Abstract
Aim
It was the aim of the study to identify commonly deregulated miRNAs in oral cancer patients by performing a meta-analysis of previously published miRNA expression profiles in cancer and matched normal non-cancerous tissue in such patients.
Material and methods
Meta-analysis included seven independent studies analyzed by a vote-counting method followed by bioinformatic enrichment analysis.
Results
Amongst seven independent studies included in the meta-analysis, 20 miRNAs were found to be deregulated in oral cancer when compared with non-cancerous tissue. Eleven miRNAs were consistently up-regulated in three or more studies (miR-21-5p, miR-31-5p, miR-135b-5p, miR-31-3p, miR-93-5p, miR-34b-5p, miR-424-5p, miR-18a-5p, miR-455-3p, miR-450a-5p, miR-21-3p), and nine were down-regulated (miR-139-5p, miR-30a-3p, miR-376c-3p, miR-885-5p, miR-375, miR-486-5p, miR-411-5p, miR-133a-3p, miR-30a-5p). The meta-signature of identified miRNAs was functionally characterized by KEGG enrichment analysis. Twenty-four KEGG pathways were significantly enriched, and TGF-beta signaling was the most enriched signaling pathway. The highest number of meta-signature miRNAs was involved in the sphingolipid signaling pathway. Natural killer cell-mediated cytotoxicity was the pathway with most genes regulated by identified miRNAs. The rest of the enriched pathways in our miRNA list describe different malignancies and signaling.
Conclusions
The identified miRNA meta-signature might be considered as a potential battery of biomarkers when distinguishing oral cancer tissue from normal, non-cancerous tissue. Further mechanistic studies are warranted in order to confirm and fully elucidate the role of deregulated miRNAs in oral cancer.
Keywords: Enrichment analysis, meta-analysis, meta-signature, miRNA, oral cancer
Introduction
Oral cancer is the most common type of head and neck cancers, characterized by high mortality rates, low long-term survival, and an increasing incidence among younger people (1). As the complexity of oral cancer is still challenging contemporary medicine, further molecular characterization of this cancer type is required. Identification of novel accurate molecular biomarkers will facilitate early detection and confirmation of the oral cancer diagnosis at the molecular level.
MicroRNA (miRNA) is a class of small non-coding RNA molecules which act as negative regulators of target gene expression at the post-transcriptional level (2). MicroRNAs induce gene silencing by binding to the 3′ untranslated region (3′-UTR) of the target mRNA, which leads to either mRNA degradation or translational repression (2). Almost all key cellular processes, such as proliferation, development, differentiation, and apoptosis, are regulated by miRNAs (2). Therefore, it is not surprising that altered expression of particular miRNAs has been associated with occurrence of cancer, including oral cancer (3,4).
Identification of deregulated miRNAs in oral cancer is important in the search for new sensitive molecular biomarkers with diagnostic, predictive, and prognostic value (3–5). Nowadays, the development of high-throughput technologies has enabled the identification of differentially expressed miRNAs between cancer and non-cancerous tissue samples on a large scale. However, the technologies used in different studies are diverse, and the obtained results are not entirely consistent. Therefore, our aim was to identify commonly deregulated miRNAs in oral cancer and characterize their biological meaning by performing a comprehensive meta-analysis of previously published miRNA expression profiling studies, followed by bioinformatic enrichment analysis.
Material and methods
Search strategy and study inclusion/exclusion criteria
Literature searching (PubMed) was carried out by using different combinations of keywords: miRNA, microRNA, miR, profile, profiling, signature, oral, cancer, tumour, tumor, carcinoma, squamous cell carcinoma. A last search was done on 31 January 2017. The meta-analysis was conducted to review published studies that compared the miRNA expression profiles in oral cancer tissues with those in normal, matched oral tissues. Therefore, we did not include miRNA expression data from public databases such as TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus). The following inclusion criteria were considered: (1) original articles published in English with full text available; (2) miRNA expression profiling in oral cancer specimens and matched non-cancerous counterparts of the same patients; (3) studies with reported lists of differentially expressed miRNAs and cut-off criteria. Papers in other languages, review articles, studies conducted on cell lines, analyzed expression of candidate miRNAs, oral cancer tissue and healthy tissue collected from different individuals, studies where different patient’s biological samples (such as blood, saliva, or serum) were used were excluded from further analysis. Data provided in the article’s supplementary material were also taken into consideration. Studies with no reported lists of differentially expressed miRNAs, or data that had only been shown on the heatmap, where further information could not be obtained, were excluded from the meta-analysis as well.
Meta-analysis of commonly deregulated miRNAs in oral cancer
From studies included in the meta-analysis, lists of differentially expressed miRNAs and their regulated directions in oral cancer were collected. Annotations of differentially expressed miRNAs in the different studies were harmonized according to the miRBase database (www.mirbase.org) (release 21 June 2014). If the miRNA strand was not reported, dominant or more abundant forms from the miRBase were used if applicable (6).
Meta-analysis was performed by means of a vote-counting method, which considers the total number of studies reporting consistently differentially expressed miRNAs (7,8). Fold change was not reported in all included studies. Therefore, this criterion was excluded in the miRNA meta-signature identification by vote-counting strategy. Seven studies were analyzed, and miRNAs reported as differentially expressed in at least three or more studies were considered as commonly deregulated.
The analyses were performed using R programming language and software environment for statistical computing and graphics. R scripts are available in the Supplement material S1 (available online).
Bioinformatic analysis
In order to access regulatory roles and identify molecular pathways controlled by commonly deregulated miRNAs in oral cancer, online open software DIANA-miRPath v3.0 (www.microrna.gr/miRPathv3) was used (9). The algorithm microT CDS v5.0 was used as prediction algorithm of miRNA–gene interactions. DIANA-miRPath v3.0 uses meta-analysis statistics for the assessment of combined miRNA action. The meta-analysis algorithm enables the identification of pathways controlled by multiple miRNAs by examining each miRNA individually and subsequently combining the result probabilities and test statistics (9). Functional analysis of commonly deregulated miRNAs was done for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) biological process terms (9). Results were merged into pathway and category union for KEGG and GO analysis, depicting 1–the probability that the examined functional category is significantly enriched with gene targets of at least one selected miRNA. For the enrichment analysis method, unbiased empirical distributions (10) with a microT threshold of 0.7 and false discovery rate (FDR) correction were used. Molecular pathways were considered significantly enriched when p < 0.05. Heatmaps of significantly enriched pathways were constructed using the DIANA-miRPath v3.0 tool (9).
Results
Overview of the studies included in the meta-analysis
By searching the PubMed database using different combinations of the previously mentioned keywords, avoiding duplications and review papers, and after careful consideration of all collected studies, seven studies met all inclusion criteria and were considered for further analysis (11–17). Details of the studies included in the meta-analysis are presented in Table 1. For the identification of deregulated miRNAs in oral cancer and matched normal tissue, different platforms and cut-off values were utilized (Table 1). From studies which met the required inclusion criteria, lists of differentially expressed miRNAs were collected (Supplementary material S2, available online). A total of 184 oral cancer samples and 162 matched adjacent normal tissue samples were analyzed. Altogether in the studies, 215 distinct miRNAs were identified to be reported as differentially expressed, after harmonization of annotations. In the list of all identified miRNAs, 93 miRNAs showed up-regulation in studies where identified, 111 miRNAs showed down-regulation, and 11 miRNAs showed inconsistent regulation (Supplementary material S3, available online).
Table 1.
Details of the included studies.
| Study (reference) | Number of tissue samples (cases/control)a | Differentially expressed miRNA |
Cut-off criteria | Platform | Country | ||
|---|---|---|---|---|---|---|---|
| Total | Up-regulated | Down-regulated | |||||
| Ganci et al., 2016 (11) | 76 (38/38) | 78 | 59 | 19 | FDR <0.06 p < 0.01 FC >1 |
Agilent platform Human miRNA microarray (V2) | Italy, Europe |
| Manikandan et al., 2016 (12) | 58 (29/29) | 39 | 15 | 24 | SD >1 FC >1 p < 0.05 |
miRCURY LNA™ microRNA array (Exiqon) | India, Asia |
| Shiah et al., 2014 (13) | 80 (40/40) | 84 | 32 | 52 | FC >2 p < 0.05 |
Human v2 microRNA expression BeadChips (Illumina) | Taiwan, Asia |
| Soga et al., 2013 (14) | 36 (29/7) | 23 | 12 | 11 | FC >4 | TaqMan Low Density Array (Human microRNA Panel v2.0) | Japan, Asia |
| Shi et al., 2015 (15) | 4 (2/2) | 38 | 31 | 7 | FC >2 | RNA Seq (Illumina HiSeq 2000) | China, Asia |
| Fukumoto et al., 2015 (16) | 72 (36/36) | 42 | NR | 42 | p < 0.05 | TaqMan Low Density Array (Human microRNA Panel v2.0) | Japan, Asia |
| Chen et al., 2017 (17) | 20 (10/10) | 12 | 7 | 5 | p < 0.05 p < 0.01 | TaqMan Low Density Array (TLDA v1.0) | USA |
Oral cancer tissue specimens/matched adjacent non-cancerous tissue from the same individual.
FC: fold change; FDR: false discovery rate; NR: not reported; SD: standard deviation.
MicroRNA meta-signature of oral cancer
After harmonization of annotations, miRNAs found to be deregulated in at least three studies were considered as commonly deregulated (Table 2). Among 20 commonly deregulated miRNAs in oral cancer compared to matched non-cancerous tissue, 11 were consistently up-regulated (miR-21-5p, miR-31-5p, miR-135b-5p, miR-31-3p, miR-93-5p, miR-34b-5p, miR-424-5p, miR-18a-5p, miR-455-3p, miR-450a-5p, miR-21-3p), while nine were consistently down-regulated (miR-139-5p, miR-30a-3p, miR-376c-3p, miR-885-5p, miR-375, miR-486-5p, miR-411-5p, miR-133a-3p, miR-30a-5p). Throughout our study miR-223-3p, miR-203-3p, and miR-17-5p also passed the identification threshold, but showed different regulated directions (Table 2). Therefore, for further analysis only miRNAs with consistent regulated direction in oral cancer were considered in the oral cancer meta-signature, while miR-223-3p, miR-203-3p, and miR-17-5p were excluded due to expression inconsistencies. Two of the most commonly up-regulated miRNAs in oral cancer, identified in five out of seven studies, were miR-21-5p and miR-31-5p. In a signature of consistently reported up-regulated miRNAs, miR-31-3p and miR-135b-5p were reported in four studies. Four studies also showed down-regulation of miR-139-5p, miR-30a-3p, and miR-376c-3p in oral cancer when compared with non-cancerous tissue. The rest of the deregulated miRNAs, included in the oral cancer meta-signature, were reported in three studies out of seven (Table 2).
Table 2.
List of commonly deregulated miRNAs in oral cancer compared with matched non-cancerous tissue among different studies.
| Deregulated miRNAs | Common: studies (n) | Up-regulated: studies (n) | Down-regulated: studies (n) | Present in study | Samples (n)a |
|---|---|---|---|---|---|
| miR-21-5p | 5 | 5 | 0 | Ganci et al., 2016; Soga et al., 2013; Shi et al., 2015; Chen et al., 2017; Manikandan et al., 2016 | 176 |
| miR-31-5p | 5 | 5 | 0 | Ganci et al., 2016; Soga et al., 2013; Chen et al., 2017; Manikandan et al., 2016; Shiah et al., 2014 | 252 |
| miR-139-5p | 4 | 0 | 4 | Soga et al., 2013; Fukumoto et al., 2015; Chen et al., 2017; Shiah et al., 2014 | 208 |
| miR-30a-3p | 4 | 0 | 4 | Soga et al., 2013; Shi et al., 2015; Fukumoto et al., 2015; Shiah et al., 2014 | 192 |
| miR-376c-3p | 4 | 0 | 4 | Ganci et al., 2016; Soga et al., 2013; Fukumoto et al., 2015; Shiah et al., 2014 | 264 |
| miR-223-3pb | 4 | 3 | 1 | Ganci et al., 2016; Soga et al., 2013; Chen et al., 2017; Manikandan et al., 2016 | 190 |
| miR-135b-5p | 4 | 4 | 0 | Ganci et al., 2016; Soga et al., 2013; Shi et al., 2015; Shiah et al., 2014 | 196 |
| miR-31-3p | 4 | 4 | 0 | Ganci et al., 2016; Soga et al., 2013; Manikandan et al., 2016; Shiah et al., 2014 | 250 |
| miR-885-5p | 3 | 0 | 3 | Shi et al., 2015; Fukumoto et al., 2015; Shiah et al., 2014 | 156 |
| miR-375 | 3 | 0 | 3 | Shi et al., 2015; Fukumoto et al., 2015; Shiah et al., 2014 | 156 |
| miR-486-5p | 3 | 0 | 3 | Soga et al., 2013; Chen et al., 2017; Shiah et al., 2014 | 140 |
| miR-411-5p | 3 | 0 | 3 | Soga et al., 2013; Fukumoto et al., 2015; Shiah et al., 2014 | 188 |
| miR-203-3pb | 3 | 2 | 1 | Soga et al., 2013; Shi et al., 2015; Manikandan et al., 2016 | 98 |
| miR-133a-3p | 3 | 0 | 3 | Ganci et al., 2016; Soga et al., 2013; Shiah et al., 2014 | 192 |
| miR-30a-5p | 3 | 0 | 3 | Ganci et al., 2016; Shi et al., 2015; Shiah et al., 2014 | 160 |
| miR-17-5pb | 3 | 2 | 1 | Ganci et al., 2016; Shi et al., 2015; Manikandan et al., 2016 | 138 |
| miR-93-5p | 3 | 3 | 0 | Ganci et al., 2016; Soga et al., 2013; Shi et al., 2015 | 116 |
| miR-34b-5p | 3 | 3 | 0 | Ganci et al., 2016; Shi et al., 2015; Shiah et al., 2014 | 160 |
| miR-424-5p | 3 | 3 | 0 | Ganci et al., 2016; Shi et al., 2015; Shiah et al., 2014 | 160 |
| miR-18a-5p | 3 | 3 | 0 | Ganci et al., 2016; Shi et al., 2015; Shiah et al., 2014 | 160 |
| miR-455-3p | 3 | 3 | 0 | Ganci et al., 2016; Shi et al., 2015; Shiah et al., 2014 | 160 |
| miR-450a-5p | 3 | 3 | 0 | Ganci et al., 2016; Shi et al., 2015; Shiah et al., 2014 | 160 |
| miR-21-3p | 3 | 3 | 0 | Ganci et al., 2016; Shi et al., 2015; Shiah et al., 2014 | 160 |
Number of tissue samples tested across the studies.
Due to inconsistencies in expression among studies, miRNAs were not further considered as commonly deregulated in oral cancer compared with matched non-cancerous tissue.
Functional annotation of commonly deregulated miRNAs in oral cancer
For further functional characterization the identified miRNA meta-signature was the subject of GO and KEGG enrichment analysis. We have noticed that GO enrichment using unbiased empirical distributions approach gave insufficient enrichment results, in contrast to the Fisher exact test (data not shown). However, it was recently pointed out that miRNA enrichment analysis using the Fisher test introduces bias (10), so we decided to proceed with the knowledge acquired by unbiased empirical distributions method. Twenty-four KEGG pathways were significantly enriched in the oral cancer miRNA meta-signature (Table 3, Figure 1). Pathways enriched in our miRNA list are coupled to different malignancies. Also, there was a plethora of signaling pathways. TGF-beta was the most enriched signaling pathway, while the highest number of meta-signature miRNAs regulate genes involved in the sphingolipid signaling pathway. The pathway with the highest number of genes regulated by identified miRNAs was the natural killer cell (NK)-mediated cytotoxicity (Table 3, Figure 1).
Table 3.
List of KEGG pathways enriched in oral cancer miRNA meta-signature.
| KEGG pathway | p valuea | Genes (n) | MiRNAs (n) |
|---|---|---|---|
| Acute myeloid leukemia | 0.006443 | 3 | 2 |
| TGF-beta signaling pathway | 0.011516 | 4 | 1 |
| Hepatitis C | 0.013149 | 19 | 4 |
| Proteoglycans in cancer | 0.015179 | 28 | 2 |
| Non-small cell lung cancer | 0.016204 | 9 | 3 |
| Estrogen signaling pathway | 0.019171 | 18 | 3 |
| Pancreatic cancer | 0.022215 | 10 | 2 |
| Cardiac muscle contraction | 0.023825 | 2 | 1 |
| Sphingolipid signaling pathway | 0.02472 | 17 | 4 |
| AMPK signaling pathway | 0.026242 | 8 | 2 |
| ErbB signaling pathway | 0.026337 | 3 | 1 |
| Natural killer cell-mediated cytotoxicity | 0.028397 | 29 | 3 |
| Neurotrophin signaling pathway | 0.032315 | 12 | 1 |
| mTOR signaling pathway | 0.032902 | 10 | 3 |
| Chronic myeloid leukemia | 0.033489 | 9 | 2 |
| Hippo signaling pathway | 0.033673 | 5 | 1 |
| FoxO signaling pathway | 0.035991 | 18 | 2 |
| cGMP-PKG signaling pathway | 0.036768 | 3 | 1 |
| Melanoma | 0.03704 | 18 | 3 |
| Jak-STAT signaling pathway | 0.037747 | 14 | 2 |
| Prolactin signaling pathway | 0.040764 | 4 | 1 |
| Glioma | 0.042782 | 6 | 2 |
| Chagas disease (American trypanosomiasis) | 0.04822 | 6 | 1 |
| Measles | 0.049572 | 4 | 1 |
p < 0.05 was considered as statistically significant.
Figure 1.
Heatmap of KEGG pathways enriched in oral cancer miRNA meta-signature. The heatmap depicts the enrichment level of KEGG pathways in miRNAs commonly deregulated in oral cancer (microT CDS v5.0 was used for target prediction, p value threshold 0.05, microT threshold 0.7, and enrichment analysis method: unbiased empirical distributions). Intensity of colors represents the log (p value).
Discussion
The fast development of high-throughput technologies has enabled easier identification of possible molecular biomarkers for different pathologies, including cancer. However, due to differently used platforms, sample sizes, data analysis, and interpretation, a high level of inconsistency among studies exists (11–17). Therefore, the need for integration of results from different studies through meta-analyses is justified.
Results of deregulated miRNAs reported in the studies included in the current meta-analysis are inconsistent. Numerous factors can contribute to this inconsistency, for instance, small effect sizes, sample preparation and storage, clinical characteristics of the patients, normalization of obtained data and data analysis, and different cut-off criteria for identification of differentially expressed miRNAs. These noticed inconsistencies among studies might emerge from differences in used platforms and types and numbers of probes as well. Even intra-tumor heterogeneity of miRNA expression (18,19) as well as molecular changes related to cancerogenesis in morphologically normal tissue (20) could introduce the inconsistency.
Our results regarding mir-21, mir-31, mir-133, and mir-139 deregulation in oral cancer are in line with findings reported in a recently published meta-analysis (21). Existing non-overlapping miRNAs between that and our study can be explained with more rigorous criteria applied in the selection process of our study. The finding of an up-regulation of mir-21 was not a surprise, since mir-21 is considered as an oncomir and therefore is highly expressed in various cancer types (22). Mir-21 has been considered to be a potential biomarker for tongue cancer detection (23) and progression and survival (24). Increased mir-21 expression is associated with poor survival of patients with head and neck cancer according to recently conducted meta-analysis (25). It has also been shown that miR-21-3p is overexpressed in oral cancer and that it plays an important role in metastasis (26). Likewise, the up-regulation of mir-31 in oral cancer is probably a result of epidermal growth factor receptor (EGFR) activation (27). Increased expression of mir-31 was also noticed in early stages of oral cancer with no detected metastasis (28). Up-regulation of mir-31 was proposed as a suitable biomarker of increased malignant transformation of oral, potentially malignant disorders (29). Down-regulation of miR-133a-3p was reported in oral cancer as well as in oral cancer cell lines (30). Oral cancer proliferation and invasion can be suppressed by miR-133a-3p over-expression and collagen type I alpha 1 chain gene (COL1A1) direct targeting (30). Decreased expression of miR-139-5p has been reported as a potential saliva biomarker for tongue cancer detection (31).
The activity of other meta-signature miRNAs was also examined in oral cancer. In a study investigating miRNA expression in oral cancer patients with and without metastasis, mir-135b was suggested to be a promising down-regulated biomarker of oral cancer progression (32). It was found that down-regulated miR-376c-3p suppresses the cancer phenotype by targeting homeobox B7 (HOXB7) in oral cancer tissue as well as in cell lines (33). In addition, in head and neck cancer, down-regulated miR-376c-3p leads to runt-related transcription factor 2 gene (RUNX2) deregulation and is associated with lymph node metastasis (34). Poor prognosis of head and neck cancer patients was associated with up-regulated expression of mir-93 (35) and mir-18a (25). Mir-34b has been found to be one of the consistently up-regulated miRNAs in head and neck cancer in a majority of studies as well (36). A recent study reported that in oral cancer miR-424-5p directly targets supressor of cytokine signaling gene (SOCS2) and modulates STAT5 signaling, expression of matrix metalloproteinase, cell migration, and invasion (37). It has been reported that miR-455-3p up-regulation is controlled by the TGF-beta pathway, consequently down-regulating ubiquitin conjugating enzyme E2B gene (UBE2B), which results in oral cancer proliferation (38).
Recent studies have emphasized the utility of mir-375 as a biomarker for detection of tongue cancer (23), malignant transformation of oral lesions (39), and late stages of oral cancer (28). Mir-375 has been down-regulated in metastatic oral cancer cell lines compared with less metastatic cells, and mir-375 induced cell migration and invasion by direct targeting the platelet-derived growth factor-A (PDGF-A) (40). Mir-375 mediates the trichostatin-A-induced radiosensitization of tongue cancer (41), indicating a therapeutic potential of mir-375. In addition, down-regulation of plasma circulating mir-375 and miR-486-5p has been associated with oral cancer recurrence (42). To the best of our knowledge, expression of mir-30a has not been studied in oral cancer. However, it has been reported that alcohol induces deregulation of mir-30a by increasing its expression in head and neck cancer (43).
It is well established that aberrant regulation of the signaling pathways is a prevalent theme in cancer. The most enriched signaling pathway is that of TGF-beta. It is involved in the regulation of crucial cellular processes of importance for cancerogenesis: cell growth, differentiation, apoptosis, and angiogenesis (44). The dual role of TGF-beta has been proposed in cancer formation—from protection from cancer progression in the early stages, to cancer promotion in later stages (44). Regarding enrichment analysis, the highest number of miRNAs regulate genes involved in the sphingolipid signaling pathway. Ceramide, sphingosine-1-phosphate, and their signaling cascade have been implicated in various aspects of oncogenesis and cancer progression (45). The main reason is that both lipids regulate the PI3K/AKT pathway, which represses apoptosis and autophagy (45). Sphingolipids also have an impact on cell cycle progression, telomerase function, cell migration, and stem cell biology (45). On the other hand, most of the genes regulated by miRNAs in our analysis belonged to the natural killer cell (NK)-mediated cytotoxicity. In vitro studies on human and other mammal cells, as well as in vivo studies in rodents, have suggested that NK cells target tumor cells (46). However, it has been suggested that tumors release exosomes which inhibit IL-2-induced NK cell cytotoxicity (47). It is known that exosomes contain miRNAs (48), which have the potential to regulate processes involved in NK cell homeostasis, survival, and turnover. As the cancerous and healthy tissues were obtained from the same patients, differentially expressed miRNAs might be involved in NK cytotoxicity modulation if transmitted by exosomes from cancer cells to NK cells via previously observed mechanisms (49).
When interpreting results obtained by the vote-counting method, some considerations have to be taken into account, keeping in mind different limitations and shortcomings (50). However, the vote-counting method has been recommended and is considered as a good alternative for identification of consistent gene expression (7,8), as well as a reliable strategy in searching for potential miRNA biomarkers in colorectal (51) and lung cancer (52).
To sum up, results reported in the current study can be helpful for more focused future confirmatory analysis of pre-selected miRNAs expression in oral cancer patients. Further validation and mechanistic studies of reported miRNA signature are highly warranted. It would also be interesting to perform more in-depth analysis of identified miRNA meta-signature in association with clinical data, such as patient survival, outcome, and metastasis.
Supplementary Material
Acknowledgements
Ivan Jovanovic and Jasmina Jovanovic contributed equally to this paper.
Funding Statement
This work was supported by the Faculty of Medicine, Military Medical Academy, Belgrade, Serbia [MFVMA/11/16-18] 18 and the Ministry of Education, Science, and Technological Development, Republic of Serbia [OI175085].
Disclosure statement
The authors report no conflicts of interest.
Notes on contributors
Katarina Zeljic, PhD, Assistant Professor at the University of Belgrade-Faculty of Biology.
Ivan Jovanovic, PhD, Assistant Research Professor at the University of Belgrade-Vinča Institute of Nuclear Sciences, Laboratory for Radiobiology and Molecular Genetics.
Jasmina Jovanovic, PhD student, University of Belgrade, Faculty of Mathematics.
Zvonko Magic, MD, PhD, Full Professor at the University of Defence-Faculty of Medicine at Military Medical Academy and Head of the Institute of Medical Research at the Military Medical Academy.
Aleksandra Stankovic, PhD, Full Research Professor at the University of Belgrade-Vinča Institute of Nuclear Sciences, Laboratory for Radiobiology and Molecular Genetics.
Gordana Supic, PhD, Assistant Professor at the University of Defence-Faculty of Medicine at Military Medical Academy.
References
- 1.Rivera C. Essentials of oral cancer. Int J Clin Exp Pathol. 2015;8:11884–94. [PMC free article] [PubMed] [Google Scholar]
- 2.Ha M, Kim VN.. Regulation of microRNA biogenesis. Nat Rev Mol Cell Biol. 2014;15:509–24. [DOI] [PubMed] [Google Scholar]
- 3.Manasa VG, Kannan S.. Impact of microRNA dynamics on cancer hallmarks: an oral cancer scenario. Tumor Biol. 2017;39:1010428317695920. [DOI] [PubMed] [Google Scholar]
- 4.Karatas OF, Oner M, Abay A, Diyapoglu A.. MicroRNAs in human tongue squamous cell carcinoma: from pathogenesis to therapeutic implications. Oral Oncol. 2017;67:124–30. [DOI] [PubMed] [Google Scholar]
- 5.Min A, Zhu C, Peng S, Rajthala S, Costea DE, Sapkota D.. MicroRNAs as important players and biomarkers in oral carcinogenesis. BioMed Res Int. 2015;2015:186904. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ.. miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008;36:D154–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Griffith OL, Melck A, Jones SJ, Wiseman SM.. Meta-analysis and meta-review of thyroid cancer gene expression profiling studies identifies important diagnostic biomarkers. J Clin Oncol. 2006;24:5043–51. [DOI] [PubMed] [Google Scholar]
- 8.Chan SK, Griffith OL, Tai IT, Jones SJ.. Meta-analysis of colorectal cancer gene expression profiling studies identifies consistently reported candidate biomarkers. Cancer Epidemiol Biomarkers Prev. 2008;17:543–52. [DOI] [PubMed] [Google Scholar]
- 9.Vlachos IS, Zagganas K, Paraskevopoulou MD, Georgakilas G, Karagkouni D, Vergoulis T, et al. DIANA-miRPath v3.0: deciphering microRNA function with experimental support. Nucleic Acids Res. 2015;43(W1):W460–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Bleazard T, Lamb JA, Griffiths-Jones S.. Bias in microRNA functional enrichment analysis. Bioinformatics. 2015;31:1592–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ganci F, Sacconi A, Manciocco V, Sperduti I, Battaglia P, Covello R, et al. MicroRNA expression as predictor of local recurrence risk in oral squamous cell carcinoma. Head Neck. 2016;38(Suppl1):E189–97. [DOI] [PubMed] [Google Scholar]
- 12.Manikandan M, Deva Magendhra Rao AK, Arunkumar G, Manickavasagam M, Rajkumar KS, Rajaraman R, et al. Oral squamous cell carcinoma: microRNA expression profiling and integrative analyses for elucidation of tumourigenesis mechanism. Mol Cancer. 2016;15:28. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Shiah SG, Hsiao JR, Chang WM, Chen YW, Jin YT, Wong TY, et al. Downregulated miR329 and miR410 promote the proliferation and invasion of oral squamous cell carcinoma by targeting Wnt-7b. Cancer Res. 2014;74:7560–72. [DOI] [PubMed] [Google Scholar]
- 14.Soga D, Yoshiba S, Shiogama S, Miyazaki H, Kondo S, Shintani S.. microRNA expression profiles in oral squamous cell carcinoma. Oncology Rep. 2013;30:579–83. [DOI] [PubMed] [Google Scholar]
- 15.Shi W, Yang J, Li S, Shan X, Liu X, Hua H, et al. Potential involvement of miR-375 in the premalignant progression of oral squamous cell carcinoma mediated via transcription factor KLF5. Oncotarget. 2015;6:40172–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Fukumoto I, Hanazawa T, Kinoshita T, Kikkawa N, Koshizuka K, Goto Y, et al. MicroRNA expression signature of oral squamous cell carcinoma: functional role of microRNA-26a/b in the modulation of novel cancer pathways. Br J Cancer. 2015;112:891–900. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Chen Z, Yu T, Cabay RJ, Jin Y, Mahjabeen I, Luan X, et al. miR-486-3p, miR-139-5p, and miR-21 as biomarkers for the detection of oral tongue squamous cell carcinoma. Biomark Cancer. 2017;9:1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Eriksen AH, Andersen RF, Nielsen BS, Sørensen FB, Appelt AL, Jakobsen A, et al. Intratumoral heterogeneity of MicroRNA expression in rectal cancer. PLoS One. 2016;11:e0156919. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Raychaudhuri M, Schuster T, Buchner T, Malinowsky K, Bronger H, Schwarz-Boeger U, et al. Intratumoral heterogeneity of microRNA expression in breast cancer. J Mol Diagn. 2012;14:376–84. [DOI] [PubMed] [Google Scholar]
- 20.Supic G, Kozomara R, Jovic N, Zeljic K, Magic Z.. Prognostic significance of tumor-related genes hypermethylation detected in cancer-free surgical margins of oral squamous cell carcinomas. Oral Oncol. 2011;47:702–8. [DOI] [PubMed] [Google Scholar]
- 21.Yan ZY, Luo ZQ, Zhang LJ, Li J, Liu JQ.. Integrated analysis and microRNA expression profiling identified seven miRNAs associated with progression of oral squamous cell carcinoma. J Cell Physiol. 2017;232:2178–85. [DOI] [PubMed] [Google Scholar]
- 22.Krichevsky AM, Gabriely G.. miR-21: a small multi-faceted RNA. J Cell Mol Med. 2009;13:39–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.He Q, Chen Z, Cabay RJ, Zhang L, Luan X, Chen D, et al. microRNA-21 and microRNA-375 from oral cytology as biomarkers for oral tongue cancer detection. Oral Oncol. 2016;57:15–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Supic G, Zeljic K, Rankov AD, Kozomara R, Nikolic A, Radojkovic D, et al. miR-183 and miR-21 expression as biomarkers of progression and survival in tongue carcinoma patients. Clin Oral Investig. 2018;22:401–9. [DOI] [PubMed] [Google Scholar]
- 25.Jamali Z, Aminabadi NA, Attaran R, Pournagiazar F, Ghertasi Oskouei S, Ahmadpour F.. MicroRNAs as prognostic molecular signatures in human head and neck squamous cell carcinoma: a systematic review and meta-analysis. Oral Oncol. 2015;51:321–31. [DOI] [PubMed] [Google Scholar]
- 26.Tseng HH, Tseng YK, You JJ, Kang BH, Wang TH, Yang CM, et al. Next-generation sequencing for microRNA profiling: microRNA-21-3p promotes oral cancer metastasis. Anticancer Res. 2017;37:1059–66. [DOI] [PubMed] [Google Scholar]
- 27.Lu WC, Kao SY, Yang CC, Tu HF, Wu CH, Chang KW, et al. EGF up-regulates miR-31 through the C/EBPbeta signal cascade in oral carcinoma. PLoS One. 2014;9:e108049. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Siow MY, Ng LP, Vincent-Chong VK, Jamaludin M, Abraham MT, Abdul Rahman ZA, et al. Dysregulation of miR-31 and miR-375 expression is associated with clinical outcomes in oral carcinoma. Oral Dis. 2014;20:345–51. [DOI] [PubMed] [Google Scholar]
- 29.Hung KF, Liu CJ, Chiu PC, Lin JS, Chang KW, Shih WY, et al. MicroRNA-31 upregulation predicts increased risk of progression of oral potentially malignant disorder. Oral Oncol. 2016;53:42–7. [DOI] [PubMed] [Google Scholar]
- 30.He B, Lin X, Tian F, Yu W, Qiao B.. MiR-133a-3p inhibits oral squamous cell carcinoma (OSCC) proliferation and invasion by suppressing COL1A1. J Cell Biochem. 2018;119:338–46. [DOI] [PubMed] [Google Scholar]
- 31.Duz MB, Karatas OF, Guzel E, Turgut NF, Yilmaz M, Creighton CJ, et al. Identification of miR-139-5p as a saliva biomarker for tongue squamous cell carcinoma: a pilot study. Cell Oncol. 2016;39:187–93. [DOI] [PubMed] [Google Scholar]
- 32.Scapoli L, Palmieri A, Lo Muzio L, Pezzetti F, Rubini C, Girardi A, et al. MicroRNA expression profiling of oral carcinoma identifies new markers of tumor progression. Int J Immunopathol Pharmacol. 2010;23:1229–34. [DOI] [PubMed] [Google Scholar]
- 33.Wang K, Jin J, Ma T, Zhai H.. MiR-376c-3p regulates the proliferation, invasion, migration, cell cycle and apoptosis of human oral squamous cancer cells by suppressing HOXB7. Biomed Pharmacother. 2017;91:517–25. [DOI] [PubMed] [Google Scholar]
- 34.Chang WM, Lin YF, Su CY, Peng HY, Chang YC, Lai TC, et al. Dysregulation of RUNX2/activin-A axis upon miR-376c downregulation promotes lymph node metastasis in head and neck squamous cell carcinoma. Cancer Res. 2016;76:7140–50. [DOI] [PubMed] [Google Scholar]
- 35.Li G, Ren S, Su Z, Liu C, Deng T, Huang D, et al. Increased expression of miR-93 is associated with poor prognosis in head and neck squamous cell carcinoma. Tumour Biol. 2015;36:3949–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Chen D, Cabay RJ, Jin Y, Wang A, Lu Y, Shah-Khan M, et al. MicroRNA deregulations in head and neck squamous cell carcinomas. J Oral Maxillofac Res. 2013;4:e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Peng HY, Jiang SS, Hsiao JR, Hsiao M, Hsu YM, Wu GH, et al. IL-8 induces miR-424-5p expression and modulates SOCS2/STAT5 signaling pathway in oral squamous cell carcinoma. Mol Oncol. 2016;10:895–909. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Cheng CM, Shiah SG, Huang CC, Hsiao JR, Chang JY.. Up-regulation of miR-455-5p by the TGF-beta-SMAD signalling axis promotes the proliferation of oral squamous cancer cells by targeting UBE2B. J Pathol. 2016;240:38–49. [DOI] [PubMed] [Google Scholar]
- 39.Harrandah AM, Fitzpatrick SG, Smith MH, Wang D, Cohen DM, Chan EK.. MicroRNA-375 as a biomarker for malignant transformation in oral lesions. Oral Surg Oral Med Oral Pathol Oral Radiol. 2016;122:743–52. [DOI] [PubMed] [Google Scholar]
- 40.Cao ZH, Cheng JL, Zhang Y, Bo CX, Li YL.. MicroRNA375 inhibits oral squamous cell carcinoma cell migration and invasion by targeting platelet-derived growth factor-A. Mol Med Rep. 2017;15:922–8. [DOI] [PubMed] [Google Scholar]
- 41.Jia L, Zhang S, Huang Y, Zheng Y, Gan Y.. Trichostatin A increases radiosensitization of tongue squamous cell carcinoma via miR375. Oncol Rep. 2017;37:305–12. [DOI] [PubMed] [Google Scholar]
- 42.Yan Y, Wang X, Veno MT, Bakholdt V, Sørensen JA, Krogdahl A, et al. Circulating miRNAs as biomarkers for oral squamous cell carcinoma recurrence in operated patients. Oncotarget. 2017;8:8206–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Saad MA, Kuo SZ, Rahimy E, Zou AE, Korrapati A, Rahimy M, et al. Alcohol-dysregulated miR-30a and miR-934 in head and neck squamous cell carcinoma. Mol Cancer. 2015;14:181. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Haque S, Morris JC.. Transforming growth factor-beta: a therapeutic target for cancer. Hum Vaccin Immunother. 2017;13:1741–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Oskouian B, Saba JD.. Cancer treatment strategies targeting sphingolipid metabolism. Adv Exp Мed Biol. 2010;688:185–205. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Vivier E, Tomasello E, Baratin M, Walzer T, Ugolini S.. Functions of natural killer cells. Nat Immunol. 2008;9:503–10. [DOI] [PubMed] [Google Scholar]
- 47.Zhang HG, Kim H, Liu C, Yu S, Wang J, Grizzle WE, et al. Curcumin reverses breast tumor exosomes mediated immune suppression of NK cell tumor cytotoxicity. Biochim Biophys Acta. 2007;1773:1116–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Muralidharan-Chari V, Clancy JW, Sedgwick A, D’Souza-Schorey C.. Microvesicles: mediators of extracellular communication during cancer progression. J Cell Sci. 2010;123:1603–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Berchem G, Noman MZ, Bosseler M, Paggetti J, Baconnais S, Le Cam E, et al. Hypoxic tumor-derived microvesicles negatively regulate NK cell function by a mechanism involving TGF-beta and miR23a transfer. Oncoimmunology. 2016;5:e1062968. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Tseng GC, Ghosh D, Feingold E.. Comprehensive literature review and statistical considerations for microarray meta-analysis. Nucleic Acids Res. 2012;40:3785–99. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Ma Y, Zhang P, Yang J, Liu Z, Yang Z, Qin H.. Candidate microRNA biomarkers in human colorectal cancer: systematic review profiling studies and experimental validation. Int J Cancer. 2012;130:2077–87. [DOI] [PubMed] [Google Scholar]
- 52.Rikke BA, Wynes MW, Rozeboom LM, Barón AE, Hirsch FR.. Independent validation test of the vote-counting strategy used to rank biomarkers from published studies. Biomark Med. 2015;9:751–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.