Identified 20 years ago, microRNAs (miRNAs) are non-coding single strand small RNA molecules of about 22 nucleotides in length that act as post-transcriptional regulators of gene expression and control many critical cellular processes. Numerous studies have reported aberrant expression of miRNAs in a range of different pathologies, with striking alterations in tumor tissues (1). Profiling of miRNAs has contributed to the molecular classification of tumors according to cancer type and prognosis (2). In 2008, the presence of miRNAs was reported in body fluids (urine, serum, plasma, etc. …) allowing non-invasive identification of individuals with cancer (3), (4), (5). Exponentially growing evidence shows that measurements of miRNAs in serum or plasma can provide valuable non-invasive biomarkers for detection of various human cancers (6), (7). Herein, a general overview of the utility of circulating miRNAs as cancer biomarkers will be presented with an emphasis on the more recent findings on several types of cancer.
Biogenesis of miRNAs
miRNAs are transcribed in the nucleus by RNA polymerase II as long primary microRNA (pri-miRNA) precursor molecules whose lengths vary greatly (up to 3–4 kb miRNAs) (8). They are then processed by the ribonuclease III Drosha associated with a partner called DGCR8, into pre-miRNAs, 60–70 nt long (9), that are exported to the cytoplasm by exportin 5 and its partner Ran-GTP (10). The second RNAse III Dicer removing the loop of the pre-miRNA then generates a small double strand RNA of about 22 nt. Dicer is associated with a catalytic complex called the RNA-induced silencing complex (RISC) for miRNA-mediated post-transcriptional gene silencing with the transactivation responsive RNA-binding protein (TRBP) that enhances the fidelity of the cleavage and recruits to the Argonaute (AGO) proteins, the catalytic engine of RISC (11). AGO loads the mature strand of the miRNA, while the passenger strand is dissociated and degraded (12), resulting in a fully active miRNA [for an extensive review, see Ref. (13)]. In the past, both the strands of a microRNA gene were named miR and miR*, the asterisk indicating that the miR is considered as a ‘minor’ product, found at lower concentration, and inferred that miR* is non-functional. But several miRs* have proven to be functional. For clarification a new nomenclature was adopted. miRNAs originating from the 3′ end or 5′ end of the microRNA gene are denoted with a ‘-3p’ or ‘-5p’ suffix, respectively.
The binding of the complex RISC to mRNA is mediated by a sequence of 2–8 nucleotides, known as the seed region, at the 5′ end of the mature miRNA (14). The complex binds to the target mRNA, via its partially complementary sequence, most often in the 3′ but sometimes in the 5′ untranslated region (15), in the open reading frame (16) or in the promoter regions (17). miRNAs inhibit gene expression through several mechanisms, depending on the degree of complementarity between the small RNA and its mRNA target (18). It was reported that the mRNA cleavage is induced when the homology is perfect (19), but mRNAs containing partial miRNA complementary sites can also be targeted for degradation in vivo (20). miRNAs could also induce repression of mRNA translation, at the level of translation initiation (21) and as of post-initiation (22). It has also been shown that miRNAs can upregulate the expression of their target genes (23). For extensive information on the modes of miRNA actions, see the reviews of Morozova et al. (24) and James et al. (25).
A single miRNA can target up to a hundred genes due to the imperfect matching outside the seed sequence (26) and conversely a single mRNA could be controlled by several miRNAs. The latest version of miRBase (release 21) has annotated 2588 human mature miRNAs sequences, which can target more than 30% of the genome (27). An analysis has even suggested that more than 80% of the gene transcripts are likely under microRNA control through their untranslated and amino acid-coding regions (28). Therefore the miRNAs play a critical role in multiple biological processes including proliferation, differentiation, apoptosis and hematopoiesis (29). Thus it is quite obvious that a dysregulation of miRNA expression leads to a number of pathologies such as inflammation, cardiovascular diseases, neurological disorders and several types of cancer.
miRNAs in cancer
miRNAs contribute to cancer by regulating either oncogenes (tumor suppressor miRNA) or tumor suppressors (oncomiRs). Most of the time oncomiRs, such as miR-17-92 cluster (30) or miR-21 (31), are overexpressed and tumor suppressor miRNAs, such as let-7 family, are downregulated (32). Gain and loss of function experiments have provided insights into the role of miRNA in oncogenesis. For instance, enforced expression of the miR-17-92 cluster that codes for miR-17-3p, -17-5p, -18a, -20a, -19a, -19b and -92, participates in the tumor development in a mouse B-cell lymphoma model by inhibition of the apoptotic pathway and cell cycle (30). In contrast, early studies showed that overexpression of miRNAs of the let-7 family-inhibited tumor formation, progression and metastasis can induce apoptosis through targeting many signaling pathways (RAS, c-MYC, cyclin D1/2/3, cyclin A, CDK4/6, etc. …) (33). But more recent studies show oncogenic functions of let-7 repressing the tumor-suppressive caspase-3 and BAX genes (34), (35). Several other miRNAs can act as oncomiR as well as tumor suppressor depending on the context (36). For instance, miR-155 was often considered as an oncomiR in different cancer types (36) such as pancreatic cancer and lymphoma (37), (38) and its overexpression induces B cell malignancy in mice (39). However, several groups reported that miR-155 displays tumor suppressive role in melanoma, gastric and ovarian cancers where it is downregulated (40), (41), (42).
Dysregulated miRNAs expression causes a loss of control of critical biological processes – proliferation, differentiation, apoptosis, EMT, migration – leading to oncogenesis. miR-21, a miRNA ubiquitously upregulated in cancer is the best example of this (43). miR-21 affects all major pathways of carcinogenesis (proliferation, apoptosis, angiogenesis and invasion), through its multiple targets including PTEN (phosphatase and tensin homolog) (44), PDCD4 (tumor suppressor gene tropomyosin 4) (45) FasL (pro-apoptotic FAS ligand) (46), and TIMP3 (metalloproteinase inhibitor 3 precursor) (47). Many miRNAs are now known to be regulators of metastasis, interfering with the different steps of the metastatic cascade (cell adhesion, migration, EMT, etc.) (48). The miR-200 family is well known as a regulator of EMT, targeting key transcription factors such as ZEB-1 and ZEB-2 (49). The transcription of miR-10b is positively regulated by Twist 1 and in turn increases the expression of RHOC, involved in metastasis (50). Similarly overexpression of miR-21 promotes a metastatic phenotype by targeting the tumor suppressor RHOB (51).
The biogenesis of miRNAs is a tightly controlled process but it has become evident that miRNAs are deregulated in cancers. Calin et al. were the first to report a deregulation of miRNAs in cancer (52). They showed that the loci miR-15-16 is deleted in patients with B cell chronic lymphocytic leukemia. Then an exponentially growing number of publications showed that miRNAs expression is altered in cancer (53), (54), (55), (56). The causes of miRNA aberrant expression in cancer are multiple. Half of the miRNA genes are localized in fragile sites or in cancer-associated genomic regions amplified or translocated in cancer such as miR-15 and miR-16 on the 13q14 locus (57). A CGH array screening 227 tumors has shown that 37.1%–85.9% of miRNAs exhibit DNA copy number alterations, correlating with miRNA expression (58). Moreover an alteration of the expression or function of enzymes of the miRNA processing, such as Drosha, Dicer 1 or DGCR8, was reported (59). A decrease of Dicer or Drosha has been observed in ovarian cancer associated with poor survival (60) and in bladder cancer (61) while their upregulation occurs in cervical squamous cell carcinoma (62), and gastric cancers (63). An analysis of gene expression in primary tumors indicates that the widespread downregulation of miRNAs observed in cancer is due to a failure at the Drosha-processing step (64). Somatic mutations of the microRNA-processing enzymes such Drosha, DGCR8 or Dicer 1 has also been observed notably in nephroblastomas (65). Splice variants of Drosha encoding truncated proteins were found in melanoma (66). Many studies indicate that the microRNA expression may be also regulated by different epigenetic mechanisms leading to the silencing of the tumor suppressor microRNA including abnormal methylation of the promoter regions (67) or histone modifications (68). Another important level of regulation of microRNA expression is its transcriptional control. The alteration of activators or repressors of pri-miRNA transcription results in miRNA level defects. The miR-34 family is known to be regulated by p53 and to be partially responsible for the onco-suppressor-induced phenotype (59), (69). Myc regulates the transcription of the oncogenic miR-17-92 cluster (70), (71) while Twist1 suppresses let-7i via binding to its promoter, to activate mesenchymal mode migration (50), (72) and c-Met via the transcription factors c-Jun and AP1 induces the oncomiR miR-221-222 cluster (73). Forkhead box (FOX) transcriptions factors as well as Hippo-signaling pathway were recently shown to regulate the miRNA transcription as well (74), (75).
Circulating miRNAs as biomarkers in cancer
Several profiling studies with microarrays, among them the study of Rosenfeld and coworkers who investigated 400 samples from 22 different tumors and metastases, have shown that miRNAs’ expression signatures permit, with high accuracy, tumor classification, according to the tissue of origin (76), (77), (78). The tumor tissues could be distinguished from normal tissues in CLL (79) lung (80), (81), breast (82), or prostate cancer (PCa) (83), (84). Moreover, besides its diagnostic utility, it turns out that the profiling of miRNAs might also be a useful tool for prognosis, prediction of metastatic outcome and therapeutic response (78), (85), (86), (87).
In 2008, princeps studies reported the presence of miRNAs in plasma and serum (3), (4), (5), (88) and that, between healthy donors and those patients with cancer or diabetes, the profiles of serum miRNAs differ (3). It was then observed that miRNAs were present in all of the 12 body fluids assessed, including plasma, urine, saliva, peritoneal fluid, pleural fluid, seminal fluid, tears, amniotic fluid, breast milk, bronchial lavage, cerebrospinal fluid and colostrum (89). The concentration and the profiles of the miRNA vary between the diverse fluids. Human urine has the lowest concentration and diversity of miRNA while breast milk displays a huge concentration and a number of miRNAs (89). On the other hand, some reports described higher miRNA concentrations in serum samples compared to the corresponding plasma samples (5), (90) in contrast to results shown by McDonald et al. (91). The discrepancy between these observations may be the result of an miRNA release from blood cells during the coagulation process (90).
In the blood, the circulating miRNAs are packaged in extracellular vesicles – microvesicles, exosomes or apoptotic bodies – (92) or complexed to RNA-binding proteins, AGO2 (93), (94) or nucleophosmin (95), but also to HDL (96), (97). It has been proposed that a large majority of plasma miRNAs are complexed with AGO proteins, while the miRNAs packaged into vesicles are poorly represented (93), (94), (95), but other studies contradicted these results (97), (98), (99). The precise mechanisms of the release of the miRNAs into extracellular compartment are not yet completely understood. The miRNAs could be released by a passive mode in pathological conditions such as necrosis, apoptosis or inflammation or by an active and selective process or a combination of both (100). The exosomal miRNAs appear to be selectively recruited and actively secreted in a regulatory manner (48), (101). Indeed the exosomal and donor cell miRNA profiles differ as reported in several publications (102), (103), (104), (105). Some miRNAs are concentrated in exosomes while the expression of most of them is lower in exosomes vs. cells (102), (106). While the precise mechanism of the regulation of the miRNAs release is not yet fully deciphered, RAB proteins, essential regulators of intracellular vesicle transport, have emerged as the key regulators of exosome secretion (107). Moreover it has been proposed that the exosome secretion is triggered by a ceramide-dependent pathway (6), (105), (108).
The circulating extracellular miRNAs are believed to play an important role in intercellular and inter-organ communication. Several mechanisms of internalization of extracellular miRNAs by recipient cells have been proposed (109), (110). They could be internalized to elicit their regulatory functions, notably either (i) by endocytosis, phagocytosis, or by direct fusion of the vesicles with the plasma membranes of the recipient cells, or (ii) by uptake by cell surface receptors of the complexes with AGO2. Extracellular miRNAs are able to promote multiple biological processes in the recipient cells and tissues, such as proliferation, invasion, metastasis and angiogenesis (48), (110), (111), (112). But a recent study has challenged the existence of exosomal miRNAs (113). By using a new exosome quantification technique, Chevillet et al. (113), (114) have observed that most of them do not carry any miRNAs, bringing into question their involvement in cell-cell communication.
However, it is now well established that the circulating miRNAs are linked to cancer and appear to be promising diagnostic and prognostic, non-invasive biomarkers in various cancers. Indeed, the circulating miRNAs display several meaningful properties making them good potential biomarkers. They show a remarkable stability in bodily fluids, where they are protected from endogenous RNAse’s activity by vesicles or carrier proteins, while miRNAs added exogenously are quickly degraded (3), (4), (5). The circulating miRNAs resist prolonged incubation at room temperature and to multiple freeze-thaw cycles (5). Moreover, the circulating miRNAs show constant homogeneous expression in healthy individuals, derived essentially from blood cells (3). By contrast, in cancer patients, most of the circulating miRNAs appeared to be directly derived from tumor tissues and may reflect the tumor burden. Indeed, in cancer patients, the plasma or serum miRNA profiles correlate with the tumor tissues’ profile. On the other hand, several studies demonstrated that circulating oncogenic miRNA levels decreased after tumor resection (115).
A recent survey of 148 published reports in the eight most prominent cancers reports a total of 279 deregulated circulating miRNAs in serum and plasma from cancer patients to healthy donors (116). A tremendous number of publications proposed that the circulating miRNAs, especially in serum and plasma, could potentially be used as diagnostic, prognostic and predictive biomarkers for different types of tumors (6), (29), (100), (116), (117), (118), (119), (120), (121), (122), (123).
It is well established that the early detection of cancer significantly improves outcomes for patients. Late diagnosis is one of the most prevalent reasons for the high mortality rate in cancer notably in lung cancer. Thus, very sensitive and specific tools are needed. Currently the classical diagnostic methods, such as CT-scan and mammography are expensive, could be dangerous when repeated, and their specificity and sensitivity are not optimal. Moreover, in the era of personalized therapies, the need for non-invasive biomarkers to get iterative information on the pathology is critical and has led to the research of circulating biomarkers since repeated biopsies are not feasible and often associated with morbidity. A rapid and non-invasive access to the molecular profile of tumors is a current challenge. The liquid biopsies, circulating tumor cells, circulating-free DNA, and miRNAs isolated from the blood of patients emerged as potential tools and are one of the most active areas of translational research in several types of cancer.
Lung cancer is the leading cause of cancer deaths in developed countries, with non-small cell lung cancer (NSCLC) that accounts for the majority of cases. Late diagnosis is one of the most prevalent reasons for the high mortality rate. The overall 5-year survival rate is no more than 15%. Besides the need for early detection of pathology, a non-invasive way to characterize the molecular profile of tumors over time is required given the increasing number of targeted therapies available for the treatment of NSCLC and its dynamic changes through cancer treatment.
Several original publications and reviews have reported that the levels of multiple miRNAs are altered in lung cancer. Recently Zhao et al. (124) have listed among different studies from 2011 to 2015, 39 miRNAs upregulated and 18 miRNAs downregulated, that correlated notably with clinical stages, metastasis or early lung cancer. A meta-analysis based on 28 publications with a total of 2121 patients and 1582 healthy ones shows that miRNA may serve as a potential biomarker in NSCLS detection, especially from blood, with a high diagnostic accuracy (125). Moreover, Ulivi and colleagues have analyzed 28 publications between 2009 and 2014 that compared microRNA levels in serum, plasma and sputum from lung cancer patients to healthy donors and summarizes the promising miRNAs for lung cancer diagnosis (126).
Many other excellent reviews have described the role of miRNAs notably in diagnosis, prognosis and therapeutic response in lung cancer (29), (100), (116), (117), (119), (122), (124), (126), (127), (128), (129), (130). Herein we have tried to summarize the most recent publications in Table 1. It is noteworthy that in a large proportion of the publications, miRNA panels are used rather than a single miRNA to discriminate with higher specificity and sensitivity, patients with lung cancer and healthy controls.
For example, in 2011 Bianchi and coworkers developed a test, based on the detection of 34 miRNAs from serum, that could identify patients with early stage NSCLCs in a population of asymptomatic high-risk individuals with 80% accuracy (131). Later, the authors refined this signature to 13 miRNAs maintaining the same performance in order to reduce the costs and complexity of the test and to increase its clinical translatability. This test called miR-Test was validated on a large-scale validation cohort of high-risk patients (n=1115) and showed a sensitivity and specificity of 77.8% and 74.8%, respectively (132). As shown in Table 1, many other signatures appear to be useful and promising tools for diagnosis, prognosis or as a surrogate marker for therapy response. But few overlaps could be found between the different studies.
Breast cancer is the most common cancer in women worldwide, with nearly 1.7 million new cases diagnosed in 2012. This represents about 12% of all new cancer cases and 25% of all cancers in women (147). Mammography and ultrasound imaging are widely used for detecting breast cancer and have helped to improve overall survival, but they are known to have a limited specificity and sensitivity. Moreover breast cancer is a very complex and heterogeneous pathology, determined by established markers such as size, node and IHC profiling of hormone receptors (ER, PR, HER2) status and proliferation marker Ki-67. These markers along with two serum-based tumor biomarkers (CA15-3 and CEA) are used to evaluate individual prognosis but with limited sensitivity and specificity. Reliable blood-based biomarkers are needed to assess prognosis but also diagnosis and response to therapy.
Numerous studies have focused on circulating miRNAs as biomarkers in breast cancer diagnosis, classification and prognosis and reviewed by several teams (29), (100), (116), (117), (119), (122), (148), (149), (150), (151), (152), (153). He et al. have analyzed 35 publications with 2850 breast cancer patients and 1479 health controls, and identified 106 (61 in plasma and 45 in serum) deregulated circulating miRNAs but only 15 among them miR-21 have been reported by more than one study (116). Recently Li et al. performed a meta-analysis of six studies with 438 patients and 228 healthy controls and indeed showed that miR-21 could be an accurate biomarker for early diagnosis (154). miRNA-155 was also reported as a diagnostic miRNA in breast cancer (153). Both these miRNA could be used also as a prognostic biomarker like miR-205 and miR-30a (153). miR-10b was associated with metastatic dissemination (155) and miR-210 or miR-155 to therapy response (151). As for lung cancer, many signatures of several miRNAs have also been identified as tools for early diagnosis, tumor staging or monitoring recurrence (Table 2). A recent study performed on 1206 cancer samples compared to healthy samples has found out a signature of five miRNAs (miR-1246, miR-1307, miR-4634, miR-6861 and miR-6875) as an accurate tool for early detection of breast cancer (156).
CRC is the third most common cancer and a leading cause of cancer-related death worldwide. Early diagnosis of CRC is a pre-requisite for proper management of the patient and increasing survival. Currently, colonoscopy is the gold standard for early diagnosis of CRC but its invasiveness is a big limitation; up to 12% of precancerous lesions miss detection and approximately 10% of CRCs occur in individuals within 3 years of a screening colonoscopy. Serum markers CEA and CA19-9 are used but are not sufficiently sensitive nor specific. Therefore, non-invasive and highly sensitive approaches are urgently needed for CRC screening. Several studies have shown that serum and plasma could detect CRC with high accuracy (100), (117), (119), (164), (165). He and coworkers have analyzed 25 studies with 2146 patients and 1267 healthy controls and found 78 deregulated miRNAs, among them miR-21, miR-29a and miR-15b were found in more than one study (116). A recent meta-analysis of nine studies has suggested miR-21 as a potential biomarker for CRC, with a pooled sensitivity and specificity of 72% and 85%, respectively (166) as shown previously (167). In contrast, Montagnana et al. have contested the use of miR-21 plasma levels as a diagnosis and staging CRC tool (168). Interestingly, it was reported that in addition to the changes in the level of the circulating miRNAs, miRNA polymorphisms could predict risk from CRC such as miR-146a polymorphism (Rs2910164) (169). Subgroup and meta-regression analyses of 19 articles demonstrated that multiple miRNA measurements display a higher predictive accuracy than a single miRNA in detecting CRC (170). In Table 3 we have summarized the most recent publications regarding the role of circulating miRNAs as diagnostic and prognostic tool in CRC. A panel of miRNAs (miR-31, miR-29c, miR-122, miR-192, miR-346, miR-372, miR-374c) was shown to have a very high specificity and sensitivity to discriminate CRC from adenoma when analyzed both in plasma and stool (171).
Melanoma is the deadliest form of skin cancer with an increasing incidence worldwide. Its early diagnosis is important because the majority of the localized stages are curable and cure rates are <15% for patients at AJCC stage IV (181). At present, there is no curative therapy for advanced stages of the disease. Currently, lactate dehydrogenase (LDH) is the only AJCC circulating biomarker approved and used in metastatic disease but LDH has a very low specificity. Thus, the identification of efficient noninvasive biomarkers is necessary to improve early systemic melanoma recurrence and/or response to treatment. Circulating miRNAs related to melanoma remain less explored than those of other cancers. However, several studies demonstrating the potential interest of miRNA in melanoma were reviewed recently (182), (183) and the most recent are listed in Table 4. In 2011, the first study assessing the diagnostic role of the circulating miRNAs in melanoma conducted by Kanemaru et al. showed that the levels of miR-221, known to be increased in melanoma tissues, were increased in the serum of the metastatic patients and were correlated with tumor thickness. Thus the authors proposed that the serum level of miR-221 might be useful for diagnosis, staging, monitoring of the patients and prognosis (184). Then in 2013, for the first time, plasma levels of miR-21 were described to be elevated in melanoma and correlated to tumor mass (185). Usefulness of the panels of the circulating miRNAs in melanoma was also demonstrated such as a serum-based of 4 miRNA signature (miR-15b, miR-30d, miR-150 and miR-425) that predicts recurrence (186) or the ‘MELmiR-7’ panel that detects the presence of melanoma with a high sensitivity (93%) and a specificity (≥82%) (187). Moreover the co-detection of miR-185 and miR-1246 in plasma allows an accurate discrimination of patients with metastatic melanoma from healthy individuals with a sensitivity of 90.5% and a specificity of 89.1% (188).
Epithelial ovarian cancers (EOC), which account for 90% of ovarian cancers, are the leading cause of death among gynecological malignancies. The high mortality rate is due to the fact that this pathology is asymptomatic up to an advanced stage and therefore the diagnosis is most often too late. CA-125 is the most routinely used serum biomarker but is not sufficiently specific to diagnose EOC at an early stage. Indeed, CA-125 is only elevated in approximately 50% of stage I. New biomarkers for detecting early stage of EOC remain a major clinical challenge. About 20 studies, on circulating miRNAs, have been published in ovarian cancers (Table 5 and recent reviews, Refs. 193 and 194). Firstly they showed the diagnostic then the prognostic interest of circulating miRNAs. The first study identified a signature of eight exosomal miRNAs (miR-21, miR-141, miR-200a, miR-200b, miR-200c, miR-203, miR-205 and miR-214) in patients with ovarian cancer compared to benign disease (195). Another study, with the largest cohort of ovarian cancer patients, showed increase and decrease of expression of plasma miR-205 and let-7f, respectively, with a high diagnostic accuracy for EOC especially in patients with stage I disease (196). Moreover, a low rate of let-7f was correlated to poor progression-free survival (PFS) (196). The most representative circulating miRNAs in ovarian cancers are miR-21, miR-92, mi-93, miR-141, miR-200a, miR-200b, miR-200c and miR-205 (Table 5 and Refs. 193), (194).
Prostate cancer (PCa) is the most frequently diagnosed tumor in men. PSA (prostate-specific antigen) is the current gold standard biomarker for the diagnosis and response to the treatment of PCa. However, PSA has a low specificity with false positive results in patients with benign prostatic hyperplasia (BPH). Novel biomarkers are needed to distinguish between indolent and aggressive pathology and to reduce the risk of overdiagnosis and overtreatment. Many studies have highlighted the interest of circulating miRNAs in the diagnosis and prognosis of PCa. Recent reviews have been carried out on this subject (205), (206), (207), (208), (209). Moreover we listed the most recent publications in Table 6. The potential diagnostic of circulating miRNAs in PCa was first reported in 2008. Mitchell and coworkers found that serum miR-141 levels can distinguish PCa from healthy controls (5). Subsequently, several miRNA signatures were identified as accurate biomarkers in PCa, such as a panel of five miRNAs (miR-30c, miR-622, miR-1285, let-7c, let-7e) which discriminates PCa from BPH and from healthy controls with a very high accuracy area under curve (AUC of 0.924 and AUC of 0.860, respectively) (210) or a signature of 14 serum miRNAs to identify patients with a low risk of harboring aggressive PCa (211). However, despite the elevated number of studies in PCa, only three miRs were regularly reported: miR-141, miR-375 and miR-21 (Table 6 and refs. 205–209).
Circulating miRNAs analysis methods
A comprehensive overview of the circulating miRNAs studies unveils great differences in the results with a lack of concordance across the different projects (218), (219). This can partially be explained by methodological heterogeneity that affects several steps of the miRNA analysis from the sample collection to the post-analytical steps. Below we will briefly present the main possible factors of the lack of concordance between the most miRNA signatures. Firstly, across the different studies, there is a discordance of the source of the circulating miRNAs such as serum or plasma, of the size of the patients and healthy control cohorts, of the preanalytical factors (such as delay before sample handling, centrifugation speed, storage temperature and time, freeze/thaw cycles, etc.) (90), (218), (220), (221), (222). Recent reports highlight the importance of proper and systematic sample collection, preparation and storage to avoid confounding variables influencing the results (223), (224). Both serum and plasma have been equally analyzed but might exhibit some differences in the miRNA profiles due in part to a release of miRNAs from blood cells or platelets during the coagulation process (90), (218), (221). Some precautions should also be taken with hemolysis that leads to contamination with red blood cell-enriched miRNAs such as miR-486-5p, miR-451, miR-92a, and miR-16. miRNA profiles which might be also susceptible to diurnal variations, fasting, hormonal changes, age of the donors, all parameters not often controlled in the various studies (218), (220), (221), (225), (226).
Others factors are more analytically related including RNA extraction method, measurement platforms, analysis and normalization of data. Several circulating miRNA isolation methods are available, phenol-based techniques associated or not with silica columns, and phenol-free techniques together with columns for RNA isolation (221). Differential efficiencies of these methods but with inconsistencies among the studies have been reported, depending notably on the different techniques of detection (225), (226). Some studies focus on the miRNAs contained within the extracellular vesicles such as exosomes. Many techniques are achievable to isolate vesicles in appreciable quantity and purity (227). The most common method uses differential ultracentrifugation but with several varied protocols (227). More recently polymer-based exosome precipitation solutions have been developed as a more rapid and simple method (227).
The accurate quantification of miRNA in bodily fluids is a difficult step due to their low quantity, their short sequence length, the high sequence conservation among family members, the wide range of miRNA concentration in body fluids and the high levels of interfering molecules measurement. Currently, several methods have emerged including hybridization-based approaches (like microarrays, nCounter Nanostring technology), reverse transcription quantitative PCR arrays (RT-qPCR) and next generation sequencing (NGS) (221), (225). The choice of measurement methods depends on the purpose of the project. RT-qPCR is the most used method because it is easily, quickly performed and has the best sensitivity, specificity, accuracy and reproducibility. Medium throughput profiling of miRNAs based on RTqPCR is possible with plates or microfluidic cards, using TaqMan® hydrolysis probes or locked-nucleic acid primers together with Sybr-green detection, the latter appearing to be more sensitive and specific (218), (222), (228). On the other hand, microarray based on RNA-DNA hybrid capture is used to perform an initial screening at lower cost while NGS technology allows the search for novel miRNAs or different isoforms (218), (222), (228). RT-qPCR is the gold standard technique for validation profiling microarray and NGS results (226).
Given all the variations in the source, the isolation and detection methods as mentioned above, the normalization of the raw data is a critical step to remove variations not related to the biological status (228), (229), (230), (231). For large scale profiling data, a global mean or quantile normalization is commonly used (220), (228). But this method is not suitable for a limited number of miRNAs. Furthermore, to correct variability during the purification step and RTqPCR efficiency, standardization could be done through the use of synthetic spike-in miRNAs (such as cel-miR-39) but that does not take into account the differences of endogenous miRNA levels and release between samples. Following RTqPCR, two quantification strategies are used to determine the levels of miRNA. Firstly relative quantification measures the comparison of the expression levels of the target miRNA and of a reference gene, making the reference gene choice highly critical. Many reference genes have been used; the most described are hsa-miR-16 or RNU6 as endogenous genes and cel-miR-39 as exogenous gene (Tables 1–6 ). However, their choice has been controversial. Indeed miR-16 was shown to be released from hemolytic erythrocytes and several studies report that it is deregulated in cancer (218). RNU6, is a small nucleolar RNA, not an miRNA, therefore the efficiency of its extraction and amplification might be different. Some studies suggest that RNA-U6 may be unsuitable as an endogenous reference gene (231), (232), (233). The use of multiple reference miRNAs instead of a single one is recommended in order to improve accuracy and limit the bias of the potential variation of the selected miRNA as it will provide statistically more significant results and will enable detection of small expression differences (231), (234), (235), (236). The selection of a set of stably expressed genes across the studies could be done using algorithms such as GeNorm or NormFinder. The published reference gene combinations are multiple (Tables 1–6) but no specific combination emerges from the studies. The most efficient standardization method is the use of relative data normalization with endogenous and exogenous reference genes. Further studies are needed to identify universal miRNA references. Secondly, absolute quantification requires a standard curve for each miRNA analyzed from known concentrations of DNA standard molecules. This method is not optimal because it does not consider the influence of RNA quality. Droplet digital PCR (ddPCR) appears as a novel alternative method providing the advantages of absolute quantification without a reference standard curve or an endogenous control.
Since their discovery in 2008, as described here, there is a plethora of publications assessing the use of circulating miRNAs as biomarkers in oncology. Despite the great enthusiasm for their potential in clinical application, currently the circulating miRNA measurement has not yet gone into clinical practice. There is not yet any individual or panel miRNA validated as a biomarker for cancer disease. In fact as shown in the different reviews as well as in Table 1, very few overlaps could be observed across relatively similar studies. Many miRNAs are reported in only one publication. Discordant results are sometimes published such as for miRNA described upregulated or downregulated. Some miRNAs used as reference genes in some publications are shown to be differentially expressed in other publications (miR-16, miR-103, etc.). Moreover, to date, the large majority of studies examined only a limited number of samples. Precautions have to be taken about the cohort composition since correlations of miRNA levels with age, sex and ethnicity have been demonstrated.
Thus before clinical application of the measurement of circulating miRNAs as biomarkers in oncology, several issues have to be overcome. Large-scale inter-laboratory studies have to be performed. New advances in standardization of all the steps in the process of miRNA analysis are required to improve knowledge on these new biomarkers such as choice of the biofluid, limiting contamination from cellular elements, standardization of the preanalytic and analytic methods, choice of a reference gene and normalization method. Overcoming all these challenges is urgently needed to render the promising circulating miRNAs as reliable and sensitive biomarkers from the bench to the bedside.
List of abbreviations
area under curve
benign prostatic hyperplasia
epithelial ovarian cancer
non-small cell lung cancer
receiver operating characteristic curve.
Chen X, Ba Y, Ma L, Cai X, Yin Y, Wang K, Guo J, Zhang Y, Chen J, Guo X, Li Q, Li X, Wang W, Wang J, Jiang X, Xiang Y, Xu C, Zheng P, Zhang J, Li R, Zhang H, Shang X, Gong T, Ning G, Zen K, Zhang CY. Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res 2008; 18: 997–1006. CrossrefGoogle Scholar
Lawrie CH, Gal S, Dunlop HM, Pushkaran B, Liggins AP, Pulford K, Banham AH, Pezzella F, Boultwood J, Wainscoat JS, Hatton CS, Harris AL. Detection of elevated levels of tumour-associated microRNAs in serum of patients with diffuse large B-cell lymphoma. Br J Haematol 2008; 141: 672–5. CrossrefGoogle Scholar
Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK, Pogosova-Agadjanyan EL, Peterson A, Noteboom J, O’Briant KC, Allen A, Lin DW, Urban N, Drescher CW, Knudsen BS, Stirewalt DL, Gentleman R, Vessella RL, Nelson PS, Martin DB, Tewari M. Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci USA 2008; 105: 10513–8. CrossrefGoogle Scholar
Larrea E, Sole C, Manterola L, Goicoechea I, Armesto M, Arestin M, Caffarel MM, Araujo AM, Araiz M, Fernandez-Mercado M, Lawrie CH. New concepts in cancer biomarkers: circulating miRNAs in liquid biopsies. Int J Mol Sci 2016; 17: 627. CrossrefGoogle Scholar
Yi R, Qin Y, Macara IG, Cullen BR. Exportin-5 mediates the nuclear export of pre-microRNAs and short hairpin RNAs. Genes Dev 2003; 17: 3011–16. Google Scholar
Chendrimada TP, Gregory RI, Kumaraswamy E, Norman J, Cooch N, Nishikura K, Shiekhattar R. TRBP recruits the Dicer complex to Ago2 for microRNA processing and gene silencing. Nature 2005; 436: 740–4. CrossrefGoogle Scholar
Lee I, Ajay SS, Yook JI, Kim HS, Hong SH, Kim NH, Dhanasekaran SM, Chinnaiyan AM, Athey BD. New class of microRNA targets containing simultaneous 5′-UTR and 3′-UTR interaction sites. Genome Res 2009; 19: 1175–83. CrossrefGoogle Scholar
Forman JJ, Legesse-Miller A, Coller HA. A search for conserved sequences in coding regions reveals that the let-7 microRNA targets Dicer within its coding sequence. Proc Natl Acad Sci USA 2008; 105: 14879–84. Google Scholar
Yekta S, Shih IH, Bartel DP. MicroRNA-directed cleavage of HOXB8 mRNA. Science 2004; 304: 594–6. Google Scholar
Pillai RS, Bhattacharyya SN, Artus CG, Zoller T, Cougot N, Basyuk E, Bertrand E, Filipowicz W. Inhibition of translational initiation by Let-7 MicroRNA in human cells. Science 2005; 309: 1573–6. CrossrefGoogle Scholar
Selbach M, Schwanhausser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N. Widespread changes in protein synthesis induced by microRNAs. Nature 2008; 455: 58–63. Google Scholar
Miranda KC, Huynh T, Tay Y, Ang YS, Tam WL, Thomson AM, Lim B, Rigoutsos I. A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 2006; 126: 1203–17. CrossrefGoogle Scholar
He L, Thomson JM, Hemann MT, Hernando-Monge E, Mu D, Goodson S, Powers S, Cordon-Cardo C, Lowe SW, Hannon GJ, Hammond SM. A microRNA polycistron as a potential human oncogene. Nature 2005; 435: 828–33. Google Scholar
Baffa R, Fassan M, Volinia S, O’Hara B, Liu CG, Palazzo JP, Gardiman M, Rugge M, Gomella LG, Croce CM, Rosenberg A. MicroRNA expression profiling of human metastatic cancers identifies cancer gene targets. J Pathol 2009; 219: 214–21. CrossrefGoogle Scholar
Zhang R, He Y, Zhang X, Xing B, Sheng Y, Lu H, Wei Z. Estrogen receptor-regulated microRNAs contribute to the BCL2/BAX imbalance in endometrial adenocarcinoma and precancerous lesions. Cancer Lett 2012; 314: 155–65. CrossrefGoogle Scholar
Ryu JK, Hong SM, Karikari CA, Hruban RH, Goggins MG, Maitra A. Aberrant MicroRNA-155 expression is an early event in the multistep progression of pancreatic adenocarcinoma. Pancreatology 2010; 10: 66–73. CrossrefGoogle Scholar
Kluiver J, Poppema S, de Jong D, Blokzijl T, Harms G, Jacobs S, Kroesen BJ, van den Berg A. BIC and miR-155 are highly expressed in Hodgkin, primary mediastinal and diffuse large B cell lymphomas. J Pathol 2005; 207: 243–9. Google Scholar
Costinean S, Zanesi N, Pekarsky Y, Tili E, Volinia S, Heerema N, Croce CM. Pre-B cell proliferation and lymphoblastic leukemia/high-grade lymphoma in E(mu)-miR155 transgenic mice. Proc Natl Acad Sci USA 2006; 103: 7024–9. CrossrefGoogle Scholar
Levati L, Pagani E, Romani S, Castiglia D, Piccinni E, Covaciu C, Caporaso P, Bondanza S, Antonetti FR, Bonmassar E, Martelli F, Alvino E, D’Atri S. MicroRNA-155 targets the SKI gene in human melanoma cell lines. Pigment Cell Melanoma Res 2011; 24: 538–50. CrossrefGoogle Scholar
Li CL, Nie H, Wang M, Su LP, Li JF, Yu YY, Yan M, Qu QL, Zhu ZG, Liu BY. microRNA-155 is downregulated in gastric cancer cells and involved in cell metastasis. Oncol Rep 2012; 27: 1960–6. Google Scholar
Qin W, Ren Q, Liu T, Huang Y, Wang J. MicroRNA-155 is a novel suppressor of ovarian cancer-initiating cells that targets CLDN1. FEBS Lett 2013; 587: 1434–9. Google Scholar
Meng F, Henson R, Wehbe–Janek H, Ghoshal K, Jacob ST, Patel T. MicroRNA-21 Regulates expression of the PTEN tumor suppressor gene in human hepatocellular cancer. Gastroenterology 2007; 133: 647–58. CrossrefGoogle Scholar
Asangani IA, Rasheed SA, Nikolova DA, Leupold JH, Colburn NH, Post S, Allgayer H. MicroRNA-21 (miR-21) post-transcriptionally downregulates tumor suppressor Pdcd4 and stimulates invasion, intravasation and metastasis in colorectal cancer. Oncogene 2008; 27: 2128–36. CrossrefGoogle Scholar
Wang K, Li PF. Foxo3a regulates apoptosis by negatively targeting miR-21. J Biol Chem 2010; 285: 16958–66. Google Scholar
Qi JH, Ebrahem Q, Moore N, Murphy G, Claesson-Welsh L, Bond M, Baker A, Anand-Apte B. A novel function for tissue inhibitor of metalloproteinases-3 (TIMP3): inhibition of angiogenesis by blockage of VEGF binding to VEGF receptor-2. Nat Med 2003; 9: 407–15. Google Scholar
Aleckovic M, Kang Y. Regulation of cancer metastasis by cell-free miRNAs. Biochim Biophys Acta 2015; 1855: 24–42. Google Scholar
Connolly EC, Van Doorslaer K, Rogler LE, Rogler CE. Overexpression of miR-21 promotes an in vitro metastatic phenotype by targeting the tumor suppressor RHOB. Mol Cancer Res 2010; 8: 691–700. CrossrefGoogle Scholar
Calin GA, Dumitru CD, Shimizu M, Bichi R, Zupo S, Noch E, Aldler H, Rattan S, Keating M, Rai K, Rassenti L, Kipps T, Negrini M, Bullrich F, Croce CM. Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci USA 2002; 99: 15524–9. Google Scholar
Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert BL, Mak RH, Ferrando AA, Downing JR, Jacks T, Horvitz HR, Golub TR. MicroRNA expression profiles classify human cancers. Nature 2005; 435: 834–8. Google Scholar
Calin GA, Sevignani C, Dumitru CD, Hyslop T, Noch E, Yendamuri S, Shimizu M, Rattan S, Bullrich F, Negrini M, Croce CM. Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci USA 2004; 101: 2999–3004. CrossrefGoogle Scholar
Zhang L, Huang J, Yang N, Greshock J, Megraw MS, Giannakakis A, Liang S, Naylor TL, Barchetti A, Ward MR, Yao G, Medina A, O’Brien-Jenkins A, Katsaros D, Hatzigeorgiou A, Gimotty PA, Weber BL, Coukos G. microRNAs exhibit high frequency genomic alterations in human cancer. Proc Natl Acad Sci USA 2006; 103: 9136–41. CrossrefGoogle Scholar
Merritt WM, Lin YG, Han LY, Kamat AA, Spannuth WA, Schmandt R, Urbauer D, Pennacchio LA, Cheng JF, Nick AM, Deavers MT, Mourad-Zeidan A, Wang H, Mueller P, Lenburg ME, Gray JW, Mok S, Birrer MJ, Lopez-Berestein G, Coleman RL, Bar-Eli M, Sood AK. Dicer, Drosha, and outcomes in patients with ovarian cancer. N Engl J Med 2008; 359: 2641–50. Google Scholar
Catto JW, Miah S, Owen HC, Bryant H, Myers K, Dudziec E, Larre S, Milo M, Rehman I, Rosario DJ, Di Martino E, Knowles MA, Meuth M, Harris AL, Hamdy FC. Distinct microRNA alterations characterize high- and low-grade bladder cancer. Cancer Res 2009; 69: 8472–81. CrossrefGoogle Scholar
Muralidhar B, Winder D, Murray M, Palmer R, Barbosa-Morais N, Saini H, Roberts I, Pett M, Coleman N. Functional evidence that Drosha overexpression in cervical squamous cell carcinoma affects cell phenotype and microRNA profiles. J Pathol 2011; 224: 496–507. CrossrefGoogle Scholar
Tchernitsa O, Kasajima A, Schafer R, Kuban RJ, Ungethum U, Gyorffy B, Neumann U, Simon E, Weichert W, Ebert MP, Rocken C. Systematic evaluation of the miRNA-ome and its downstream effects on mRNA expression identifies gastric cancer progression. J Pathol 2010; 222: 310–9. CrossrefGoogle Scholar
Thomson JM, Newman M, Parker JS, Morin-Kensicki EM, Wright T, Hammond SM. Extensive post-transcriptional regulation of microRNAs and its implications for cancer. Genes Dev 2006; 20: 2202–7. CrossrefGoogle Scholar
Rakheja D, Chen KS, Liu Y, Shukla AA, Schmid V, Chang TC, Khokhar S, Wickiser JE, Karandikar NJ, Malter JS, Mendell JT, Amatruda JF. Somatic mutations in DROSHA and DICER1 impair microRNA biogenesis through distinct mechanisms in Wilms tumours. Nat Commun 2014; 2: 4802. CrossrefGoogle Scholar
Grund SE, Polycarpou-Schwarz M, Luo C, Eichmuller SB, Diederichs S. Rare Drosha splice variants are deficient in microRNA processing but do not affect general microRNA expression in cancer cells. Neoplasia 2012; 14: 238–48. CrossrefGoogle Scholar
Lujambio A, Calin GA, Villanueva A, Ropero S, Sanchez-Cespedes M, Blanco D, Montuenga LM, Rossi S, Nicoloso MS, Faller WJ, Gallagher WM, Eccles SA, Croce CM, Esteller M. A microRNA DNA methylation signature for human cancer metastasis. Proc Natl Acad Sci USA 2008; 105: 13556–61. CrossrefGoogle Scholar
He L, He X, Lim LP, de Stanchina E, Xuan Z, Liang Y, Xue W, Zender L, Magnus J, Ridzon D, Jackson AL, Linsley PS, Chen C, Lowe SW, Cleary MA, Hannon GJ. A microRNA component of the p53 tumour suppressor network. Nature 2007; 447: 1130–4. CrossrefGoogle Scholar
O’Donnell KA, Wentzel EA, Zeller KI, Dang CV, Mendell JT. c-Myc-regulated microRNAs modulate E2F1 expression. Nature 2005; 435: 839–43. Google Scholar
Dews M, Homayouni A, Yu D, Murphy D, Sevignani C, Wentzel E, Furth EE, Lee WM, Enders GH, Mendell JT, Thomas-Tikhonenko A. Augmentation of tumor angiogenesis by a Myc-activated microRNA cluster. Nat Genet 2006; 38: 1060–5. CrossrefGoogle Scholar
Yang WH, Lan HY, Huang CH, Tai SK, Tzeng CH, Kao SY, Wu KJ, Hung MC, Yang MH. RAC1 activation mediates Twist1-induced cancer cell migration. Nat Cell Biol 2012; 14: 366–74. Google Scholar
Garofalo M, Di Leva G, Romano G, Nuovo G, Suh SS, Ngankeu A, Taccioli C, Pichiorri F, Alder H, Secchiero P, Gasparini P, Gonelli A, Costinean S, Acunzo M, Condorelli G, Croce CM. miR-221&222 regulate TRAIL resistance and enhance tumorigenicity through PTEN and TIMP3 downregulation. Cancer Cell 2009; 16: 498–509. CrossrefGoogle Scholar
Li C, Zhang K, Chen J, Chen L, Wang R, Chu X. MicroRNAs as regulators and mediators of forkhead box transcription factors function in human cancers. Oncotarget 2017; 8: 12433–50. Google Scholar
Rosenfeld N, Aharonov R, Meiri E, Rosenwald S, Spector Y, Zepeniuk M, Benjamin H, Shabes N, Tabak S, Levy A, Lebanony D, Goren Y, Silberschein E, Targan N, Ben-Ari A, Gilad S, Sion-Vardy N, Tobar A, Feinmesser M, Kharenko O, Nativ O, Nass D, Perelman M, Yosepovich A, Shalmon B, Polak-Charcon S, Fridman E, Avniel A, Bentwich I, Bentwich Z, Cohen D, Chajut A, Barshack I. MicroRNAs accurately identify cancer tissue origin. Nat Biotechnol 2008; 26: 462–9. CrossrefGoogle Scholar
Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, Visone R, Iorio M, Roldo C, Ferracin M, Prueitt RL, Yanaihara N, Lanza G, Scarpa A, Vecchione A, Negrini M, Harris CC, Croce CM. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA 2006; 103: 2257–61. CrossrefGoogle Scholar
Calin GA, Liu CG, Sevignani C, Ferracin M, Felli N, Dumitru CD, Shimizu M, Cimmino A, Zupo S, Dono M, Dell’Aquila ML, Alder H, Rassenti L, Kipps TJ, Bullrich F, Negrini M, Croce CM. MicroRNA profiling reveals distinct signatures in B cell chronic lymphocytic leukemias. Proc Natl Acad Sci USA 2004; 101: 11755–60. CrossrefGoogle Scholar
Lu Y, Govindan R, Wang L, Liu PY, Goodgame B, Wen W, Sezhiyan A, Pfeifer J, Li YF, Hua X, Wang Y, Yang P, You M. MicroRNA profiling and prediction of recurrence/relapse-free survival in stage I lung cancer. Carcinogenesis 2012; 33: 1046–54. CrossrefGoogle Scholar
Saito M, Schetter AJ, Mollerup S, Kohno T, Skaug V, Bowman ED, Mathe EA, Takenoshita S, Yokota J, Haugen A, Harris CC. The association of microRNA expression with prognosis and progression in early-stage, non-small cell lung adenocarcinoma: a retrospective analysis of three cohorts. Clin Cancer Res 2011; 17: 1875–82. CrossrefGoogle Scholar
Iorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, Sabbioni S, Magri E, Pedriali M, Fabbri M, Campiglio M, Menard S, Palazzo JP, Rosenberg A, Musiani P, Volinia S, Nenci I, Calin GA, Querzoli P, Negrini M, Croce CM. MicroRNA gene expression deregulation in human breast cancer. Cancer Res 2005; 65: 7065–70. CrossrefGoogle Scholar
Schaefer A, Jung M, Mollenkopf HJ, Wagner I, Stephan C, Jentzmik F, Miller K, Lein M, Kristiansen G, Jung K. Diagnostic and prognostic implications of microRNA profiling in prostate carcinoma. Int J Cancer 2010; 126: 1166–76. Google Scholar
Munker R, Calin GA. MicroRNA profiling in cancer. Clin Sci (Lond.) 2011; 121: 141–58. Google Scholar
Gilad S, Meiri E, Yogev Y, Benjamin S, Lebanony D, Yerushalmi N, Benjamin H, Kushnir M, Cholakh H, Melamed N, Bentwich Z, Hod M, Goren Y, Chajut A. Serum microRNAs are promising novel biomarkers. PLoS One 2008; 3: e3148. CrossrefGoogle Scholar
Weber JA, Baxter DH, Zhang S, Huang DY, Huang KH, Lee MJ, Galas DJ, Wang K. The microRNA spectrum in 12 body fluids. Clin Chem 2010; 56: 1733–41. Google Scholar
Valadi H, Ekstrom K, Bossios A, Sjostrand M, Lee JJ, Lotvall JO. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol 2007; 9: 654–9. CrossrefGoogle Scholar
Arroyo JD, Chevillet JR, Kroh EM, Ruf IK, Pritchard CC, Gibson DF, Mitchell PS, Bennett CF, Pogosova-Agadjanyan EL, Stirewalt DL, Tait JF, Tewari M. Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proc Natl Acad Sci USA 2011; 108: 5003–8. CrossrefGoogle Scholar
Vickers KC, Palmisano BT, Shoucri BM, Shamburek RD, Remaley AT. MicroRNAs are transported in plasma and delivered to recipient cells by high-density lipoproteins. Nat Cell Biol 2011; 13: 423–33. CrossrefGoogle Scholar
Wagner J, Riwanto M, Besler C, Knau A, Fichtlscherer S, Roxe T, Zeiher AM, Landmesser U, Dimmeler S. Characterization of levels and cellular transfer of circulating lipoprotein-bound microRNAs. Arterioscler Thromb Vasc Biol 2013; 33: 1392–400. CrossrefGoogle Scholar
Cheng L, Sharples RA, Scicluna BJ, Hill AF. Exosomes provide a protective and enriched source of miRNA for biomarker profiling compared to intracellular and cell-free blood. J Extracell Vesicles 2014; 3: 23743. CrossrefGoogle Scholar
Pigati L, Yaddanapudi SC, Iyengar R, Kim DJ, Hearn SA, Danforth D, Hastings ML, Duelli DM. Selective release of microRNA species from normal and malignant mammary epithelial cells. PLoS One 2010; 5: e13515. CrossrefGoogle Scholar
Skog J, Wurdinger T, van Rijn S, Meijer DH, Gainche L, Sena-Esteves M, Curry WT Jr, Carter BS, Krichevsky AM, Breakefield XO. Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat Cell Biol 2008; 10: 1470–6. CrossrefGoogle Scholar
Collino F, Deregibus MC, Bruno S, Sterpone L, Aghemo G, Viltono L, Tetta C, Camussi G. Microvesicles derived from adult human bone marrow and tissue specific mesenchymal stem cells shuttle selected pattern of miRNAs. PLoS One 2010; 5: e11803. CrossrefGoogle Scholar
Mittelbrunn M, Gutierrez-Vazquez C, Villarroya-Beltri C, Gonzalez S, Sanchez-Cabo F, Gonzalez MA, Bernad A, Sanchez-Madrid F. Unidirectional transfer of microRNA-loaded exosomes from T cells to antigen-presenting cells. Nat Commun 2011; 2: 282. CrossrefGoogle Scholar
Zhong S, Chen X, Wang D, Zhang X, Shen H, Yang S, Lv M, Tang J, Zhao J. MicroRNA expression profiles of drug-resistance breast cancer cells and their exosomes. Oncotarget 2016; 7: 19601–9. CrossrefGoogle Scholar
Matsuda A, Yan IK, Foye C, Parasramka M, Patel T. MicroRNAs as paracrine signaling mediators in cancers and metabolic diseases. Best Pract Res Clin Endocrinol Metab 2016; 30: 577–90. CrossrefGoogle Scholar
Felicetti F, De Feo A, Coscia C, Puglisi R, Pedini F, Pasquini L, Bellenghi M, Errico MC, Pagani E, Care A. Exosome-mediated transfer of miR-222 is sufficient to increase tumor malignancy in melanoma. J Transl Med 2016; 14: 56. CrossrefGoogle Scholar
Chevillet JR, Kang Q, Ruf IK, Briggs HA, Vojtech LN, Hughes SM, Cheng HH, Arroyo JD, Meredith EK, Gallichotte EN, Pogosova-Agadjanyan EL, Morrissey C, Stirewalt DL, Hladik F, Yu EY, Higano CS, Tewari M. Quantitative and stoichiometric analysis of the microRNA content of exosomes. Proc Natl Acad Sci USA 2014; 111: 14888–93. CrossrefGoogle Scholar
Li J, Liu Y, Wang C, Deng T, Liang H, Wang Y, Huang D, Fan Q, Wang X, Ning T, Liu R, Zhang CY, Zen K, Chen X, Ba Y. Serum miRNA expression profile as a prognostic biomarker of stage II/III colorectal adenocarcinoma. Sci Rep 2015; 5: 12921. CrossrefGoogle Scholar
Wang H, Wu S, Zhao L, Zhao J, Liu J, Wang Z. Clinical use of microRNAs as potential non-invasive biomarkers for detecting non-small cell lung cancer: a meta-analysis. Respirology 2015; 20: 56–65. CrossrefGoogle Scholar
Boeri M, Sestini S, Fortunato O, Verri C, Suatoni P, Pastorino U, Sozzi G. Recent advances of microRNA-based molecular diagnostics to reduce false-positive lung cancer imaging. Expert Rev Mol Diagn 2015; 15: 801–13. Google Scholar
Murlidhar V, Ramnath N, Nagrath S, Reddy RM. Optimizing the detection of circulating markers to aid in early lung cancer detection. Cancers (Basel) 2016; 8: E61. Google Scholar
Bianchi F, Nicassio F, Marzi M, Belloni E, Dall’olio V, Bernard L, Pelosi G, Maisonneuve P, Veronesi G, Di Fiore PP. A serum circulating miRNA diagnostic test to identify asymptomatic high-risk individuals with early stage lung cancer. EMBO Mol Med 2011; 3: 495–503. CrossrefGoogle Scholar
Montani F, Marzi MJ, Dezi F, Dama E, Carletti RM, Bonizzi G, Bertolotti R, Bellomi M, Rampinelli C, Maisonneuve P, Spaggiari L, Veronesi G, Nicassio F, Di Fiore PP, Bianchi F. miR-Test: a blood test for lung cancer early detection. J Natl Cancer Inst 2015; 107: djv063. CrossrefGoogle Scholar
Wang P, Yang D, Zhang H, Wei X, Ma T, Cheng Z, Hong Q, Hu J, Zhuo H, Song Y, Jia C, Jing F, Jin Q, Bai C, Mao H, Zhao J. Early detection of lung cancer in serum by a panel of microRNA biomarkers. Clin Lung Cancer 2015; 16: 313–9.e1. CrossrefGoogle Scholar
Yan HJ, Ma JY, Wang L, Gu W. Expression and significance of circulating microRNA-31 in lung cancer patients. Med Sci Monit 2015; 21: 722–6. Google Scholar
Sozzi G, Boeri M, Rossi M, Verri C, Suatoni P, Bravi F, Roz L, Conte D, Grassi M, Sverzellati N, Marchiano A, Negri E, La Vecchia C, Pastorino U. Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: a correlative MILD trial study. J Clin Oncol 2014; 32: 768–73. CrossrefGoogle Scholar
Wozniak MB, Scelo G, Muller DC, Mukeria A, Zaridze D, Brennan P. Circulating microRNAs as non-invasive biomarkers for early detection of non-small-cell lung cancer. PLoS One 2015; 10: e0125026. CrossrefGoogle Scholar
Powrozek T, Krawczyk P, Kowalski DM, Kuznar-Kaminska B, Winiarczyk K, Olszyna-Serementa M, Batura-Gabryel H, Milanowski J. Application of plasma circulating microRNA-448, 506, 4316, and 4478 analysis for non-invasive diagnosis of lung cancer. Tumour Biol 2016; 37: 2049–55. CrossrefGoogle Scholar
Yang JS, Li BJ, Lu HW, Chen Y, Lu C, Zhu RX, Liu SH, Yi QT, Li J, Song CH. Serum miR-152, miR-148a, miR-148b, and miR-21 as novel biomarkers in non-small cell lung cancer screening. Tumour Biol 2015; 36: 3035–42. Google Scholar
Wang RJ, Zheng YH, Wang P, Zhang JZ. Serum miR-125a-5p, miR-145 and miR-146a as diagnostic biomarkers in non-small cell lung cancer. Int J Clin Exp Pathol 2015; 8: 765–71. Google Scholar
Dou H, Wang Y, Su G, Zhao S. Decreased plasma let-7c and miR-152 as noninvasive biomarker for non-small-cell lung cancer. Int J Clin Exp Med 2015; 8: 9291–8. Google Scholar
Fan L, Qi H, Teng J, Su B, Chen H, Wang C, Xia Q. Identification of serum miRNAs by nano-quantum dots microarray as diagnostic biomarkers for early detection of non-small cell lung cancer. Tumour Biol 2016; 37: 7777–84. CrossrefGoogle Scholar
Liu Q, Yu Z, Yuan S, Xie W, Li C, Hu Z, Xiang Y, Wu N, Wu L, Bai L, Li Y. Circulating exosomal microRNAs as prognostic biomarkers for non-small-cell lung cancer. Oncotarget 2016. Google Scholar
Zhang WC, Chin TM, Yang H, Nga ME, Lunny DP, Lim EK, Sun LL, Pang YH, Leow YN, Malusay SR, Lim PX, Lee JZ, Tan BJ, Shyh-Chang N, Lim EH, Lim WT, Tan DS, Tan EH, Tai BC, Soo RA, Tam WL, Lim B. Tumour-initiating cell-specific miR-1246 and miR-1290 expression converge to promote non-small cell lung cancer progression. Nat Commun 2016; 7: 11702. Google Scholar
Nadal E, Truini A, Nakata A, Lin J, Reddy RM, Chang AC, Ramnath N, Gotoh N, Beer DG, Chen G. A novel serum 4-microRNA signature for lung cancer detection. Sci Rep 2015; 5: 12464. CrossrefGoogle Scholar
Zhao Q, Cao J, Wu YC, Liu X, Han J, Huang XC, Jiang LH, Hou XX, Mao WM, Ling ZQ. Circulating miRNAs is a potential marker for gefitinib sensitivity and correlation with EGFR mutational status in human lung cancers. Am J Cancer Res 2015; 5: 1692–705. Google Scholar
Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015; 136: E359–86. CrossrefGoogle Scholar
Guttery DS, Blighe K, Page K, Marchese SD, Hills A, Coombes RC, Stebbing J, Shaw JA. Hide and seek: tell-tale signs of breast cancer lurking in the blood. Cancer Metastasis Rev 2013; 32: 289–302. CrossrefGoogle Scholar
Pimentel F, Bonilla P, Ravishankar YG, Contag A, Gopal N, LaCour S, Lee T, Niemz A. Technology in microRNA profiling: circulating microRNAs as noninvasive cancer biomarkers in breast cancer. J Lab Autom 2015; 20: 574–88. CrossrefGoogle Scholar
Li S, Yang X, Yang J, Zhen J, Zhang D. Serum microRNA-21 as a potential diagnostic biomarker for breast cancer: a systematic review and meta-analysis. Clin Exp Med 2016; 16: 29–35. CrossrefGoogle Scholar
Shimomura A, Shiino S, Kawauchi J, Takizawa S, Sakamoto H, Matsuzaki J, Ono M, Takeshita F, Niida S, Shimizu C, Fujiwara Y, Kinoshita T, Tamura K, Ochiya T. Novel combination of serum microRNA for detecting breast cancer in the early stage. Cancer Sci 2016; 107: 326–34. CrossrefGoogle Scholar
Lyng MB, Kodahl AR, Binder H, Ditzel HJ. Prospective validation of a blood-based 9-miRNA profile for early detection of breast cancer in a cohort of women examined by clinical mammography. Mol Oncol 2016; 10: 1621–6. CrossrefGoogle Scholar
Freres P, Wenric S, Boukerroucha M, Fasquelle C, Thiry J, Bovy N, Struman I, Geurts P, Collignon J, Schroeder H, Kridelka F, Lifrange E, Jossa V, Bours V, Josse C, Jerusalem G. Circulating microRNA-based screening tool for breast cancer. Oncotarget 2016; 7: 5416–28. CrossrefGoogle Scholar
Hagrass HA, Sharaf S, Pasha HF, Tantawy EA, Mohamed RH, Kassem R. Circulating microRNAs – a new horizon in molecular diagnosis of breast cancer. Genes Cancer 2015; 6: 281–7. Google Scholar
Mishra S, Srivastava AK, Suman S, Kumar V, Shukla Y. Circulating miRNAs revealed as surrogate molecular signatures for the early detection of breast cancer. Cancer Lett 2015; 369: 67–75. Google Scholar
Zhang L, Xu Y, Jin X, Wang Z, Wu Y, Zhao D, Chen G, Li D, Wang X, Cao H, Xie Y, Liang Z. A circulating miRNA signature as a diagnostic biomarker for non-invasive early detection of breast cancer. Breast Cancer Res Treat 2015; 154: 423–34. CrossrefGoogle Scholar
Kleivi Sahlberg K, Bottai G, Naume B, Burwinkel B, Calin GA, Borresen-Dale AL, Santarpia L. A serum microRNA signature predicts tumor relapse and survival in triple-negative breast cancer patients. Clin Cancer Res 2015; 21: 1207–14. CrossrefGoogle Scholar
Huo D, Clayton WM, Yoshimatsu TF, Chen J, Olopade OI. Identification of a circulating microRNA signature to distinguish recurrence in breast cancer patients. Oncotarget 2016; 7: 55231–48. CrossrefGoogle Scholar
Yu W, Wang Z, Shen LI, Wei Q. Circulating microRNA-21 as a potential diagnostic marker for colorectal cancer: a meta-analysis. Mol Clin Oncol 2016; 4: 237–44. Google Scholar
Chen H, Liu H, Zou H, Chen R, Dou Y, Sheng S, Dai S, Ai J, Melson J, Kittles RA, Pirooznia M, Liptay MJ, Borgia JA, Deng Y. Evaluation of plasma miR-21 and miR-152 as diagnostic biomarkers for common types of human cancers. J Cancer 2016; 7: 490–9. CrossrefGoogle Scholar
Montagnana M, Benati M, Danese E, Minicozzi AM, Paviati E, Gusella M, Pasini F, Bovo C, Guidi GC, Lippi G. Plasma expression levels of circulating miR-21 are not useful for diagnosing and monitoring colorectal cancer. Clin Lab 2016; 62: 967–70. CrossrefGoogle Scholar
Liu XX, Wang M, Xu D, Yang JH, Kang HF, Wang XJ, Lin S, Yang PT, Liu XH, Dai ZJ. Quantitative assessment of the association between genetic variants in microRNAs and colorectal cancer risk. Biomed Res Int 2015; 2015: 276410. Google Scholar
Carter JV, Roberts HL, Pan J, Rice JD, Burton JF, Galbraith NJ, Eichenberger MR, Jorden J, Deveaux P, Farmer R, Williford A, Kanaan Z, Rai SN, Galandiuk S. A highly predictive model for diagnosis of colorectal neoplasms using plasma microRNA: improving specificity and sensitivity. Ann Surg 2016; 264: 575–84. Google Scholar
Vychytilova-Faltejskova P, Radova L, Sachlova M, Kosarova Z, Slaba K, Fabian P, Grolich T, Prochazka V, Kala Z, Svoboda M, Kiss I, Vyzula R, Slaby O. Serum-based microRNA signatures in early diagnosis and prognosis prediction of colon cancer. Carcinogenesis 2016; 37: 941–50. CrossrefGoogle Scholar
Wang W, Qu A, Liu W, Liu Y, Zheng G, Du L, Zhang X, Yang Y, Wang C, Chen X. Circulating miR-210 as a diagnostic and prognostic biomarker for colorectal cancer. Eur J Cancer Care (Engl) 2016. Google Scholar
Fang Z, Tang J, Bai Y, Lin H, You H, Jin H, Lin L, You P, Li J, Dai Z, Liang X, Su Y, Hu Q, Wang F, Zhang ZY. Plasma levels of microRNA-24, microRNA-320a, and microRNA-423-5p are potential biomarkers for colorectal carcinoma. J Exp Clin Cancer Res 2015; 34: 86. CrossrefGoogle Scholar
Chang PY, Chen CC, Chang YS, Tsai WS, You JF, Lin GP, Chen TW, Chen JS, Chan EC. MicroRNA-223 and microRNA-92a in stool and plasma samples act as complementary biomarkers to increase colorectal cancer detection. Oncotarget 2016; 7: 10663–75. CrossrefGoogle Scholar
Yamada A, Horimatsu T, Okugawa Y, Nishida N, Honjo H, Ida H, Kou T, Kusaka T, Sasaki Y, Yagi M, Higurashi T, Yukawa N, Amanuma Y, Kikuchi O, Muto M, Ueno Y, Nakajima A, Chiba T, Boland CR, Goel A. Serum miR-21, miR-29a, and miR-125b are promising biomarkers for the early detection of colorectal neoplasia. Clin Cancer Res 2015; 21: 4234–42. Google Scholar
Kijima T, Hazama S, Tsunedomi R, Tanaka H, Takenouchi H, Kanekiyo S, Inoue Y, Nakashima M, Iida M, Sakamoto K, Suzuki N, Takeda S, Ueno T, Yamamoto S, Yoshino S, Okuno K, Nagano H. MicroRNA-6826 and -6875 in plasma are valuable noninvasive biomarkers that predict the efficacy of vaccine treatment against metastatic colorectal cancer. Oncol Rep 2017; 37: 23–30. Google Scholar
Kou CH, Zhou T, Han XL, Zhuang HJ, Qian HX. Downregulation of mir-23b in plasma is associated with poor prognosis in patients with colorectal cancer. Oncol Lett 2016; 12: 4838–44. Google Scholar
Imaoka H, Toiyama Y, Fujikawa H, Hiro J, Saigusa S, Tanaka K, Inoue Y, Mohri Y, Mori T, Kato T, Toden S, Goel A, Kusunoki M. Circulating microRNA-1290 as a novel diagnostic and prognostic biomarker in human colorectal cancer. Ann Oncol 2016; 27: 1879–86. CrossrefGoogle Scholar
Balch CM, Gershenwald JE, Soong SJ, Thompson JF, Atkins MB, Byrd DR, Buzaid AC, Cochran AJ, Coit DG, Ding S, Eggermont AM, Flaherty KT, Gimotty PA, Kirkwood JM, McMasters KM, Mihm MC Jr, Morton DL, Ross MI, Sober AJ, Sondak VK. Final version of 2009 AJCC melanoma staging and classification. J Clin Oncol 2009; 27: 6199–206. CrossrefGoogle Scholar
Mirzaei H, Gholamin S, Shahidsales S, Sahebkar A, Jaafari MR, Mirzaei HR, Hassanian SM, Avan A. MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma. Eur J Cancer 2016; 53: 25–32. CrossrefGoogle Scholar
Kanemaru H, Fukushima S, Yamashita J, Honda N, Oyama R, Kakimoto A, Masuguchi S, Ishihara T, Inoue Y, Jinnin M, Ihn H. The circulating microRNA-221 level in patients with malignant melanoma as a new tumor marker. J Dermatol Sci 2011; 61: 187–93. CrossrefGoogle Scholar
Saldanha G, Potter L, Shendge P, Osborne J, Nicholson S, Yii N, Varma S, Aslam MI, Elshaw S, Papadogeorgakis E, Pringle JH. Plasma microRNA-21 is associated with tumor burden in cutaneous melanoma. J Invest Dermatol 2013; 133: 1381–4. CrossrefGoogle Scholar
Fleming NH, Zhong J, da Silva IP, Vega-Saenz de ME, Brady B, Han SW, Hanniford D, Wang J, Shapiro RL, Hernando E, Osman I. Serum-based miRNAs in the prediction and detection of recurrence in melanoma patients. Cancer 2015; 121: 51–9. CrossrefGoogle Scholar
Stark MS, Klein K, Weide B, Haydu LE, Pflugfelder A, Tang YH, Palmer JM, Whiteman DC, Scolyer RA, Mann GJ, Thompson JF, Long GV, Barbour AP, Soyer HP, Garbe C, Herington A, Pollock PM, Hayward NK. The prognostic and predictive value of melanoma-related microRNAs using tissue and serum: a microRNA expression analysis. EBioMedicine 2015; 2: 671–80. CrossrefGoogle Scholar
Armand-Labit V, Meyer N, Casanova A, Bonnabau H, Platzer V, Tournier E, Sansas B, Verdun S, Thouvenot B, Hilselberger B, Doncescu A, Lamant L, Lacroix-Triki M, Favre G, Pradines A. Identification of a circulating microRNA profile as a biomarker of metastatic cutaneous melanoma. Acta Derm Venereol 2016; 96: 29–34. CrossrefGoogle Scholar
Ono S, Oyama T, Lam S, Chong K, Foshag LJ, Hoon DS. A direct plasma assay of circulating microRNA-210 of hypoxia can identify early systemic metastasis recurrence in melanoma patients. Oncotarget 2015; 6: 7053–64. CrossrefGoogle Scholar
Margue C, Reinsbach S, Philippidou D, Beaume N, Walters C, Schneider JG, Nashan D, Behrmann I, Kreis S. Comparison of a healthy miRNome with melanoma patient miRNomes: are microRNAs suitable serum biomarkers for cancer? Oncotarget 2015; 6: 12110–27. CrossrefGoogle Scholar
Tian R, Liu T, Qiao L, Gao M, Li J. Decreased serum microRNA-206 level predicts unfavorable prognosis in patients with melanoma. Int J Clin Exp Pathol 2015; 8: 3097–103. Google Scholar
Guo S, Guo W, Li S, Dai W, Zhang N, Zhao T, Wang H, Ma J, Yi X, Ge R, Wang G, Gao T, Li C. Serum miR-16: a potential biomarker for predicting melanoma prognosis. J Invest Dermatol 2016; 136: 985–93. CrossrefGoogle Scholar
Zheng H, Zhang L, Zhao Y, Yang D, Song F, Wen Y, Hao Q, Hu Z, Zhang W, Chen K. Plasma miRNAs as diagnostic and prognostic biomarkers for ovarian cancer. PLoS One 2013; 8: e77853. CrossrefGoogle Scholar
Langhe R, Norris L, Saadeh FA, Blackshields G, Varley R, Harrison A, Gleeson N, Spillane C, Martin C, O’Donnell DM, D’Arcy T, O’Leary J, O’Toole S. A novel serum microRNA panel to discriminate benign from malignant ovarian disease. Cancer Lett 2015; 356: 628–36. CrossrefGoogle Scholar
Zuberi M, Mir R, Das J, Ahmad I, Javid J, Yadav P, Masroor M, Ahmad S, Ray PC, Saxena A. Expression of serum miR-200a, miR-200b, and miR-200c as candidate biomarkers in epithelial ovarian cancer and their association with clinicopathological features. Clin Transl Oncol 2015; 17: 779–87. Google Scholar
Kapetanakis NI, Uzan C, Jimenez-Pailhes AS, Gouy S, Bentivegna E, Morice P, Caron O, Gourzones-Dmitriev C, Le TG, Busson P. Plasma miR-200b in ovarian carcinoma patients: distinct pattern of pre/post-treatment variation compared to CA-125 and potential for prediction of progression-free survival. Oncotarget 2015; 6: 36815–24. Google Scholar
Meng X, Muller V, Milde-Langosch K, Trillsch F, Pantel K, Schwarzenbach H. Diagnostic and prognostic relevance of circulating exosomal miR-373, miR-200a, miR-200b and miR-200c in patients with epithelial ovarian cancer. Oncotarget 2016; 7: 16923–35. CrossrefGoogle Scholar
Todeschini P, Salviato E, Paracchini L, Ferracin M, Petrillo M, Zanotti L, Tognon G, Gambino A, Calura E, Caratti G, Martini P, Beltrame L, Maragoni L, Gallo D, Odicino FE, Sartori E, Scambia G, Negrini M, Ravaggi A, D’Incalci M, Marchini S, Bignotti E, Romualdi C. Circulating miRNA landscape identifies miR-1246 as promising diagnostic biomarker in high-grade serous ovarian carcinoma: a validation across two independent cohorts. Cancer Lett 2016; 388: 320–7. Google Scholar
Zhu T, Gao W, Chen X, Zhang Y, Wu M, Zhang P, Wang S. A pilot study of circulating microRNA-125b as a diagnostic and prognostic biomarker for epithelial ovarian cancer. Int J Gynecol Cancer 2017; 27: 3–10. CrossrefGoogle Scholar
Shukla KK, Misra S, Pareek P, Mishra V, Singhal B, Sharma P. Recent scenario of microRNA as diagnostic and prognostic biomarkers of prostate cancer. Urol Oncol 2017; 35: 92–101. CrossrefGoogle Scholar
Yin C, Fang C, Weng H, Yuan C, Wang F. Circulating microRNAs as novel biomarkers in the diagnosis of prostate cancer: a systematic review and meta-analysis. Int Urol Nephrol 2016; 48: 1087–95. CrossrefGoogle Scholar
Mihelich BL, Maranville JC, Nolley R, Peehl DM, Nonn L. Elevated serum microRNA levels associate with absence of high-grade prostate cancer in a retrospective cohort. PLoS One 2015; 10: e0124245. CrossrefGoogle Scholar
Kachakova D, Mitkova A, Popov E, Popov I, Vlahova A, Dikov T, Christova S, Mitev V, Slavov C, Kaneva R. Combinations of serum prostate-specific antigen and plasma expression levels of let-7c, miR-30c, miR-141, and miR-375 as potential better diagnostic biomarkers for prostate cancer. DNA Cell Biol 2015; 34: 189–200. Google Scholar
Li Z, Ma YY, Wang J, Zeng XF, Li R, Kang W, Hao XK. Exosomal microRNA-141 is upregulated in the serum of prostate cancer patients. Onco Targets Ther 2016; 9: 139–48. Google Scholar
Wang J, Ye H, Zhang D, Hu Y, Yu X, Wang L, Zuo C, Yu Y, Xu G, Liu S. MicroRNA-410-5p as a potential serum biomarker for the diagnosis of prostate cancer. Cancer Cell Int 2016; 16: 12. CrossrefGoogle Scholar
Sharova E, Grassi A, Marcer A, Ruggero K, Pinto F, Bassi P, Zanovello P, Zattoni F, D’Agostino DM, Iafrate M, Ciminale V. A circulating miRNA assay as a first-line test for prostate cancer screening. Br J Cancer 2016; 114: 1362–6. CrossrefGoogle Scholar
Cochetti G, Poli G, Guelfi G, Boni A, Egidi MG, Mearini E. Different levels of serum microRNAs in prostate cancer and benign prostatic hyperplasia: evaluation of potential diagnostic and prognostic role. Onco Targets Ther 2016; 9: 7545–53. CrossrefGoogle Scholar
Osip’yants AI, Knyazev EN, Galatenko AV, Nyushko KM, Galatenko VV, Shkurnikov MY, Alekseev BY. Changes in the level of circulating hsa-miR-297 and hsa-miR-19b-3p miRNA are associated with generalization of prostate cancer. Bull Exp Biol Med 2017; 162: 379–82. Google Scholar
Jarry J, Schadendorf D, Greenwood C, Spatz A, van Kempen LC. The validity of circulating microRNAs in oncology: five years of challenges and contradictions. Mol Oncol 2014; 8: 819–29. CrossrefGoogle Scholar
Lee I, Baxter D, Lee MY, Scherler K, Wang K. The importance of standardization on analyzing circulating RNA. Mol Diagn Ther 2016. Google Scholar
Hrustincova A, Votavova H, Dostalova Merkerova M. Circulating microRNAs: methodological aspects in detection of these biomarkers. Folia Biol (Praha) 2015; 61: 203–18. Google Scholar
Glinge C, Clauss S, Boddum K, Jabbari R, Jabbari J, Risgaard B, Tomsits P, Hildebrand B, Kaab S, Wakili R, Jespersen T, Tfelt-Hansen J. Stability of circulating blood-based microRNAs – pre-analytic methodological considerations. PLoS One 2017; 12: e0167969. CrossrefGoogle Scholar
Khan J, Lieberman JA, Lockwood CM. Variability in, variability out: best practice recommendations to standardize pre-analytical variables in the detection of circulating and tissue microRNAs. Clin Chem Lab Med 2017; 55: 608–21. CrossrefGoogle Scholar
Hrustincova A, Votavova H, Dostalova MM. Circulating microRNAs: methodological aspects in detection of these biomarkers. Folia Biol (Praha) 2015; 61: 203–18. Google Scholar
Marabita F, de CP, Torri A, Tegner J, Abrignani S, Rossi RL. Normalization of circulating microRNA expression data obtained by quantitative real-time RT-PCR. Brief Bioinform 2016; 17: 204–12. CrossrefGoogle Scholar
Gee HE, Buffa FM, Camps C, Ramachandran A, Leek R, Taylor M, Patil M, Sheldon H, Betts G, Homer J, West C, Ragoussis J, Harris AL. The small-nucleolar RNAs commonly used for microRNA normalisation correlate with tumour pathology and prognosis. Br J Cancer 2011; 104: 1168–77. CrossrefGoogle Scholar
Xiang M, Zeng Y, Yang R, Xu H, Chen Z, Zhong J, Xie H, Xu Y, Zeng X. U6 is not a suitable endogenous control for the quantification of circulating microRNAs. Biochem Biophys Res Commun 2014; 454: 210–4. CrossrefGoogle Scholar
Chen X, Liang H, Guan D, Wang C, Hu X, Cui L, Chen S, Zhang C, Zhang J, Zen K, Zhang CY. A combination of Let-7d, Let-7g and Let-7i serves as a stable reference for normalization of serum microRNAs. PLoS One 2013; 8: e79652. CrossrefGoogle Scholar
Ratert N, Meyer HA, Jung M, Mollenkopf HJ, Wagner I, Miller K, Kilic E, Erbersdobler A, Weikert S, Jung K. Reference miRNAs for miRNAome analysis of urothelial carcinomas. PLoS One 2012; 7: e39309. CrossrefGoogle Scholar
Torres A, Torres K, Wdowiak P, Paszkowski T, Maciejewski R. Selection and validation of endogenous controls for microRNA expression studies in endometrioid endometrial cancer tissues. Gynecol Oncol 2013; 130: 588–94. CrossrefGoogle Scholar
About the article
Published Online: 2017-04-27
Published in Print: 2017-05-24