Sage Journals: Discover world-class research

Abstract

Half of all people aged 50 and over develop a thyroid nodule in their lifetime, exclusion of cancer is required in each case. Nodule tissue sampling is performed by way of fine needle aspiration biopsy (FNAB), however a definite diagnosis is possible only in 30% of cases. The discovery of a diagnostic biomarker to discriminate between thyroid cancer and benign nodules would therefore greatly improve current clinical practice. Using the databases of Medline, Embase and Pubmed we identified 21 original research papers examining various microRNA as potential biomarkers. Currently, the most evidence supporting diagnostic utility exists for miRNA-222. It has been shown repeatedly to have potential in diagnosis of PTC & MTC as well as being linked with the most prognostic factors of all microRNA. To a lesser extent, evidence seems to support the diagnostic and prognostic utility of miR-146b, Let-7 family, miR-221 for PTC and miR-21 for PTC & FTC. MicroRNA appear to show promise as potential diagnostic and prognostic biomarkers, however there is still not enough data to produce a consensus. Continued research should be undertaken with streamlined protocols.

Keywords

MicroRNA biomarker serum thyroid cancer

1. Introduction

Throughout 2015 to 2017, there was a total of 3,685 newly diagnosed thyroid cancers in the United Kingdom. Whilst thyroid cancer is relatively rare compared to other malignancies, its incidence rate has grown by 68% over the last decade and is projected to grow a further 74% from 2014–2035; representing an incidence of 11 in 100,000 people. This is a growing problem in society, disproportionately effecting women who are diagnosed at a rate 2–3 times more than men. Thyroid cancer has an average peak incidence seen in men aged 65–69; however, this age peak occurs earlier for women at around the ages of 45–49 [1].

Two postulated causes for this growing rate of incidence are improved detection of microcarcinomas and increasing environmental risks such as exposure to radiation [2]. Researchers in the United States conducted a retrospective study of patients with thyroid cancer from 1973–2002 using the SEER (Surveillance, Epidemiology, and End Results Program) database which encompasses approximately 10% of the country’s population. The increased incidence was observed predominantly in the small papillary cancers group while no significant changes were seen in follicular (FTC), medullary (MTC) or anaplastic (ATC) carcinomas [3]. In Ireland, Dijkstra et al. found a higher intake of iodine was correlated with significantly higher incidence of PTC in 190 patients over 30 years [4]. Ultimately however, the cause of the growing incidence rate of thyroid cancer has no clear consensus and remains in speculation.

About 1 in 2 people over the age of 50 are expected to develop a thyroid nodule in their lifetime and approximately 38% of clinically detected nodules are malignant, exclusion of thyroid cancer is required in each case [5]. Nodule tissue sampling is performed by way of fine needle aspiration biopsy (FNAB), however a definite diagnosis is possible only in about 30% of cases [5]. For these indefinite biopsies, a second histological sample is essential for diagnosis and is obtained by surgical resection of the thyroid lobe containing the lesion. Although generally safe, this invasive surgical procedure carries multiple risks including airway obstruction, recurrent laryngeal nerve palsy, accidental parathyroidectomy, and wound infection [6]. Of all diagnostic hemithyroidectomy patients, only 20% of the operative cases are retrospectively confirmed with diagnosis of a malignant thyroid cancer. This leaves an overwhelming 80% majority of patients subjected to surgical complication risks they otherwise could avoid if a more effective diagnostic test were available.

The majority of all thyroid cancer cases are histologically “well-differentiated” and classified as either papillary thyroid carcinoma (PTC) or follicular thyroid carcinoma (FTC). Histologically, these subtypes arise from papillary or follicular thyroid tissues respectively; they structurally resemble their original cell types as opposed to advanced undifferentiated thyroid cancers which do not [7]. There is a favourable long-term clinical outcome with a 10-year survival rate of 80–90% for patients with well-differentiated thyroid carcinoma. It is important however to assess for factors of poor prognosis such as tumour size, node metastases as well as tumour extension and invasion to better inform clinical treatment decisions [2]. A sensitive and less-invasive biomarker for diagnosis and prognosis of well-differentiated thyroid cancer would therefore prove invaluable, potentially providing earlier disease detection, reduction of both mortality and morbidity as well as the associated treatment cost and time savings. Since such a biomarker does not yet exist, more pioneering research is required to discover and assess the suitability of such tests. The currently existing biomarkers for thyroid cancer include calcitonin and thyroglobulin. Calcitonin is naturally produced in the C-cells of the thyroid and therefore can be sometimes useful in diagnostic work-up for medullary thyroid cancer. Unfortunately, this marker has very low specificity as it can be raised in patients with high BMI and thyroid nodules among other conditions. The precursor to T4 and T3 is thyroglobulin, it is used as a biomarker only for detecting recurrence of follicular thyroid cancer in patients with no remaining thyroid tissue. Thyroglobulin cannot distinguish between thyrotoxicosis and can be misinterpreted if remnant thyroid tissue exists [8].

The purpose of this review article was to review the current literature regarding microRNA in the detection of thyroid cancer and to assess whether they have the potential to be serum biomarkers in the diagnosis of thyroid cancer.

1.1 Current serum biomarkers

Currently there are a handful of biomarkers in clinical use however their utility is limited to specific situations and are seldom used in isolation for diagnostic purposes. Calcitonin is used solely for diagnostic work-up in medullary thyroid cancer due to its C-cell origin it can be raised when MTC is present. Unfortunately, serum calcitonin can also be raised in C-cell hyperplasia, follicular thyroid nodules, obesity, breast-feeding, smokers and pulmonary SCC. Additionally, in particularly aggressive cases of MTC which may be poorly differentiated, calcitonin levels are frequently found to be low. Regardless, calcitonin has a reported sensitivity of 83% and a specificity of 94% [9]. For FTC workup serum thyroglobulin can be measured however, it is only used to assess the possibility of disease recurrence after thyroidectomy and radioisotope ablation in order to eliminate all thyroid tissue. Raised thyroglobulin from residual thyroid tissue could therefore be misinterpreted as disease recurrence. There are of course conditions that interfere with thyroglobulin levels such as anti-thyroglobulin antibodies, taking of thyroid-suppressing medications, thyroiditis, thyrotoxicosis, thyroid adenoma or even iodine deficiency [10, 13]. The cited sensitivity and specificity of thyroglobulin are 57.1% and 98.1% respectively [10].

BRAF mutation is the most common gene mutation in PTC and its presence is linked with malignancy and metastasis. It does present however in other malignancies like melanoma and colorectal cancer and can therefore present with low specificity. Nevertheless some studies have found it to have a sensitivity and specificity of 62.5% and 71.1% respectively in PTC diagnosis [11].

1.2 MicroRNA

MicroRNA (miRNA) are small non-coding strands of RNA ranging from 18–25 nucleotides in size, which function as regulators of gene expression [10]. A single strand of miRNA can target and bind with multiple target mRNAs, binding with their untranslated 3’ region and subsequently activating RNase resulting in cleavage of the mRNA strand. In this way, the translation of the target RNA sequence to its encoded protein is prevented. An estimated 30% of protein-coding genes are regulated by microRNA via this mechanism, resulting in crucial regulatory changes to cellular and metabolic pathways [10]. Some of the functions resulting from miRNA gene silencing are modulation of cell proliferation and differentiation as well as apoptosis. In their retrospective study of the publicly available TCGA database, Dai et al looked specifically at miRNA-122 expression in 17 types of cancers. They found significant upregulation of the microRNA in colorectal carcinoma, thyroid, breast, lung, kidney and uterine cancers as well as adenocarcinomas of the prostate and stomach [15]. MiRNAs are crucial for proper tissue functioning and have been shown to be linked with various neoplastic processes when over or under-expressed, essentially acting as either tumor suppressors or oncogenes [14]. To date, thousands of unique tissue-specific miRNAs have been identified in the human body as well as many isoforms of each, known as “isomiRNA”. These isomers are length variations of a particular miRNA 5’ end which lead to variations in their target seeding regions thus regulating distinctly different genes [16]. Numerous studies have documented unique variations of miRNA expression to be associated with many benign conditions in addition to various cancers including thyroid carcinoma. A meta-analysis published in 2015 outlines several studies evidencing inflammation-associated diseases and their relation to specific microRNA signatures [16]. Cardiovascular disease was associated with miR-155, miR-302a, miR-712 and diabetes mellitus was associated with changes to levels of miR-126, miR-15a, miR-29b, miR-223, miR-28-3p, miR-375 as well as let-7. In 2016 a study of 27 consecutive patients found a significant positive correlation of miR-221-3p with raised troponin levels and significantly inversely correlated with left ventricular ejection fraction suggesting potential as a biomarker for early detection of acute myocardial infarction [19].

1.3 Detection of miRNA

MicroRNAs were first identified in tumor tissues but have since been found in various other tissues such as blood and urine, demonstrating exceptional stability in both fresh and frozen tumor samples as well as serum and plasma [16, 18]. Studies comparing serum versus plasma miRNA levels have failed to come to a consensus on the superiority of one over the other for accurate quantification. Studies suggesting use of plasma over serum postulate that the RNA released during the coagulation process may obscure the true circulating concentrations of miRNA [17, 19]. Whilst proponents of serum over plasma reference miRNA leeching from cellular lysis as well as heparin interference as reasons to avoid using plasma [20, 21]. Others found that the measured quantities of miRNA in both serum and plasma are strongly correlated, suggesting both are suitable mediums for quantification purposes [22].

Quantification of miRNA can be achieved by various techniques, the most commonly available of which are quantitative real-time polymerase chain reaction (qRT-PCR), microarray profiling, as well as next-generation sequencing (NGS). Whilst qRT-PCR remains the gold-standard test for sensitivity and specificity, the other two conventional techniques carry a few specific advantages such as the ability to test for a vast array of miRNAs at once. However, there are questions raised as to the reliability of microarray vs qRT-PCR as Ach et al. demonstrated discrepancies in miRNA expression levels when comparing commercially available microarray kits [23].

Ultimately, most researchers choose to use qRT-PCR exclusively or a combination of microarray/NGS as a screening test followed by validation of the discovered miRNA with qRT-PCR to preserve comparability of results between studies [24, 25]. As a diagnostic test, none of the currently available methods are suitable outside of specialized laboratories as they are expensive, prone to user-error and unstandardized. Therefore, current research is focused on identifying potential candidate miRNA capable of demonstrating diagnostic utility either individually or as a panel. In the future, a cheaper and easier to use method for miRNA isolation/quantification will need to be developed in order to be suitable as a widely available diagnostic test.

2. Materials and methods

The following searches were conducted in the Embase, Medline and Pubmed databases with no limit to publication date. Initially, the search terms “thyroid cancer” OR “thyroid carcinoma” OR “papillary thyroid carcinoma” OR “Follicular thyroid carcinoma” OR “well differentiated thyroid carcinoma” yielded 99141 results. Secondly, a search for publications referencing serum miRNA levels was done by looking for “miRNA” OR “microRNA” OR “miR” AND “serum”. This was then joined into an AND search with the keywords “detection” OR “diagnosis” and was subsequently limited to 90 articles written in English and pertaining to humans. These were then screened for relevancy and availability which yielded 21 articles examining serum/plasma miRNA, 16 studies looked at PTC specifically, 3 studies expanded to include either FTC, microPTC and MTC respectively. Whilst the two final studies either grouped all thyroid cancers together or looked at “unspecified atypical histology”. A table compiling all 21 studies and their details is included as Table 1. The second table includes study findings categorized by specific microRNA (Table 2). A breakdown of data normalization techniques used by each study is included in Table 3.

Table 1
Breakdown of study findings

Year	1 ${}^{\text{st}}$ author	Country	$N=$	Groups studied	Tissue studied	Technique	Upregulated	Downregulated
2012	Yu S ${}^{1}$	China	$N=$ 245	106 PTC 95 BG 44 Healthy controls (HC)	Serum	qTR-PCR (QIAGEN miScript SYBR Green PCR)	Let-7e (2.56x) miR-151-5p (2.71x) miR-222 (3.31x) (PTC vs. BG) Let-7e (2.46x) miR-151-5p (2.12x) miR-222 (3.42x) (PTC vs. HC)	–
2013	Lee JC	Australia	$N=$ 83	42 PTC 36 BG 5 Healthy controls (HC)	Plasma (pre&post-op)	qRT-PCR (Applied Biosystems TaqMan miRNA Assays)	miR-221 ( $p=$ 0.11) miR-222 ( $p=$ 0.004) miR-146b ( $p=$ 0.001) (PTC vs. HC) miR-221 ( $p=$ 0.019) miR-222 ( $p=$ 0.004) miR-146b ( $p=$ 0.003) (BG vs. HC)No sig. changes(PTC vs. BG)	–
2014	Cantara S	Italy	$N=$ 200	79 PTC 80 BG 41 Healthy controls (HC)	Serum	qRT-PCR microarray (TaqMan Array Human MicroRNA A Cards v2.0)	miR-190 ( $p<$ 0.001) (PTC vs. BG & HC)	miR-95 ( $p<$ 0.001) miR-579 ( $p<$ 0.01) miR-29b ( $p<$ 0.001) (PTC vs. BG & HC)
2015	Ferracin M	Italy	$N=$ 47	27 “thyroid cancers” 20 Healthy controls (HC)	Plasma	ddPCR (EvaGreen-based miRNA Assays)	miR-181a-5p ( $p<$ 0.0005) (PTC vs. HC)	–
2015	Graham R	Canada	$N=$ 51	18 PTC 13 BG 20 Healthy controls (HC)	Serum	qRT-PCR (miRCURY LNA Universal RT microRNA PCR Kit)	miR-93-5p Let7b-5p miR-191-5p (PTC vs. HC) Let7b-5p miR-191-5p (BG vs. HC)No sig. change (PTC vs. BG)	miR-146a-5p miR-150-5p miR-342-3p (PTC vs. HC) miR-150-5p miR-342-3p (BG vs. HC)No sig. change (PTC vs. BG)
2015	Igci YZ	Turkey	$N=$ 60	Malignant, atypical cells, BG	Serum	qRT-PCR	miR-30a-5p (PTC vs. BG) ( ${}^{**}p=$ 0.0166)	–
2015	Lee YS	South Korea	$N=$ 89	70 PTC 19 BG	Serum	qRT-PCR (Applied Biosystems TaqMan MicroRNA RT Kit)	No sig. change (PTC vs. BG)	No sig. change (PTC vs. BG)

Table 1, continued
Year	1 ${}^{\text{st}}$ author	Country	$N=$	Groups studied	Tissue studied	Technique	Upregulated	Downregulated
2015	Li M	China	$N=$ 161	56 PTC 95 BG 10 Healthy controls (HC)	Plasma	Microarray (Agilent Human miRNA Microarray Kit Release 19.0) qRT-PCR (SYBR Green PCR Kit)	miR-25-3p (2.4x) miR-451a (3.69x) miR-140-3p (23.83x) let-7i (5.72x) (PTC vs. BG) miR-25-3p (3.67x) miR-451a (5.08x) miR-140-3p (19.27x) let-7i (5.10x) (PTC vs. HC)	–
2016	Samsonov R	Russia	$N=$ 60	PTC, FTC, BG	Plasma	qRT-PCR	miR-31 miR-126-3p miR-145-5p (PTC vs. BG) miR-21-5p(FTC vs. PTC & BG)	miR-181a-5p(FTC vs. PTC)
2016	Yoruker E	Turkey	$N=$ 86	31 PTC 31 BG 24 Healthy controls (HC)	Serum	qRT-PCR (QIAGEN miScript SYBR Green PCR)	miR-222 miR-31 miR-151-5p let-7e (PTC vs. HC) miR-222 (PTC vs. BG)	miR-21(PTC vs. HC & BG)
2016	Yu S ${}^{2}$	China	$N=$ 150	50 PTC 50 BG 50 Healthy controls (HC)	Plasma	Microarray (miRCURY LNA Array) qRT-PCR (SYBR Green microRNA)	miR-124-3p miR-9-3p (PTC vs. HC & BG) ( ${}^{}P<$ 0.05) miR-196b-5p (BG vs. HC & PTC) ( ${}^{}P<$ 0.05)	–
2016	Zheng J	China	$N=$ 165	48 PTCR (recurrence) 117 PTC (no recurrence)	Serum	qRT-PCR (Applied Biosystems TaqMan MicroRNA RT Kit)	miR-203 (PTCR vs.PTC) ( $p<$ 0.01)	–
2017	Rosignolo F	Italy	$N=$ 83	44 PTC 19 BG 20 Healthy controls (HC)	Serum (pre&post-op)	qRT-PCR (Thermofisher TaqMan MicroRNA Assays)	miR-146a-5p ( $p=$ 0.0007) miR-221-3p ( $p<$ 0.0001) miR-222-3p ( $p<$ 0.0001) (PTC vs. HC) No sig. change (PTC vs. BG)	–
2017	Zhang M	China	$N=$ 140	70 PTC 70 BG	Serum	qRT-PCR	–	miR-451 (vs. BG) ( $p=$ 0.01)
2017	Wang D	China	$N=$ 290	150 PTC 100 BG 40 Healthy controls (HC)	Serum	qRT-PCR (BioRad SYBR Green Supermix)	miR-22(PTC vs. HC)	–

Table 1, continued
Year	1 ${}^{\text{st}}$ author	Country	$N=$	Groups studied	Tissue studied	Technique	Upregulated	Downregulated
2018	Ye W	China	$N=$ 90	30 PTC $+$ lymph mets 30 PTC $+$ no mets 30 Healthy controls (HC)	Serum	qRT-PCR (QIAGEN miScript SYBR Green PCR Kit)	miR-423-5p (PTC vs. HC)	–
2018	Zhang Y	China	$N=$ 180	58 PTMC (microcarcinoma) 47 PTC 35 BG 40 Healthy controls (HC)	Serum	qRT-PCR (Tiangen SYBR Green PCR Master Mix)	miR-222 miR-221 miR-146b miR-21 (PTMC vs. HC & BG) miR-222 miR-221 miR-146b miR-21(PTC vs. HC & BG)	–
2019	Zhang A	China	$N=$ 295	100 PTC 15 MTC 91 BG 89 Healthy controls (HC)	Serum	Microarray qRT-PCR (Applied Biosystems TaqMan probe-based qRT-PCR assay)	miR-222-3p miR-17-5p miR-451a (PTC vs. HC) No sig. change (PTC vs. BG) miR-222-3p miR-17-5p(MTC vs. BG & HC)	miR-146a-5p miR-132-3p miR-183-3p (PTC vs. HC) No sig. change (PTC vs. BG)
2020	Dai D	China	$N=$ 252	119 PTC 82 BG 51 Healthy controls (HC)	Plasma	qRT-PCR	miR-367a-3p miR-4306 miR-4433a-5p miR-485-3p (PTC vs. HC & BG) ( ${}^{**}P<$ 0.05)	–
2020	Jiang K	China	$N=$ 64	49 PTC $+$ lymph mets 15 PTC $+$ no mets	Plasma	qRT-PCR	miR-146b-5p ( $p=$ 0.008) miR-222-3p ( $p=$ 0.015) (PTC vs. PTC no mets)	–
2020	Kondrotiene A	Lithuania	$N=$ 129	49 PTC 23 BG 57 Healthy controls (HC)	Serum	qRT-PCR (Applied Biosystems TaqMan probe-based qRT-PCR assay)	miR-146b miR-222 miR-21 miR-221 miR-181b (PTC vs. HC) miR-222(PTC vs. BG)

PTC: Papillary thyroid cancer, BG: Benign goitre, HC: Healthy controls, PTCR: Recurrent PTC, PTMC: Papillary thyroid microcarcinoma X denotes fold-change of miRNA. E.g. (2.56x) denotes a 2.56-fold change in expression.

Table 2

Breakdown of study findings by microRNA

miRNA	# of studies	PTC vs NC	PTC vs BG	FTC or MTC	Prognostic correlations
miR-222	10	Raised (7 studies)	Raised (5 studies)	Raised in MTC vs BG (1 study)	BRAF mutation, lymph mets, advanced TNM, multifocality, tumour size $<$ 2 cm, bilateral lesions, extrathyroidal extension
miR-146 (a&b)	10	Raised (4 studies) Lowered (2 studies)	Raised (2 studies) Lowered (1 study)	–	BRAF mutation, lymph mets, multifocality, size $>$ 2 cm, bilateral lesions
miR-221	9	Raised (4 studies)	Raised (1 study)	–	BRAF mutation, lymph mets, TNM stage $>$ III
Let-7e	4	–	Raised (2 studies)	–	Multifocality
Let-7i	1	–	Raised (1 study)	–	–
Let-7b	1	–	Raised (1 study)	–	–
miR-21	8	Raised (3 studies) Lowered (1 study)	Raised (2 studies) Lowered (1 study)	Raised in FTCvs BG (1 study)	Lymph mets, bilaterality, multifocality, advanced TNM stage, extrathyroidal extension
miR-451	5	Raised (2 studies)	Raised (1 study) Lowered (1 study)	–	–
miR-151	3	Raised (2 studies)	Raised (1 study)	–	Lymph mets, tumour size $>$ 2 cm
miR-190	3	–	Raised (1 study)	–	–
miR-95	3	–	Lowered (1 study)	–	–
miR-31	3	–	Raised (1 study)	–	–
miR-29	2	–	Lowered (1 study)	–	–
miR-579	1	–	Lowered (1 study)	–	–
miR-9-3p	1	Raised (1 study)	Raised (1 study)	–	Age $<$ 45
miR-25-3p	2	Raised (1 study)	Raised (1 study)	–	–
miR-124-3p	1	Raised (1 study)	Raised (1 study)	–	Age $<$ 45, tumour size $>$ 2 cm
miR-196b	1	–	Lowered (1 study)	–	–
miR-191-5p	2	Raised (1 study)	–	–	–
miR-150-5p	1	Raised (1 study)	–	–	–
miR-342-3p	1	Raised (1 study)	–	–	–
miR-93-5p	1	Raised (1 study)	–	–	–
miR-376	2	–	Raised (1 study)	–	–
miR-485	1	Raised (1 study)	Raised (1 study)	–	Lymph metastases, BRAF mutation, tumour size $>$ 1 cm, advanced TNM stage, extrathyroidal extension
miR-4306	1	–	Raised (1 study)	–	–
miR-4433	1	–	Raised (1 study)	–	–
miR-17	1	Raised (1 study)	–	Raised in MTC vs BG (1 study)	–
miR-30	2	–	Raised (1 study)	–	–
miR-204	2	–	–	–	Lymph mets
miR-423	1	Raised (1 study)	–	–	–
miR-140	1	–	Raised (1 study)	–	–
miR-155	1	–	Raised (1 study)	–	–
miR-22	1	–	Raised (1 study)	–	–

Table 3

miRNA expression data normalization techniques used by each study

Article	Expression data normalization technique
2012 – Yu S	(Raw copy number/total clean read count) $\times$ 1,000,000
2013 – Lee JC	Baseline average from healthy controls
2014 – Cantara S	Ath-miR-159a, miR-16, miR-451
2015 – Ferracin M	ddPCR – no normalization
2015 – Graham R	Norm rank-invariant normalization
2015 – Igci YZ	U6
2015 – Lee YS	miR-39
2015 – Li M	$2^{-\Delta\Delta Ct}$
2015 – Samsonov R	Bio-Rad DC Protein assay
2016 – Yoruker E	miR-16
2016 – Yu S	miR-16
2016 – Zheng J	$2^{-\Delta\Delta Ct}$
2017 – Rosignolo F	CT $=$ K(log of miR copies) $+$ (y intercept)
2017 – Zhang M	RUN-44
2017 – Wang D	–
2018 – Ye W	miR-39
2018 – Zhang Y	miR-16
2019 – Zhang A	Let-7d/g/i
2020 – Dai D	miR-39
2020 – Jiang K	$2^{-\Delta\Delta Ct}$
2020 – Kondretiene A	miR-39

2.1 Results – miRNA-222

The most frequently referenced microRNA in the available literature is miR-222 and its mature isomer miR-222-3p. A total of 10 independent research papers cited this microRNA as being differentially expressed in PTC. Eight studies demonstrated an increase of miR-222 expression by 1.61–3.42 fold in PTC when compared to healthy controls, with a diagnostic area under the curve (AUC) of 0.852–0.882 as well as a sensitivity and specificity of 90.9–94.3% and 70.0–70.5% respectively [27, 28, 29, 30]. The AUC represents a comparable value of diagnostic accuracy as it describes the sensitivity of a test over all possible specificity values and vice versa [31].

One study was unable to detect miR-222 at all in their PTC serum samples using microarray analysis [32] whilst another study found the raise in miR-222 could not achieve significance [33]. The findings are more divisive when comparing PTC to benign goitre samples (BG), 5 studies observed raised miR-222 & miR-222-3p levels in PTC whilst another 4 studies found no significant changes between PTC and BG [27, 28, 34, 35]. In this comparison the diagnostic utility was less reliable with an AUC range of 0.587–0.906, sensitivity and specificity ranged from 47.7%–81.1% and 84.2%–92.5% respectively. All studies which measured miR-222 & miR-222-3p levels post thyroidectomy found them to be significantly decreased ( $p<$ 0.05).

Many prognostic correlations were identified, as miR-222 was found to be expressed 13x higher in PTC patients with BRAF gene mutation ( $p=$ 0.012) [27]. MiR-222 was also associated with lymph metastases ( $p<$ 0.05), TNM stages III & IV ( $p<$ 0.05), multifocal lesions ( $p=$ 0.002), tumour size $<$ 2 cm ( $p<$ 0.05) and bilateral lesions ( $p=$ 0.005). MiR-222 was associated with extrathyroidal extension ( $p=$ 0.026) in a 2018 publication, however another study found no such correlation [28, 33].

Only one paper studied miR-222-3p in medullary thyroid cancer (MTC), and found it to be expressed more in MTC versus healthy controls but also in BG. An AUC of 0.858 was calculated for differentiating MTC from BG suggesting miR-222 may provide significant diagnostic utility for that condition [35].

The use of miR-222-3p as a diagnostic marker for various cancers has been supported by evidence published by Wang et al.’s review in 2021. Associations were found between upregulated miR-222-3p and cancers of the bladder, breast, GI tract, endometrium, liver, skin, ovaries, bones and thyroid [36].

2.2 miRNA-146

In the miR-146 family, 7 studies measured levels of miR-146b and three other studies looked at miR-146a. Together, this family of microRNAs is the second most cited to be dysregulated in thyroid cancer. MiR-146b was shown to be expressed higher in PTC vs BG by four studies with a diagnostic AUC of between 0.653–0.9, sensitivity and specificity of 77.14%–79.5% and 52.6%–78.95% respectively [27, 28, 30, 33]. Only one study found a relative significant decrease of 2.4x when comparing PTC to BG ( $p<$ 0.001) [37]. Three studies found no significant difference in the miR-146b levels in PTC vs healthy controls, whilst in 2018 Zhang et al. in fact found a higher level in PTC ( $p<$ 0.001). All studies measuring post-operative miR-146b levels in PTC patients found them to have significantly decreased as much as 5.1x ( $p=$ 0.032) [27, 29]. Serum miR-146a was studied in four papers and in three of those, found to be decreased in both PTC and BG groups when compared with healthy controls.

As a diagnostic marker, miR-146b was found to be consistent with traditional thyroglobulin assay results in 85% of participants in one study. Strong prognostic correlation was found with BRAF mutation signified by a 35x higher expression level ( $p=$ 0.0025) [27]. Levels were higher in patients with lymph metastases ( $p<$ 0.05), bilateral lesions ( $p=$ 0.001), multifocal lesions ( $p=$ 0.005) as well as tumours $>$ 2 cm in size ( $p=$ 0.007). Levels were also higher with extrathyroidal extension but did not achieve statistical significance ( $p=$ 0.103).

Other studies showed that decreased levels of miR-146a are linked with systemic lupus erythematosus, rheumatoid arthritis, Sjögren’s syndrome whilst elevated levels in the brain are linked with Alzheimer’s and age-related macular degeneration [38].

2.3 miRNA-221

There are ten publications examining the utility of miR-221 as a diagnostic/prognostic blood biomarker for thyroid cancer, and five papers report either undetectable or insignificant changes in expression. Another four papers however, found significant raises of miR-221 in PTC vs healthy controls. Differentiation between PTC and BG is difficult, with only one study citing raised miR-221 levels with a diagnostic AUC of 0.65 with sensitivities and specificities of 77.14% and 50% respectively [32]. MiR-221 was found to be 6.5x higher in patients with BRAF mutation ( $p=$ 0.005), TNM stage $>$ III ( $p=$ 0.004) and lymph metastases ( $p=$ 0.0363). No correlations were found with tumour size or extrathyroidal extension [27, 30, 33, 39].

MiR-221 has also been examined in the context of other disease and some associations have been made with glioblastoma, bladder cancer, prostate cancer, pancreatic cancer, colorectal cancer and gastric cancer [40].

2.4 Let-7 (family)

Collectively, the various let-7 microRNA act broadly as onco-suppressors by suppressing cellular proliferation, inducing chemosensitivity as well as modulating cell-signalling pathways [41]. Blazeau et al. published a review which collated evidence on the Let-7 family of microRNA which have subsequently been associated with over 20 different types of cancers [42].

The most frequently studied member of the let-7 microRNA group is Let-7e followed by Let-7i and Let-7b. In total there were four studies examining Let-7e of which two found a significantly increased level in PTC compared to BG, whilst the remaining studies could not detect sufficient levels for analysis using [29, 32, 35, 39]. The only prognostic correlation found was a higher Let-7e level in patients with multifocal lesions ( $p<$ 0.001) [39].

Let-7i was found by Li et al. to be increased in PTC by 5.72x ( $p<$ 0.016) and 5.10x ( $p<$ 0.019) respectively, when compared to BG and healthy controls respectively [44]. Interestingly, Zhang et al. found stable expression levels of Let-7i across all samples and subsequently used it in normalizing expression data [45].

Let-7b was also raised in PTC and BG by 3.9x ( $p<$ 0.001) and 2.7x ( $p=$ 0.01) when compared to healthy controls [37].

2.5 miRNA-21

Dysregulated miR-21 has been implicated in over 29 different neoplastic and benign conditions ranging from myocardial infarctions to breast cancer [46]. More recently, studies have begun to examine the correlation with thyroid cancer.

MiRNA-21 levels were significantly raised (15.21x, $p<$ 0.001) in PTC compared to healthy controls, a result which was independently replicated by only three research teams [28, 47, 48]. AUC for detecting PTC from BG was 0.95 with sensitivity and specificity of 88.57% and 89.47% [28]. However, four other studies in 2016, 2015 & 2013 could not find any significant differences in miR-21 levels between PTC and BG groups. In fact, one study found higher miR-21 levels only in BG vs PTC & HC ( $p=$ 0.002) [34]. Samsonov et al. proposed some diagnostic utility for discerning FTC from BG, as significantly higher levels were observed in the FTC cohort [47].

MiRNA-21 was associated with poorer prognosis due to links with lymph metastases ( $p<$ 0.03), bilateral ( $p=$ 0.015) and multifocal lesions ( $p=$ 0.005), advanced TNM stage ( $p=$ 0.008) as well as extrathyroidal extension ( $p=$ 0.021) [28, 37].

2.6 miRNA-451

Results from two studies found a raised miR-451 level in PTC compared to healthy controls (5.08x, $p<$ 0.05), only one of which found a significant difference between PTC and BG (3.69x, $p<$ 0.05)[44, 45]. The calculated AUC was 0.857 with sensitivity and specificity of 88.9% and 66.7% respectively. A post-operative miR-451 level decrease in PTC cases ( $p=$ 0.004) was also found in that study. Using microarray detection, Yu et al. found no significant difference in miR-451 level in PTC or benign goitre. Another study found a significant decrease in PTC vs BG as well as in lymph metastases ( $p=$ 0.017) [49]. A result which was not replicated in 2020 as Jiang et al. found no significant difference with lymph metastases ( $p=$ 0.5) [39].

Zhang et al. similarly found no significant difference in the microRNA level in MTC compared with benign goitre [45].

A review published in 2019 found that dysregulated miR-451 may be associated with hepatocellular carcinoma, GI-tract cancers, urinary tract cancers, ovarian and cervical cancers as well as lung and bone cancers [50].

2.7 miRNA-151

Raises in the serum levels of miR-151 in patients with PTC were found in two studies compared to healthy controls, however a difference in expression between PTC and BG was only found by one research team (2.17x, $p<$ 0.001) [34, 43]. For PTC vs BG an AUC of 0.78 was calculated along with specificity and sensitivity of 59.4% and 89.5% respectively. Two other studies found no significant difference between miR-151 levels in PTC vs BG with one study unable to obtain sufficient signal for analysis. Both Yu et al. and Yoruker et al. found prognostic association of miR-151 with lymph metastases ( $p=$ 0.012) and tumour size $>$ 2 cm ( $p<$ 0.001).

Raised levels of miR-151-3p have been previously found to be linked with colon adenocarcinoma by one study in 2020 [51].

2.8 miRNA-190 and miRNA-95

Levels of these microRNAs have been detected by 3 independent research groups. Cantara and Pilli found elevated miRNA-190 in PTC vs BG ( $p<$ 0.001) and decreased miRNA-95 for the same group ( $p<$ 0.001) [52]. They found that for diagnosis of PTC combining the two markers into a panel yields an AUC of 0.99, with sensitivity and specificity 94.9% and 98.7% respectively. Two other teams however could not detect sufficient levels for statistical analysis, by both microarray and PCR methods [33, 28].

Dysregulated levels of miR-190-5p have also been linked with bladder cancer, pancreatic cancer, meningioma, gastric cancer, breast cancer, prostate cancer and others [53]; whilst miR-95-3p has been loosely linked with hepatocellular carcinoma and colorectal cancer [54].

2.9 Other miRNAs

A large selection of other microRNAs has been documented by various individual research teams, these have not yet been thoroughly re-examined and have not had results reproduced. MiR-31 was identified by Samsonov et al. to be elevated in PTC vs BG, Yoruker et al. found no significance in the change in expression they observed, and Lee JC et al. had a low detection signal unsuitable for statistical analysis [27, 34, 47]. Cantara and Pilli identified in their study decreased levels of miR-29b and miR-579 in PTC vs BG ( $p<$ 0.001) [52].

Yu et al. examined plasma expression levels of miR-9-3p, miR-25-3p, miR-124-3p, miR-196b-5p and miR-4701. Raised levels of both miR-9-3p and miR-124-3p were seen in PTC vs BG ( $p<$ 0.05), with AUC, sensitivity and specificities of 0.823, 80%, 73.7% and 0.831, 88%, 76% respectively [32]. Raises in miR-9-3p and miR-124-3p were also higher in patients younger than 45 years of age, and for miR-124-3p they were associated with tumour sizes larger than 2 cm. MiR-196b-5p was decreased in PTC vs BG, an AUC of 0.781 as well as sensitivity and specificity of 74% and 66% were calculated. For miR-25-3p no significant change was found in PTC vs BG, however Li et al. indeed found a 2.4-fold increase. Yu et al. found no change in MiR-4701 expression between all cohorts including PTC, BG and HC.

Graham et al. examined levels of miR-150-5p, miR-342-3p, miR-191-5p and miR-93-5p but found no significance in any changes found comparing PTC to BG ( $p>$ 0.05) [37].

Dai et al. found significant raises ( $p<$ 0.05) in PTC vs BG in all plasma microRNAs that it studied which included miR-376a-3p, miR-485-3p, miR-4306 and miR-4433a-5p [15]. The only correlation found was for miR-485-3p which was noted to be associated with tumour size >1cm, advanced TNM stage, extrathyroidal extension, lymph metastases and BRAF mutation.

Zhang et al. found no significant changes in expression in PTC vs BG for miR-17-5p, miR-132-3p and miR-183-3p. Notably, a significantly higher expression of serum miR-17-5p in MTC vs BG ( $p<$ 0.01) was observed by the researchers representing an AUC of 0.84.

In 2020, Jiang et al. studied changes in levels of miR-30a-3p, miR-204-5p and miR-138-5p in patients with metastatic and non-metastatic PTC, they found that only miR-204-5p was correlated with lymph metastases ( $p=$ 0.015). Ye et al. compared miR-423-5p in metastatic and non-metastatic PTC finding it raised when compared to healthy controls ( $p<$ 0.01). Other microRNAs found increased in PTC vs BG were miR-140-3p (23.8-fold increase, $p<$ 0.05) [43], miR-155 ( $p=$ 0.009), miR-22 ( $p<$ 0.001) [33]

Further, two other microRNAs were found to have no significant differences in PTC vs BG expression which included miR-181b [27, 47], miR-100 [45].

Finally, for PTC recurrence miR-203 was uniquely found raised vs non-recurrent PTC (AUC 0.755, $p<$ 0.01) and it was also associated with higher TNM stage ( $p=$ 0.014), lower survival rate as well as perineural invasion [55].

3. Concluding remarks

Biomarker research has been gaining popularity over the last decade and our resultant knowledge has continued to expand along with it. Although tissue biomarker research has become more widely studied, “liquid biopsy” serum/plasma biomarkers are still in their infancy for many diseases. Such is the case with microRNAs in thyroid cancer, the bulk of research focusing on PTC but at present little or no research has been done into the other thyroid cancers.

Although the body of evidence is still small, some microRNAs seem to show promise for their diagnostic and prognostic utility. Whilst the currently used prognostic biomarkers are limited as calcitonin is in MTC, microRNA appear to be linked to various different prognostic factors. Associations with tumour size, tumour invasiveness, lymph node involvement and metastases, further research could examine an assorted panel of microRNA which may provide the clinician with important insights into these risks. It may therefore be beneficial to understand if any of these prognostic microRNA are present in borderline cases when it comes to decide whether or not to proceed with surgery. MiR-222 has been reproducibly shown to be positively correlated with PTC diagnosis as well as many associated prognostic factors and as a potential diagnostic test for MTC. To a lesser extent, in the case of PTC evidence seems to show some diagnostic and prognostic utility for miR-146b, Let-7 family, miR-221, and miR-21. Comparing sensitivities and specificities for current biomarkers such as thyroglobulin shows that generally individual microRNA have higher sensitivities but lower specificities. Thyroglobulin had a sensitivity of around 57% and a sensitivity of 98% whilst 5 of the most commonly cited microRNA (ie. miR-222, miR-146, miR-221, miR-21, miR-451) have sensitivities ranging from 77-94% and specificities ranging from 50-89%. Since microRNA are involved in cell level metabolic processes present in many different malignancies this is manifested as low test specificities in multiple studies. Further research is however required to reproduce many of the individual finding claims and to identify panels of interest. In particular miR-21 was suggested for further study in FTC diagnosis by Samsonov et al [47]. Other potential miRNAs of interest are miR-190, miR-95 which together could represent a strong diagnostic tool for PTC with an AUC of 99% as found by Cantara et al. [52]. Further evaluation of the proposed diagnostic power of miR-451 and miR-17-5p for MTC is needed.

Major hurdles remain in assuring accurate, sensitive, reproducible and unbiased data as some of the existing study results appear to be contradicting. Possible reasons for this are differences in the used lab protocols and PCR kits, especially as the examined publications span from 2013 to 2021. The development of more consistent microarray testing technology would eliminate selection bias when studying microRNAs. There is also a need to establish a widely used and comprehensive protocol for sample collection and processing with emphasis on data normalization for PCR to further reduce process variables. Enrolment of sufficient study participants to achieve good statistical power is a challenge for the less common cancers like FTC and MTC. Further research is needed to better understand the roles of different microRNAs and their changes in expression in human physiology and pathophysiology related to common non-oncologic conditions as this could account for some of the discrepant findings between studies.

Progress is being made to better understand the mechanics of microRNA expression, with further research they hold potential as powerful tools for easier, cheaper, and quicker diagnosis as well as improved mortality and morbidity.

Footnotes

Acknowledgments

No funding was acquired for this paper.

Author contributions

Conception: Arvind Arya.

Interpretation or analysis of data: Cezary Bielak.

Preparation of the manuscript: Cezary Bielak.

Revision for important intellectual content: Arvind Arya, Stuart Savill.

Supervision: Arvind Arya.

References

Thyroid cancer incidence statistics, Cancer Research UK (2021), Available from: https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/thyroid-cancer/incidence.

Perros

Colley

and Boelaert

, Guidelines for the management of thyroid cancer, Clinical Endocrinology (2014).

Davies

and Welch

, Increasing Incidence of Thyroid Cancer in the United States, 1973–2002, JAMA (2006), 2164–2167.

Dijkstra

and Prichard

, Changing patterns of thyroid carcinoma, Irish Journal of Medical Science (2007), 87–90.

Dean

, Epidemiology of thyroid nodules, Best Practice & Research Clinical Endocrinology & Metabolism (2008), 901–911.

Karamanakos

and Markou

, Complications and risk factors related to the extent of surgery in thyroidectomy. Results from 2,043 procedures, Hormones (2010), 318–325.

Kinder

, Well differentiated thyroid cancer, Current Opinion in Oncology (2003), 71–77.

Grogan

Mitmaker

and Clark

, The Evolution of Biomarkers in Thyroid Cancer – From Mass Screening to a Personalized Biosignature, Cancers (2010), 885–912.

Schneider

et al., Calcitonin screening in patients with thyroid nodules. Diagnostic value, Nuklearmedizin (2012), 228–233.

10.

Perros

et al., British thyroid association guidelines for the management of thyroid cancer, Clinical Endocrinology (2014), 1–122.

11.

Lubitz

et al., Detection of Circulating BRAF V600E in Patients with Papillary Thyroid Carcinoma, The Journal of Molecular Diagnostics (2016), 100–108.

12.

Zahra

et al., Prognostic value of serum thyroglobulin and anti-thyroglobulin antibody in thyroid carcinoma patients following thyroidectomy, Diagnostics (Basel) (2021), 2080.

13.

Wang

and Yan-feng

, Overexpression miR-211-5p hinders the proliferation, migration, and invasion of thyroid tumor cells by downregulating SOX11, Journal of Clinical Laboratory Analysis (2018).

14.

MacFarlane

and Murphy

, MicroRNA: Biogenesis, Function and Role in Cancer, Current Genomics (2010), 537–561.

15.

Dai

and Limin

, Clinical value of miRNA-122 in the diagnosis and prognosis of various types of cancer, Oncology Letters (2019), 3919–3929.

16.

Weige

and Bodu

, MicroRNAs and cancer: Key paradigms in molecular therapy (Review), Oncology Letters (2018), 2735–2742.

17.

Swierniak

et al., In-Depth Characterization of the MicroRNA Transcriptome in Normal Thyroid and Papillary Thyroid Carcinoma, The Journal of Clinical Endocrinology & Metabolism 98(8) (2013), E1401–E1409.

18.

Marques-Rocha

et al., Ioncoding RNAs, cytokines, and inflammation-related diseases, The FASEB Journal 29(9) (2015), 3595–3611.

19.

Coskunpinar

et al., Circulating miR-221-3p as a novel marker for early prediction of acute myocardial infarction, Gene 591(1) (2016), 90–96.

20.

Celano

et al., MicroRNAs as Biomarkers in Thyroid Carcinoma, International Journal of Genomics 2017 (2017), 1–11.

21.

Wang

et al., Comparing the MicroRNA Spectrum between Serum and Plasma, PLoS ONE 7(7) (2012), e41561.

22.

Kim

et al., Plasma Components Affect Accuracy of Circulating Cancer-Related MicroRNA Quantitation, The Journal of Molecular Diagnostics 14(1) (2012), 71–80.

23.

Hastings

Palma

and Duelli

, Sensitive PCR-based quantitation of cell-free circulating microRNAs, Methods 58(2) (2012), 144–150.

24.

Mitchell

et al., Circulating microRNAs as stable blood-based markers for cancer detection, Proceedings of the National Academy of Sciences 105(30) (2008), 10513–10518.

25.

Ach

Wang

and Curry

, Measuring microRNAs: Comparisons of microarray and quantitative PCR measurements, and of different total RNA prep methods, BMC Biotechnology 8(1) (2008), 69.

26.

Moody

et al., Methods and novel technology for microRNA quantification in colorectal cancer screening, Clinical Epigenetics 9(1) (2017).

27.

Lee

et al., MicroRNA-222 and MicroRNA-146b are tissue and circulating biomarkers of recurrent papillary thyroid cancer, Cancer 119(24) (2013), 4358–4365.

28.

et al., Circulating MicroRNA Profiles as Potential Biomarkers for Diagnosis of Papillary Thyroid Carcinoma, The Journal of Clinical Endocrinology & Metabolism 97(6) (2012), 2084–2092.

29.

Rosignolo

, Identification of Thyroid-Associated Serum microRNA Profiles and Their Potential Use in Thyroid Cancer Follow-Up, Journal of the Endocrine Society (2017), 3–13.

30.

Zhang

et al., Combination of serum microRNAs and ultrasound profile as predictive biomarkers of diagnosis and prognosis for papillary thyroid microcarcinoma, Oncology Reports (2018).

31.

Jayawant

, Receiver Operative Characteristic Curve in Diagnostic Test Assessment, Journal of Thoracic Oncology (2010), 1315–1316.

32.

et al., Circulating microRNA124-3p, microRNA9-3p and microRNA196b-5p may be potential signatures for differential diagnosis of thyroid nodules, Oncotarget 7(51) (2016), 84165–84177.

33.

Lee

et al., Differential expression levels of plasma-derived miR-146b and miR-155 in papillary thyroid cancer, Oral Oncology 51(1) (2015), 77–83.

34.

Yoruker

et al., MicroRNA Expression Profiles in Papillary Thyroid Carcinoma, Benign Thyroid Nodules and Healthy Controls, Journal of Cancer 7(7) (2016), 803–809.

35.

Zhang

et al., Altered Serum MicroRNA Profile May Serve as an Auxiliary Tool for Discriminating Aggressive Thyroid Carcinoma from Nonaggressive Thyroid Cancer and Benign Thyroid Nodules, Disease Markers 2019 (2019), 1–11.

36.

Wang

and Sang

, Emerging roles and mechanisms of microRNA-222-3p in human cancer (Review), International Journal of Oncology (2021), 20.

37.

Graham

et al., Serum microRNA profiling to distinguish papillary thyroid cancer from benign thyroid masses, Journal of Otolaryngology – Head & Neck Surgery 44(1) 2015.

38.

Saba

Sorensen

and Booth

, MicroRNA-146a: a dominant, negative regulator of the innate immune response, Frontiers in Immunology (2014).

39.

Jiang

and Li

, Plasma Exosomal miR-146b-5p and miR-222-3p are Potential Biomarkers for Lymph Node Metastasis in Papillary Thyroid Carcinomas, Oncotargets and Therapy (2020), 1311–1319.

40.

Abak

and Amini

, MicroRNA-221: biogenesis, function and signatures in human cancers, European Review for Medical and Pharmacological Sciences (2018).

41.

Chirshev

Oberg

Ioffe

and Unternaehrer

, Let-7 as biomarker, prognostic indicator, and therapy for precision medicine in cancer, Clinical and Translational Medicine 8(1) (2019).

42.

Blazeau

and Menezes

, The LIN28/let-7 Pathway in Cancer, Frontiers in Genetics (2017).

43.

et al., Circulating MicroRNA Profiles as Potential Biomarkers for Diagnosis of Papillary Thyroid Carcinoma, The Journal of Clinical Endocrinology & Metabolism 97(6) (2012), 2084–2092.

44.

et al., Correction: Circulating miR-25-3p and miR-451a May Be Potential Biomarkers for the Diagnosis of Papillary Thyroid Carcinoma, Plos One 10(8) (2015), e0135549.

45.

Zhang

46.

Jenike

and Halushka

, miR-21: a non-specific biomarker of all, Biomarker Research (2021).

47.

Samsonov

et al., Plasma exosomal miR-21 and miR-181a differentiates follicular from papillary thyroid cancer, Tumor Biology 37(9) (2016), 12011–12021.

48.

Kondrotienė

et al., Plasma-Derived miRNA-222 as a Candidate Marker for Papillary Thyroid Cancer, International Journal of Molecular Sciences 21(17) (2020), 6445.

49.

Zhang

et al., MicroRNA-451 as a prognostic marker for diagnosis and lymph node metastasis of papillary thyroid carcinoma, Cancer Biomarkers 19(4) (2017), 437–445.

50.

Hua

and Wi

, miR-451: A Novel Biomarker and Potential Therapeutic Target for Cancer, OncoTargets and Therapy (2019), 11069–11082.

51.

Yue

and Chen

, miR-151-3p Inhibits Proliferation and Invasion of Colon Cancer Cell by Targeting Close Homolog of L1, Journal of Biomedical Nanotechnology (2020), 876–884.

52.

Cantara

et al., Circulating miRNA95 and miRNA190 Are Sensitive Markers for the Differential Diagnosis of Thyroid Nodules in a Caucasian Population, The Journal of Clinical Endocrinology & Metabolism 99(11) (2014), 4190–4198.

53.

Yue

and Cao

, miR-190-5p in human diseases, Cancer Cell International (2019).

54.

Hong

and Huang

, MicroRNA-95-3p inhibits cell proliferation and metastasis in colorectal carcinoma by HDGF, Biomedical Journal (2020), 163–173.

55.

Zheng

and Li

, Serum miRNA-203 expression, a potential biomarker for recurrence and prognosis in papillary thyroid carcinoma, Cancer Biomarkers (2016), 1–7.

Circulating microRNA as potential diagnostic and prognostic biomarkers of well-differentiated thyroid cancer: A review article

Abstract

Keywords

1. Introduction

1.1 Current serum biomarkers

1.2 MicroRNA

1.3 Detection of miRNA

2. Materials and methods

Table 1 Breakdown of study findings

2.2 miRNA-146

2.3 miRNA-221

2.4 Let-7 (family)

2.5 miRNA-21

2.6 miRNA-451

2.7 miRNA-151

2.8 miRNA-190 and miRNA-95

2.9 Other miRNAs

3. Concluding remarks

Footnotes

Acknowledgments

Author contributions

References

Table 1
Breakdown of study findings