenesis of WDTC, few studies have identified the mechanism of metastasis and recurrence in WDTC. This is due to the fact that tissues must be taken from metastatic sites, yet metastasis-prone sites of WDTC, such as the brain and bones, are challenging to sample properly. Second, the incidence of metastasis and recurrence is relatively low, and as such, WDTC patients have better outcomes than other malignancies.

In the present study, we collected tissue samples from four separate stages of WDTC: Normal, cancerous, metastatic and recurrent stages and we performed mRNA and miRNA microarray analysis. By integrating our study with gene expression data from The Cancer Genome Atlas (TCGA) (21), we revealed novel prognostic markers and key genes related to the progression of WDTC and possible pathways involved in WDTC recurrence.

Materials and methods

Pipeline and workflow

We observed changes in the expression of mRNA and miRNA in normal, cancerous, metastatic and recurrent stages of WDTC. To study tumorigenesis and metastasis, we overlapped our results with differentially expressed mRNAs (DEmRNAs) and miRNAs (DEmiRNAs) from TCGA sequencing data, which contained more than 500 cancerous samples of papillary thyroid cancer, 59 normal samples, 8 lymph node metastatic (LNM) samples and corresponding clinical data. We performed survival analysis to identify prognostic influence, enrichment analysis to reveal relevant pathways and ontology of gene sets and miRNA target prediction to depict possible miRNA-mRNA interactions in WDTC (Fig. 1).

Patient and sample preparation

The present study was approved by the Institutional Review Board and Ethics Committee of The Shanghai Tenth People's Hospital, Tongji University, Shanghai, China (SHSY-IEC-KY-4.0/17-13/01). In March 2010, case WDTC01 was a 40-year-old male patient, diagnosed with follicular thyroid cancer and distant bone metastasis (BM) in the 11th thoracic vertebrae. Total thyroidectomy was performed at Zhongshan Hospital, Shanghai, China. Paracancerous and cancerous tissues were collected during surgery. To relieve neurological symptoms of the lower extremity caused by BM, the metastatic lesion was debulked, collected and internally fixed. After three 131I radiation ablations, local recurrence was observed at the 11th thoracic vertebrae on September 2011 and the recurred lesion was collected during surgery (Fig. 1). All samples were immediately stored at −80°C until use. Total RNA was harvested using TRIzol (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA) and an miRNeasy mini kit (Qiagen GmbH, Hilden, Germany) according to the manufacturer's instructions.

Genome-wide transcriptional profiling with Agilent microarray and microRNA microarray expression profiling

RNA was immediately shipped on dry ice to KangChen Bio-Tech (Shanghai, China) for mRNA and miRNA microarray assay. For analysis via the Agilent Whole Human Genome Oligo Microarray platform, total RNA from each sample was amplified and transcribed into fluorescent cRNA using the manufacturer's instructions (Agilent's Quick Amp Labeling protocol, version 5.7; Agilent Technologies, Santa Clara, CA, USA). Labeled cRNAs were hybridized onto the Whole Human Genome Oligo Microarray (4×44 K; Agilent Technologies). After washing the slides, arrays were scanned using the Agilent Scanner G2505C. Agilent Feature Extraction software (version 11.0.1.1) was used to analyze the acquired array images. Quantile normalization and subsequent data processing were performed using the GeneSpring GX v11.5.1 software package (Agilent Technologies).

For the miRNA microarray, samples were labeled using the miRCURY Hy3/Hy5 Power labeling kit and hybridized with a miRCURY LNA Array (v.16.0) (Exiqon, Vedbaek, Denmark). Following washing, the slides were scanned using the Axon GenePix 4000B microarray scanner. Scanned images were then imported into GenePix Pro 6.0 software (Axon Instruments; Molecular Devices, LLC, Sunnyvale, CA, USA) for grid alignment and data extraction. Replicated miRNAs were averaged and miRNAs in which intensities were ≥50 in all samples were chosen for calculating a normalization factor.

Targeting prediction and enrichment analysis

The online prediction tool MiRWalk 2.0 (24) was used to predict interactions between miRNAs and mRNAs. In addition, miRanda (25) and TargetScan (26) were selected to generate an intersecting gene list. When an mRNA was predicted by all three databases, it was considered a target for the miRNA. Biological pathways defined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) (27) and Gene Ontology (GO) (28) were analyzed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) (29). DAVID provides a set of functional annotations for many genes. In the present study, KEGG pathway and GO terms were selected with a threshold of P<0.05.

Statistical analysis

For WDTC01, mRNAs and miRNAs with fold-changes (FC) >1.5 times were defined as differentially expressed and considered for analysis. RNA sequencing data with 505 samples of papillary thyroid cancer (PTC), 59 normal control and eight LNM, with the −3 miRNA sequencing data of 502 samples of PTC, 59 normal control and eight LNM from TCGA thyroid carcinoma (THCA), and survival data were downloaded from the TCGA analytical tool UCSC Xena (http://xena.ucsc.edu). However, recurrent sites were not available. The unit for the mRNA sequencing data was log2 (RSEM+1) and the unit for the miRNA sequencing data was log2 (RPM+1). To screen differentially expressed mRNAs and miRNAs with a median expression >0, we used the linear model of the limma package (30) of R-3.4.1. Benjamin and Hochberg's (31) correction was applied to ensure a false discovery rate (FDR). For comparisons between cancer and normal samples, DEmRNAs and DEmiRNAs were identified when FC >1.5, P<0.05 and FDR <0.05. For comparisons between 8 LNM and cancer samples, criteria were FC >1.5 and P<0.05.

To depict expression differences, a scatter plot was used to describe averages and standard deviations of expression. To address overall survival (OS) and recurrence-free survival (RFS) for patients, we retrieved follow-up states for patients in the clinical data matrix from TCGA and merged this with the expression matrix and performed Kaplan-Meier analysis for each mRNA and miRNA with survival of R. OS and RFS were considered significant when log-rank test was P<0.05.

Results

Upregulated ASF1B and ALPL and downregulated RPS27, CACNA1I, let-7a-2-3p and miR-486-5p are related to the prognosis of WDTC

We observed 444 upregulation and 740 downregulated DEmRNAs and 114 upregulation and 105 downregulated DEmiRNAs in cancerous tissue for WDTC01 (Fig. 2). To validate commonly expressed DEmRNAs and miRNAs in WDTC, DEmRNAs and miRNAs were compared with TCGA data. We identified 49 upregulated and 53 downregulated DEmRNAs, along with 2 upregulated and 10 downregulated DEmiRNAs, which were commonly deregulated (Fig. 3A). We selected overlapping molecules to perform survival analysis and construct a miRNA-mRNA network using online miRNA-target predicting tools. We found 10 overlapping mRNAs that interacted with 21 commonly deregulated mRNAs (Fig. 3B).

Among commonly expressed DEmRNAs and miRNAs, high expression of alkaline phosphatase, liver/bone/kidney (ALPL), low expression of ribosomal protein S27 (RPS27) and calcium voltage-gated channel subunit alpha1 I (CACNA1I) were associated with overall survival of WCDT. High expression of anti-silencing function 1B histone chaperone (ASF1B) and low expression of let-7a-2-3p and miR-486-3p were associated with RFS for WDTC (Fig. 3C).

Expression patterns are related to metastatic variations of WDTC

We identified 632 upregulation and 316 downregulated DEmRNAs and 20 upregulation and 46 downregulated DEmiRNAs in DM and recurrent lesions of WDTC01. To identify evolutionary signatures related to metastasis of WDTC, we investigated common expression patterns shared with BM from WDTC01 and LNM from TCGA. DEmRNAs and miRNAs from both metastatic sources were different. We found 18 upregulated DEmRNAs, such as apolipoprotein E (ApoE), cathepsin H (CTSH), C-X-C motif chemokine receptor 4 (CXCR4) and mucin 4 (MUC4) and one downregulated DEmRNA, gonadotropin releasing hormone receptor (GNRHR) in BM and LNM. For miRNAs, downregulation of miR-30a-5p occurred in BM and LNM (Fig. 4A).

Hyper-activation of the ribosome pathway contributes to DM of WDTC as indicated by enrichment analysis

To analyze features of BM and LNM of WDTC, we performed enrichment analysis with DEmRNAs that did not overlap between the two metastatic sources. Ribosomes, first listed in KEGG analysis and cell component (CC) in the GO analysis, were involved.

Significant biological process (BP) and molecular function (MF) terms were related to the translational process. Our analysis focused on the ribosome pathway in which 63 enriched genes were upregulated (Fig. 4B). For LNM, KEGG terms were related to immune-related pathways. BP terms were related to the ERK1/ERK2 signaling pathway and cellular calcium ion homeostasis. CC terms were related to extracellular activity and MF terms were associated with regulation of ion and protein binding (Fig. 4C).

Comparison among different stages of WDTC reveals that upregulation and downregulated DEmRNAs and DEmiRNAs are associated with tumor progression

To investigate DEmRNAs and miRNAs associated with tumor progression, we analyzed three stages of WDTC01 (normal, cancerous and metastatic tissues) before 131I treatment. Upregulated DEmRNAs included ATP-binding cassette subfamily A member 10 (ABCA10) and Mitogen-Activated Protein Kinase Kinase 3 (MAP2K3) and 13 downregulated DEmRNAs, such as BCL2 binding component 3 (BBC3) and thyroglobulin (TG), as well as 18 upregulation and 17 downregulated DEmiRNAs that were constantly deregulated with tumor progression (Fig. 4D).

Potential interactions among DEmRNAs and miRNAs using the aforementioned algorithm suggested that downregulation of miR-1291, miR-133a-5p and miR-3646 may increase the expression of MAP2K3 and ABCA10. Downregulation of the pro-apoptotic gene BBC3 may be caused by increases in 5 different progression-related miRNAs. The lineage upregulation of miR-204-3p and miR-3202 may interfere with the expression of TG, an important marker for thyroid cancer cell stemness (Fig. 4E).

Regained TG expression and impaired p53 downstream in recurrent lesions

To understand the underlying molecular mechanisms for recurrence following three 131I treatments, we compared recurrence and metastasis and revealed that there were 740 upregulation and 754 downregulated mRNAs and 88 upregulation and 60 downregulated miRNAs. TG was the most upregulated gene (10-fold) in recurrent lesions. Pro-apoptotic genes BAX (FC=23.6) and caspase-8 (FC=9.2) were among the top 20 downregulated genes (Fig. 5A).

KEGG pathway enrichment analysis was applied to 1,494 DEmRNAs and the p53 signaling pathway was of interest (Fig. 5B). Downregulation of BCL2 associated X, apoptosis regulator (BAX) and caspase-8 (CASP8) and upregulation of cyclin B1 (CCB1), D1 (CCD1) and E1 (CCE1) indicated attenuated downstream effects of the p53 pathway, such as cell cycle arrest and apoptosis (Fig. 5C). MiRNA-mRNA interaction analysis revealed potential DEmiRNAs in recurrent lesions, which regulated these mRNAs downstream of the p53 pathway. Among apoptotic-inducing genes, BAX and CASP8 were targeted by several miRNAs, and miR-1827 interacted with BAX and CASP8; EI24 was targeted by miR-211-5p, a well-known oncogene in WDTC (Fig. 5D).

Discussion

Despite increasing WDTC globally, few studies have investigated metastatic sites of WDTC from a genomic perspective. RNA-Seq analysis of metastatic sites, including two LNM and one pleural metastasis from a single patient, revealed intratumor heterogeneity and clonal evolution (32). Another miRNA study analyzed miRNA transcriptomes of PTC with four LNM and noted that downregulation of miRNAs was a common feature in PTC tumorigenesis (33). However, there has been no study to investigate the underlying mechanisms of recurrent WDTC using recurrent lesions and genomic profiling due to the difficulty of collecting tissue. We used one paired bone metastatic and one paired recurrent metastatic lesion and performed mRNA and miRNA microarray analysis. Integrated with TCGA data, we found common prognostic factors as key expression changes related to metastasis and distinct features of LMN and BM. We also suggested a pattern for recurrence from a genomic perspective, and we provided evidence for future prevention and therapy for metastasis and relapse of WDTC.

The epithelial-mesenchymal transition is a well-established model explaining thyroid cancer development (34). TG is thought to be a differentiation marker for WDTC (35), and lower TG expression was revealed to be correlated with thyroid cancer cell stemness (34). For WDTC01, TG expression gradually decreased in normal tissue to metastatic stage tissue (Fig. 4D). This may be explained by the fact that tumor cells with lower TG expression migrating from the primary tumor are prone to BM, which is supported by the fact that poorly differentiated thyroid cancer has more DM (36). Additionally, significant repression of TG was absent in 8 LNM from TCGA, indicating that TG suppression is specifically related to BM and not LNM. Next, the microenvironment of the vertebrae may induce an adaptive response to suppress expression of TG. According to the miRNA-mRNA analysis, TG may be suppressed by aberrant upregulation of miR-3202 and miR-204-3p (Fig. 4D). Along with TG, the pro-apoptotic BBC3 gene was gradually downregulated during lineage dedifferentiation. The putative miRNA-mRNA network revealed that continuously increasing expression of miR-622, miR-144-3p, miR-30b-3p, miR-711 and miR-665 may be the trigger of dysregulation of BBC3.

High expression of ALPL and low expression of miR-486-5p were related to a prognosis of WDTC, and these markers were associated with cancer cell stemness. High expression of ALPL, as a pluripotent stem cell marker (37), has been revealed to be associated with poor OS for glioblastoma and prostate cancers (38,39). For lung and prostate cancer, downregulation of miR-486-5p was reported to be associated with poor survival (4042). Cellular function data revealed that loss of miR-486-5p dedifferentiated cancer cells via epithelial-mesenchymal transition (42).

To find the common molecules related to BM and LNM in WDTC, we overlapped expression changes in both and among the 19 overlapped DEmRNAs, and CXCR4 was key to the CXCL12/CXCR4 pathway, a critical and well-studied pathway in BM (43). CXCR4 was reported to be significantly upregulated and related to BRAF mutations and neoplastic infiltration, resulting in more aggressive WDTC (44). Another two molecules involved in WDTC progression were also related to cancer cell stemness. As reported in ovarian carcinoma (45), MUC4 can induce epithelial-mesenchymal transition, and it can enhance the invasion of tumors in pancreatic and breast cancers (46,47). miR-30a-5p, the only overlapped miRNA in metastatic lesions, is reported to be associated with cancer metastasis. The loss of miR-30a-5p can lead to invasion and migration of breast cancer (48) and colorectal cancer cells (49) and epithelial-mesenchymal transition (50).

In addition, BM and LNM had distinct features with respect to enrichment analysis. Notably, all KEGG and GO terms of BM for WDTC01 were associated with ribosomal activity, and all genes enriched in this pathway were upregulated, indicating that efficient ribosome translational machineries contribute to BM in WDCT01. Although individual ribosomal proteins, such as RPL17 and RPS27L, were reported to be tumor suppressors, ribosome biogenesis and translation were directly associated with increased cell growth and proliferation (51). In contrast, no sign of overturned ribosomal activity was observed in the 8 LNM, perhaps due to the different microenvironment between BM and LNM.

The most common reason for WDTC recurrence was due to poor uptake of 131I in the poorly differentiated variant of WDTC. For WDTC01, gradually decreased expression of TG indicated a more dedifferentiated type of WDTC, so inefficient uptake was considered to contribute to cancer relapse (52). However, following 131I treatments, expression differed greatly from former metastatic lesions, indicating heterogeneity of the recurrent lesion. Well-differentiated neoplasms arise from residual cancer stem cells (CSCs) following surgical debulking. TG expression was again recovered to its pre-metastatic state because CSCs give rise to differentiated progeny (53). According to enrichment analysis, several critical genes of the p53 pathway were altered, and apoptotic resistance and a promoted cell cycle should increase proliferation of the recurrent lesion.

In summary, we offered an integrated analysis of mRNAs and miRNAs involved in the metastasis, progression and recurrence of WDTC and identified numerous mRNA and miRNA related to stemness, which contributed to the malignancy and recurrence of WDTC. The ribosome pathway was hyper-activated in BM, and the p53 pathway was correlated with relapse in our case. This information may provide useful regulatory targets and pathways for future development of predictive tools and therapies of WDTC.

Acknowledgements

We thank the study subject for his participation. We appreciate the experimental support of the Central Laboratory for Medical Research, Shanghai Tenth People's Hospital.

Funding

The present study was supported partly by grants from the National Natural Science Foundation of China (81472202, 81772932, 81201535, 81302065, 81301993, 81472501, 81372175, 81472209 and 81702243), The Fundamental Research Funds For the Central Universities (22120170212 and 22120170117), The Shanghai Natural Science Foundation (12ZR1436000 and 16ZR1428900) and the Shanghai Municipal Commission of Health and Family Planning (201440398 and 201540228). The funder has no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

Availability of data and materials

The datasets used during the present study are available from the corresponding author upon reasonable request.

Authors' contributions

YJZ, YSM, QX, DF and XFW designed the study. YJZ, YSM, QX, FY, ZWL, CYJ, DF and XFW performed the experiments. YJZ, YSM, DF and XFW performed the statistical analyses and interpreted the data. QX, FY, ZWL, CYJ, GXL, XQY and XFW are involved in the patient recruitment. FY, ZWL, CYJ, XXJ, LZ, YCS, WTX, GXL, XQY, PZ, DF and XFW contributed to the study materials and consumables. YJZ, YSM, DF and XFW wrote the manuscript. YJZ, YSM and QX contributed equally to this work. All authors read and approved the manuscript and agree to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Ethics approval and consent to participate

The present study was approved by the Institutional Review Board and Ethics Committee of The Shanghai Tenth People's Hospital, Tongji University, Shanghai, China (SHSY-IEC-KY-4.0/17-13/01).

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Kilfoy BA, Zheng T, Holford TR, Han X, Ward MH, Sjodin A, Zhang Y, Bai Y, Zhu C, Guo GL, et al: International patterns and trends in thyroid cancer incidence, 1973–2002. Cancer Causes Control. 20:525–531. 2009. View Article : Google Scholar : PubMed/NCBI

2 

Chen AY, Jemal A and Ward EM: Increasing incidence of differentiated thyroid cancer in the United States, 1988–2005. Cancer. 115:3801–3807. 2009. View Article : Google Scholar : PubMed/NCBI

3 

Sherman SI: Thyroid carcinoma. Lancet. 361:501–511. 2003. View Article : Google Scholar : PubMed/NCBI

4 

Xing M, Haugen BR and Schlumberger M: Progress in molecular-based management of differentiated thyroid cancer. Lancet. 381:1058–1069. 2013. View Article : Google Scholar : PubMed/NCBI

5 

Kimura ET, Nikiforova MN, Zhu Z, Knauf JA, Nikiforov YE and Fagin JA: High prevalence of BRAF mutations in thyroid cancer: Genetic evidence for constitutive activation of the RET/PTC-RAS-BRAF signaling pathway in papillary thyroid carcinoma. Cancer Res. 63:1454–1457. 2003.PubMed/NCBI

6 

Cohen Y, Xing M, Mambo E, Guo Z, Wu G, Trink B, Beller U, Westra WH, Ladenson PW and Sidransky D: BRAF mutation in papillary thyroid carcinoma. J Natl Cancer Inst. 95:625–627. 2003. View Article : Google Scholar : PubMed/NCBI

7 

Ciampi R, Knauf JA, Kerler R, Gandhi M, Zhu Z, Nikiforova MN, Rabes HM, Fagin JA and Nikiforov YE: Oncogenic AKAP9-BRAF fusion is a novel mechanism of MAPK pathway activation in thyroid cancer. J Clin Invest. 115:94–101. 2005. View Article : Google Scholar : PubMed/NCBI

8 

Wreesmann VB, Sieczka EM, Socci ND, Hezel M, Belbin TJ, Childs G, Patel SG, Patel KN, Tallini G, Prystowsky M, et al: Genome-wide profiling of papillary thyroid cancer identifies MUC1 as an independent prognostic marker. Cancer Res. 64:3780–3789. 2004. View Article : Google Scholar : PubMed/NCBI

9 

Moon S, Song YS, Kim YA, Lim JA, Cho SW, Moon JH, Hahn S, Park DJ and Park YJ: Effects of coexistent BRAFV600E and TERT promoter mutations on poor clinical outcomes in papillary thyroid cancer: A meta-analysis. Thyroid. 27:651–660. 2017. View Article : Google Scholar : PubMed/NCBI

10 

Lee JJ, Wang TY, Liu CL, Chien MN, Chen MJ, Hsu YC, Leung CH and Cheng SP: Dipeptidyl peptidase IV as a prognostic marker and therapeutic target in papillary thyroid carcinoma. J Clin Endocrinol Metab. 102:2930–2940. 2017. View Article : Google Scholar : PubMed/NCBI

11 

Chowdhury S, Veyhl J, Jessa F, Polyakova O, Alenzi A, MacMillan C, Ralhan R and Walfish PG: Programmed death-ligand 1 overexpression is a prognostic marker for aggressive papillary thyroid cancer and its variants. Oncotarget. 7:32318–32328. 2016. View Article : Google Scholar : PubMed/NCBI

12 

Samaan NA, Schultz PN, Hickey RC, Goepfert H, Haynie TP, Johnston DA and Ordonez NG: The results of various modalities of treatment of well differentiated thyroid carcinomas: A retrospective review of 1599 patients. J Clin Endocrinol Metab. 75:714–720. 1992. View Article : Google Scholar : PubMed/NCBI

13 

Schlumberger MJ: Papillary and follicular thyroid carcinoma. N Engl J Med. 338:297–306. 1998. View Article : Google Scholar : PubMed/NCBI

14 

Durante C, Haddy N, Baudin E, Leboulleux S, Hartl D, Travagli JP, Caillou B, Ricard M, Lumbroso JD, De Vathaire F, et al: Long-term outcome of 444 patients with distant metastases from papillary and follicular thyroid carcinoma: Benefits and limits of radioiodine therapy. J Clin Endocrinol Metab. 91:2892–2899. 2006. View Article : Google Scholar : PubMed/NCBI

15 

Haugen BR: 2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: What is new and what has changed? Cancer. 123:372–381. 2017. View Article : Google Scholar : PubMed/NCBI

16 

O'Neill CJ, Oucharek J, Learoyd D and Sidhu SB: Standard and emerging therapies for metastatic differentiated thyroid cancer. Oncologist. 15:146–156. 2010. View Article : Google Scholar : PubMed/NCBI

17 

Mazzaferri EL and Kloos RT: Clinical review 128: Current approaches to primary therapy for papillary and follicular thyroid cancer. J Clin Endocrinol Metab. 86:1447–1463. 2001. View Article : Google Scholar : PubMed/NCBI

18 

Mazzaferri EL and Jhiang SM: Differentiated thyroid cancer long-term impact of initial therapy. Trans Am Clin Climatol Assoc. 106(151–168): discussion 168–170. 1995.PubMed/NCBI

19 

Visone R, Russo L, Pallante P, De Martino I, Ferraro A, Leone V, Borbone E, Petrocca F, Alder H, Croce CM and Fusco A: MicroRNAs (miR)-221 and miR-222, both overexpressed in human thyroid papillary carcinomas, regulate p27Kip1 protein levels and cell cycle. Endocr Relat Cancer. 14:791–798. 2007. View Article : Google Scholar : PubMed/NCBI

20 

Mancikova V, Castelblanco E, Pineiro-Yanez E, Perales-Paton J, de Cubas AA, Inglada-Perez L, Matias-Guiu X, Capel I, Bella M, Lerma E, et al: MicroRNA deep-sequencing reveals master regulators of follicular and papillary thyroid tumors. Mod Pathol. 28:748–757. 2015. View Article : Google Scholar : PubMed/NCBI

21 

Cancer Genome Atlas Research Network: Integrated genomic characterization of papillary thyroid carcinoma. Cell. 159:676–690. 2014. View Article : Google Scholar : PubMed/NCBI

22 

Zhao M, Wang KJ, Tan Z, Zheng CM, Liang Z and Zhao JQ: Identification of potential therapeutic targets for papillary thyroid carcinoma by bioinformatics analysis. Oncol Lett. 11:51–58. 2016. View Article : Google Scholar : PubMed/NCBI

23 

Geraldo MV and Kimura ET: Integrated analysis of thyroid cancer public datasets reveals role of post-transcriptional regulation on tumor progression by targeting of immune system mediators. PLoS One. 10:e01417262015. View Article : Google Scholar : PubMed/NCBI

24 

Dweep H and Gretz N: miRWalk2.0: A comprehensive atlas of microRNA-target interactions. Nat Methods. 12:6972015. View Article : Google Scholar : PubMed/NCBI

25 

Betel D, Wilson M, Gabow A, Marks DS and Sander C: The microRNA.org resource: Targets and expression. Nucleic Acids Res. 36:D149–D153. 2008. View Article : Google Scholar : PubMed/NCBI

26 

Agarwal V, Bell GW, Nam JW and Bartel DP: Predicting effective microRNA target sites in mammalian mRNAs. Elife. 4:e050052015. View Article : Google Scholar :

27 

Kanehisa M, Sato Y, Kawashima M, Furumichi M and Tanabe M: KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44:D457–D462. 2016. View Article : Google Scholar : PubMed/NCBI

28 

Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: Tool for the unification of biology. The gene ontology consortium. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI

29 

da Huang W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using david bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI

30 

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: Limma powers differential expression analyses for rna-sequencing and microarray studies. Nucleic Acids Res. 43:e472015. View Article : Google Scholar : PubMed/NCBI

31 

Benjamini Y and Hochberg Y: Controlling the false discovery rate-a practical and powerful approach to multiple testing. J R Stat Soc Ser B-Methodol. 57:289–300. 1995.

32 

Le Pennec S, Konopka T, Gacquer D, Fimereli D, Tarabichi M, Tomas G, Savagner F, Decaussin-Petrucci M, Tresallet C, Andry G, et al: Intratumor heterogeneity and clonal evolution in an aggressive papillary thyroid cancer and matched metastases. Endocr Relat Cancer. 22:205–216. 2015. View Article : Google Scholar : PubMed/NCBI

33 

Saiselet M, Gacquer D, Spinette A, Craciun L, Decaussin-Petrucci M, Andry G, Detours V and Maenhaut C: New global analysis of the microRNA transcriptome of primary tumors and lymph node metastases of papillary thyroid cancer. BMC Genomics. 16:8282015. View Article : Google Scholar : PubMed/NCBI

34 

Lin RY: Thyroid cancer stem cells. Nat Rev Endocrinol. 7:609–616. 2011. View Article : Google Scholar : PubMed/NCBI

35 

Takano T, Miyauchi A, Yokozawa T, Matsuzuka F, Maeda I, Kuma K and Amino N: Preoperative diagnosis of thyroid papillary and anaplastic carcinomas by real-time quantitative reverse transcription-polymerase chain reaction of oncofetal fibronectin messenger RNA. Cancer Res. 59:4542–4545. 1999.PubMed/NCBI

36 

Are C and Shaha AR: Anaplastic thyroid carcinoma: Biology, pathogenesis, prognostic factors, and treatment approaches. Ann Surg Oncol. 13:453–464. 2006. View Article : Google Scholar : PubMed/NCBI

37 

Hou Z, Meyer S, Propson NE, Nie J, Jiang P, Stewart R and Thomson JA: Characterization and target identification of a DNA aptamer that labels pluripotent stem cells. Cell Res. 25:390–393. 2015. View Article : Google Scholar : PubMed/NCBI

38 

Rao SR, Snaith AE, Marino D, Cheng X, Lwin ST, Orriss IR, Hamdy FC and Edwards CM: Tumour-derived alkaline phosphatase regulates tumour growth, epithelial plasticity and disease-free survival in metastatic prostate cancer. Br J Cancer. 116:227–236. 2017. View Article : Google Scholar : PubMed/NCBI

39 

Iwadate Y, Suganami A, Tamura Y, Matsutani T, Hirono S, Shinozaki N, Hiwasa T, Takiguchi M and Saeki N: The pluripotent stem-cell marker alkaline phosphatase is highly expressed in refractory glioblastoma with DNA hypomethylation. Neurosurgery. 80:248–256. 2017. View Article : Google Scholar : PubMed/NCBI

40 

Vosa U, Vooder T, Kolde R, Vilo J, Metspalu A and Annilo T: Meta-analysis of microRNA expression in lung cancer. Int J Cancer. 132:2884–2893. 2013. View Article : Google Scholar : PubMed/NCBI

41 

Tan X, Qin W, Zhang L, Hang J, Li B, Zhang C, Wan J, Zhou F, Shao K, Sun Y, et al: A 5-microRNA signature for lung squamous cell carcinoma diagnosis and hsa-miR-31 for prognosis. Clin Cancer Res. 17:6802–6811. 2011. View Article : Google Scholar : PubMed/NCBI

42 

Zhang X, Zhang T, Yang K, Zhang M and Wang K: miR-486-5p suppresses prostate cancer metastasis by targeting Snail and regulating epithelial-mesenchymal transition. Onco Targets Ther. 9:6909–6914. 2016. View Article : Google Scholar : PubMed/NCBI

43 

Burton A: Regulation of RANKL might reduce bone metastases. Lancet Oncol. 7:3672006. View Article : Google Scholar : PubMed/NCBI

44 

Torregrossa L, Giannini R, Borrelli N, Sensi E, Melillo RM, Leocata P, Materazzi G, Miccoli P, Santoro M and Basolo F: Cxcr4 expression correlates with the degree of tumor infiltration and braf status in papillary thyroid carcinomas. Mod Pathol. 25:46–55. 2012. View Article : Google Scholar : PubMed/NCBI

45 

Ponnusamy MP, Lakshmanan I, Jain M, Das S, Chakraborty S, Dey P and Batra SK: Muc4 mucin-induced epithelial to mesenchymal transition: A novel mechanism for metastasis of human ovarian cancer cells. Oncogene. 29:5741–5754. 2010. View Article : Google Scholar : PubMed/NCBI

46 

Singh AP, Moniaux N, Chauhan SC, Meza JL and Batra SK: Inhibition of muc4 expression suppresses pancreatic tumor cell growth and metastasis. Cancer Res. 64:622–630. 2004. View Article : Google Scholar : PubMed/NCBI

47 

Rowson-Hodel AR, Wald JH, Hatakeyama J, O'Neal WK, Stonebraker JR, VanderVorst K, Saldana MJ, Borowsky AD, Sweeney C and Carraway KL III: Membrane mucin muc4 promotes blood cell association with tumor cells and mediates efficient metastasis in a mouse model of breast cancer. Oncogene. 37:197–207. 2018. View Article : Google Scholar : PubMed/NCBI

48 

Li W, Liu C, Zhao C, Zhai L and Lv S: Downregulation of β3 integrin by miR-30a-5p modulates cell adhesion and invasion by interrupting Erk/Ets-1 network in triple-negative breast cancer. Int J Oncol. 48:1155–1164. 2016. View Article : Google Scholar : PubMed/NCBI

49 

Wei W, Yang Y, Cai J, Cui K, Li RX, Wang H, Shang X and Wei D: miR-30a-5p suppresses tumor metastasis of human colorectal cancer by targeting ITGB3. Cell Physiol Biochem. 39:1165–1176. 2016. View Article : Google Scholar : PubMed/NCBI

50 

Chung YH, Li SC, Kao YH, Luo HL, Cheng YT, Lin PR, Tai MH and Chiang PH: miR-30a-5p inhibits epithelial-to-mesenchymal transition and upregulates expression of tight junction protein claudin-5 in human upper tract urothelial carcinoma cells. Int J Mol Sci. 18:E18262017. View Article : Google Scholar : PubMed/NCBI

51 

Ruggero D and Pandolfi PP: Does the ribosome translate cancer? Nat Rev Cancer. 3:179–192. 2003. View Article : Google Scholar : PubMed/NCBI

52 

Ma ZF and Skeaff SA: Thyroglobulin as a biomarker of iodine deficiency: A review. Thyroid. 24:1195–1209. 2014. View Article : Google Scholar : PubMed/NCBI

53 

Vermeulen L, Sprick MR, Kemper K, Stassi G and Medema JP: Cancer stem cells-old concepts, new insights. Cell Death Differ. 15:947–958. 2008. View Article : Google Scholar : PubMed/NCBI

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Journal Cover

December 2018
Volume 40 Issue 6

Print ISSN: 1021-335X
Online ISSN:1791-2431

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APA
Zhang, Y., Ma, Y., Xia, Q., Yu, F., Lv, Z., Jia, C. ... Wang, X. (2018). MicroRNA‑mRNA integrated analysis based on a case of well‑differentiated thyroid cancer with both metastasis and metastatic recurrence. Oncology Reports, 40, 3803-3811. https://doi.org/10.3892/or.2018.6739
MLA
Zhang, Y., Ma, Y., Xia, Q., Yu, F., Lv, Z., Jia, C., Jiang, X., Zhang, L., Shao, Y., Xie, W., Lu, G., Yv, X., Zhong, P., Fu, D., Wang, X."MicroRNA‑mRNA integrated analysis based on a case of well‑differentiated thyroid cancer with both metastasis and metastatic recurrence". Oncology Reports 40.6 (2018): 3803-3811.
Chicago
Zhang, Y., Ma, Y., Xia, Q., Yu, F., Lv, Z., Jia, C., Jiang, X., Zhang, L., Shao, Y., Xie, W., Lu, G., Yv, X., Zhong, P., Fu, D., Wang, X."MicroRNA‑mRNA integrated analysis based on a case of well‑differentiated thyroid cancer with both metastasis and metastatic recurrence". Oncology Reports 40, no. 6 (2018): 3803-3811. https://doi.org/10.3892/or.2018.6739