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International Journal of Molecular Sciences logoLink to International Journal of Molecular Sciences
. 2023 Oct 12;24(20):15114. doi: 10.3390/ijms242015114

Identifying MicroRNAs Suitable for Detection of Breast Cancer: A Systematic Review of Discovery Phases Studies on MicroRNA Expression Profiles

Lisa Padroni 1, Laura De Marco 1, Valentina Fiano 2, Lorenzo Milani 3, Giorgia Marmiroli 1, Maria Teresa Giraudo 3, Alessandra Macciotta 3, Fulvio Ricceri 3,†, Carlotta Sacerdote 1,*,†
Editor: Nuno Vale
PMCID: PMC10607026  PMID: 37894794

Abstract

The analysis of circulating tumor cells and tumor-derived materials, such as circulating tumor DNA, circulating miRNAs (cfmiRNAs), and extracellular vehicles provides crucial information in cancer research. CfmiRNAs, a group of short noncoding regulatory RNAs, have gained attention as diagnostic and prognostic biomarkers. This review focuses on the discovery phases of cfmiRNA studies in breast cancer patients, aiming to identify altered cfmiRNA levels compared to healthy controls. A systematic literature search was conducted, resulting in 16 eligible publications. The studies included a total of 585 breast cancer cases and 496 healthy controls, with diverse sample types and different cfmiRNA assay panels. Several cfmiRNAs, including MIR16, MIR191, MIR484, MIR106a, and MIR193b, showed differential expressions between breast cancer cases and healthy controls. However, the studies had a high risk of bias and lacked standardized protocols. The findings highlight the need for robust study designs, standardized procedures, and larger sample sizes in discovery phase studies. Furthermore, the identified cfmiRNAs can serve as potential candidates for further validation studies in different populations. Improving the design and implementation of cfmiRNA research in liquid biopsies may enhance their clinical diagnostic utility in breast cancer patients.

Keywords: breast cancer, microRNA, miRNA, serum, plasma, high throughput techniques

1. Introduction

MicroRNAs (miRNAs) constitute a class of small RNA molecules that are naturally present and have been conserved over evolutionary history [1]. These single-stranded RNA molecules do not participate in the encoding of proteins and typically consist of 19 to 25 nucleotides [1]. A collection of around 2650 distinct mature microRNA sequences is documented in miRNA libraries [1]. Functionally, miRNAs play a critical role as post-transcriptional regulators, influencing gene expression across various tissues and developmental stages. They accomplish this by engaging in precise interactions within intricate regulatory networks [2].

Due to their limited binding region between miRNA and mRNA, a single miRNA has the capacity to target multiple specific mRNAs, thereby exerting influence across diverse pathways [2]. Given their diverse functions, miRNAs possess the ability to regulate various pathways associated with cellular activities and intercellular communication. These pathways encompass processes such as cellular growth, specialization, replication, and programmed cell death [3].

Approximately half of the genetic codes responsible for miRNAs in humans are situated within regions of the genome that are linked to cancer or at chromosomal sites prone to fragility and instability [4].

In breast cancer, as in numerous other cancer types, the onset of abnormal cell behavior leads to uncontrolled proliferation. This proliferation is driven by genetic modifications that influence cellular growth regulatory mechanisms. The miRNAs associated with this disease can be classified into two categories: oncogenic miRNAs (known as oncomiRs) and tumor suppressor miRNAs (referred to as tsmiRs) [1]. OncomiRs are generally upregulated in breast cancer and function by suppressing the expression of potential tumor-suppressing genes [5]. Conversely, tsmiRs hinder the expression of oncogenes that contribute to the formation of breast tumors [5]. Consequently, decreased expression of tsmiRs can lead to the initiation of breast malignancy [5]. Figure 1 offers an overview of the specific regulatory roles of miRNAs in breast cancer.

Figure 1.

Figure 1

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Overview of regulatory role of oncogenic and tumor suppressor miRNAs in breast cancer.

These regulatory networks encompass several fundamental aspects of cancer biology, including the maintenance of growth signals that promote proliferation, the achievement of replicative immortality, the initiation of invasion and metastasis, the resistance to programmed cell death and apoptotic responses, the stimulation of new blood vessel formation (angiogenesis), the activation of cellular metabolism and energy processes, and the facilitation of immune evasion by cancer cells [5]

Liquid biopsy provides important information on the analysis of circulating tumor cells and circulating tumor-derived materials, such as circulating tumor DNA, circulating miRNAs (cfmiRNAs), and extracellular vesicles [6].

In particular, cfmiRNAs have been extensively investigated as diagnostic biomarkers, other than as biomarkers for prognosis and therapy response. CfmiRNAs constitute a group of short, noncoding regulatory RNAs that modulate gene expression at the post transcriptional level [7]. Cell-free circulating microRNAs likely released from cells in lipid vesicles, microvesicles, or exosomes have been detected in peripheral blood circulation [8].

Usually, the study design of research works on biomarkers consists of a first phase generally regarded as a discovery phase, followed by a validation phase [9].

The discovery phase typically involves exploration carried out with high-throughput laboratory techniques to select a pool of candidates [10]. The objective is to identify a short list of promising cfmiRNAs associated with disease for further investigation. The discovery research poses considerable challenges, due to the large number of biomarkers being investigated, the typical weakness of signals from individual markers, and the frequent presence of strong noise due to experimental effects [10]. The validation study is a key step for translating laboratory findings into clinical practice; furthermore, this is heavily conditioned by the short list of biomarkers selected in the discovery phase [10].

While evolving molecular technologies in discovery studies have generated plenty of omics data, identification success has been very limited considering the reduced number of cfmiRNAs that have reached clinical use [11].

One of the reasons behind this phenomenon is the lack of adequate study designs in the discovery phase research [12]. Furthermore, several studies analyze candidate cfmiRNAs selected from a search on previous literature, thereby amplifying the problems that may have arisen due to a suboptimal discovery phase.

The search for cfmiRNAs to use as diagnostic biomarkers in breast cancer is very active. Several reviews and meta-analyses have been published on the predictive role of cfmiRNAs in breast cancer diagnosis [13,14,15,16,17]. Nevertheless, all of them were based on validation phases of the study or on studies on candidate cfmiRNAs.

This review aims to identify the altered levels of circulating microRNA in breast cancer patients compared to healthy controls, including only the discovery phases of the study. This can be of great usefulness for the progression of this research field, allowing the selection of candidate cfmiRNAs to be investigated in new case–control studies.

2. Materials and Methods

We have registered the protocol of this review in the international database of prospective registered systematic reviews (PROSPERO 2022; CRD42023399977). The workflow and methodology were based on the Preferred Reporting Items for Systematic Review and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA) guidelines [18].

2.1. Publication Search

We capitalized on a previous literature review conducted by our group, in which we conducted searches on PubMed, Cochrane Library, EMBASE, Google Scholar, and NCBI PubMed Central to select appropriate studies [13] (the previous review was updated to 31 December 2022).

The search was performed using the following keywords as a search strategy: ((Circulating) AND (microRNA OR miRNA) AND (breast AND Cancer)) NOT (cells) NOT (tissue) AND ((English [Filter]) AND (Humans [Filter]) AND (“31 December 2022” [Date—Release])). Additionally, other studies were identified through the references in previously selected publications.

2.2. Inclusion and Exclusion Criteria

In the systematic review, we considered all studies that fulfilled the following requirements: (1) inclusion of both patients with BC and healthy controls; (2) measurement of cfmiRNA levels in serum, plasma, or blood; and (3) presence of a discovery phase that used high throughput techniques, including studies with an agnostic genome-wide design.

Studies were excluded if they were candidate cfmiRNA studies, reviews, meta-analyses, letters, commentaries, or conference abstracts or if they were duplications of previous publications or written in languages other than English.

2.3. Data Extraction

Adhering to the inclusion criteria, the primary authors (L.P. and C.S.) independently gathered the relevant data. In the event of any disagreements, consensus was reached through discussion. The extracted data included first author’s name and reference, country, sample size, biological sample type (plasma, serum, or blood), cfmiRNAs, AUC value (95% CI), fold change (95% CI), and expression (upregulation or downregulation).

2.4. Quality Assessment

All studies included in the review underwent independent evaluation for quality by two reviewers, L.P. and C.S. They utilized the revised Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2) [19] to assess potential biases in four critical domains: patient selection, index test, reference standard, and flow and timing. The agreement percentage between the two reviewers was calculated for each variable in QUADAS-2. Any discrepancies in coding or QUADAS-2 assessments were resolved through consensus discussions.

2.5. Statistical Analysis

We used STATA17.0 software to perform the statistical analyses. Pyramid plots were chosen to illustrate descriptive statistics on the directions of microRNA expression; sample subgroups were created to compare cfmiRNA expressions in different biological samples (serum and plasma).

3. Results

We took advantage of a previous literature review performed by our group, where from a total of 308 initially identified records, we excluded 206 records for several reasons (duplicates, secondary literature, being off topic, etc.) (see [13] for details). In total, 102 papers were considered in the screening stage for a manual review of titles and abstracts; 3 papers were excluded because the abstract was not available in English. After carefully examining the abstracts and, when useful, the full texts, an additional group of 83 publications were excluded as they did not meet the inclusion criteria (i.e., the discovery phase was performed only in tissues, or the discovery technique was not of a high-throughput type). Ultimately, this review included 16 publications [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36]. Figure 2 illustrates the flowchart depicting the paper exclusion process.

Figure 2.

Figure 2

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Flow chart of identification, screening, and eligibility of the included studies (identification in [13]).

Table 1 provides a summary of the key features of these studies. This review encompassed a total of 585 breast cancer (BC) cases and 496 healthy controls. Among the included studies, only 1 out of 16 had more than 100 BC cases [24]. The studies were conducted in various countries, including China (N = 3), the USA (N = 3), Germany (N = 2), Italy (N = 1), Ireland (N = 1), Denmark (N = 1), the Czech Republic (N = 1), Australia (N = 1), Singapore (N = 1), Malaysia (N = 1), and Saudi Arabia (N = 1). Notably, most of the studies focused on a white European population (N = 6), while the remaining studies predominantly focused on Asiatic (N = 6) or mixed U.S. or Australian populations (N = 4). This supports the evidence that Black and Hispanic populations were relatively limited in the context of microRNA and breast cancer research.

Table 1.

General features of the studies included in the systematic review on the role of microRNA in breast cancer diagnosis.

First Author, Year Country Specimen Source Lab Technique Case–Control Size QUADAS-2 Domains with Risk of Bias Applied Multiple Testing Correction
Schrauder MG, 2012 [20] Germany Blood Geniom Biochip miRNA homo sapiens 48/57 Index test Benjamini–Hochberg
Wu Q, 2012 [21] China Serum Life Technologies SOLiD™ sequencing base miRNA expression profiling 13/10 Patient selection, Index test Not applied
Chan M, 2013 [22] Singapore Serum Agilent Human miRNA microarray 32/22 Patient selection, Index test, Flow and timing Benjamini–Hochberg
Cuk K, 2013 [23] Germany Plasma TLDA human MicroRNA Cards A v2.1 and B v2.0 10/10 Patient selection, Index test Benjamini–Hochberg *
Ng E K, 2013 [25] USA Plasma TLDA human MicroRNA Cards A v2.1 and B v2.0 5/5 Patient selection, Index test Not applied
Godfrey AC, 2013 [24] USA Serum Affimetrix GeneChip miRNA 2.0 array 205/205 Index test Not applied
Kodahl AR, 2014 [26] Denmark Serum Exiqon microRNA panel (miRCURY) 48/24 Index test Bonferroni *
McDermott AM, 2014 [27] Ireland Blood TLDA human MicroRNA Cards A v2.1 and B v2.0 10/10 Index test Not applied
Shen J, 2014 [28] USA Plasma Exiqon microRNA panel (miRCURY) 52/35 Index test, Flow and timing Benjamini–Hochberg
Zearo S, 2014 [29] Australia Serum TLDA human MicroRNA Cards A and B v3.0 39/10 Patient selection, Index test, Flow and timing Bonferroni
Ferracin M, 2015 [31] Italy Plasma Agilent Human miRNA microarray 18/18 Patient selection, Index test, Flow and timing Not applied
Shin VY, 2015 [32] China Plasma Exiqon microRNA panel (miRCURY) 5/5 Patient selection, Index test, Flow and timing Not applied
Zhang L, 2015 [30] China Serum Serum-direct multiplex qRT-PCR (SdM-qRT-PCR) 25/20 Patient selection, Index test, Flow and timing Bonferroni, Benjamini–Hochberg
Hamam R, 2016 [33] Saudi Arabia Blood Agilent Human miRNA microarray 23/9 Patient selection, Index test, Flow and timing Benjamini–Hochberg
Jusoh A, 2021 [34] Malaysia Plasma Qiagen miScript miRNA PCR Array 8/9 Index test, Flow and timing Not applied
Záveský L, 2022 [35] Czech Republic Plasma TLDA human MicroRNA Cards A v2.1 and B v2.0 7/7 Patient selection, Index test Benjamini–Hochberg
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* Cuk et al. [23] and Kodhal et al. [26] performed the adjustment for multiple comparisons but considered unadjusted p values for cfmiRNA selection for the validation phase.

Regarding the types of samples, some studies used serum (N = 6), while others used plasma (N = 7) or whole blood (N = 3).

The 17 studies included in the review employed different panels of microRNA assays: the TLDA human micro RNA cards (N = 5) was the most popular, followed by Exiqon microRBA panel miRCURY (N = 3) and Agilent Human microarray (N = 3).

The sixth column of Table 1 presents the QUADAS domains for which a potential risk of bias was identified in each study.

The quality assessment using the QUADAS-2 tool showed that the included studies had low applicability but a high risk of bias (Figure 3). A higher risk of bias was observed in many studies across the QUADAS-2 domains of patient selection, index testing, and flow and timing (respectively, 62.5%, 100%, and 50% of studies with a risk of bias). Patient selection involves detailing the methods of selecting patients, while index testing pertains to how the cfmiRNA analysis was conducted and interpreted, standard of reference assesses the accuracy of disease status classification, and flow and timing refer to the time interval and any interventions before cfmiRNA analysis. Indeed, several studies lacked sufficient detail on the patient selection process, such as whether cases consisted of consecutive patients or controls originated from the same population that produced the cases. Furthermore, there was insufficient information on the timing of biological sample retrieval, such as whether it occurred at diagnosis, before or after surgery, or during chemotherapy. The breast cancer diagnosis was histologically confirmed in all the studies, indicating a low risk of bias in the reference standard domain. In the category of the index test, some studies failed to mention whether a threshold was pre-specified.

Figure 3.

Figure 3

Figure 3

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Quality assessment with the QUADAS-2 tool [18].

Furthermore, the authors of 9 out 16 studies applied a multiple testing correction in the cfmiRNA selection (mostly the Benjamini–Hochberg False Discovery Rate method), Moreover, Cuk et al. [23] and Kodhal et al. [26] also performed the adjustment for multiple comparisons, considering unadjusted p values for cfmiRNA selection in the validation phase.

The authors employed very heterogeneous criteria to select interesting cfmiRNAs for inclusion in the validation phase of their study. Godfrey et al. [24] and Shin et al. [32] focused on those demonstrating statistical significance in the discovery phase (p < 0.05). Schrauder et al. [20] selected the 25 top hits from statistically significant cfmiRNAs (p < 0.05). Chan et al. [22] chose cfmiRNAs with statistical significance (p < 0.05) excluding those with collinearity. Cuk et al. [23], Shen et al. [28], Zearo et al. [29], Zhang et al. [30], and Hamam et al. [33] used both statistical significance (all p < 0.05 except for Zearo p < 0.01) and fold change (generally FC > 2) as selection criteria. Ng et al. [25] and Jusoh et al. [34] opted for cfmiRNAs with a fold change greater than 2, while Ferracin et al. [31] selected those with the highest fold changes in plasma and serum. Wu et al. [21] focused exclusively on up-regulated cfmiRNAs (and showed them in a table) but validated only cfmiRNAs with the same pathway in serum and tissue. McDermott et al. [27] used the ANN data mining algorithm to identify cfmiRNAs with detectable and altered expression in patients. Záveský et al. [35] chose those with a Ct value exceeding 40, and finally, Kodahl et al. [26] performed automatic selection using component-wise likelihood-based boosting.

Table 2 shows the results of the studies included in this review.

Table 2.

Summary of the results of the studies included in the systematic review on the role of cfmiRNAs in breast cancer diagnosis. (For cfmiRNAs analyzed in Schrauder et al. [20], Chan et al. [22], Cuk at al. [23], Kodhal et al. [26], Shen et al. [28], Zearo et al. [29], Zhang et al. [30], Hamam et al. [33], and Záveský et al. [35], adjusted p value were reported. Only cfmiRNAs that demonstrated statistical significance in the discovery phase have been included in the table. However, for Cuk et al. [23] and Kodhal et al. [26], non-significant adjusted p-values were reported since the authors considered unadjusted statistically significant p-values during the selection for the validation phase. About Záveský et al. [35], we decided to include all the miRNAs with a Ct-cutoff < 35, and to minimize data loss, we also added all the miRNAs that had not already been included with a Ct cut-off ≤ 40).

MIR First Author, Year Specimen Source Direction AUC p-Value
1 Chan M, 2013 [22] Serum up <0.001
7 Chan M, 2013 [22] Serum up <0.001
16 Chan M, 2013 [22] Serum up <0.001
Ng E K, 2013 [25] Plasma up
Shin VY, 2015 [32] Plasma down <0.05
Zhang L, 2015 [30] Serum up 0.001
Záveský L, 2022 [35] Plasma down 0.038
17 Chan M, 2013 [22] Serum up 0.001
Záveský L, 2022 [35] Plasma down 0.017
21 Ng E K, 2013 [25] Plasma up
Ferracin M, 2015 [31] Plasma up
Shin VY, 2015 [32] Plasma down <0.05
22 Shen J, 2014 [28] Plasma up 0.85 <0.001
Jusoh A, 2021 [34] Plasma up 0.83 0.020
24 Schrauder MG, 2012 [20] Blood down 0.65 0.023
Wu Q, 2012 [21] Serum up
25 Wu Q, 2012 [21] Serum up
Chan M, 2013 [22] Serum up <0.001
Ng E K, 2013 [25] Plasma up
28 Chan M, 2013 [22] Serum down 0.005
Shen J, 2014 [28] Plasma up 0.85 <0.001
93 Chan M, 2013 [22] Serum up <0.001
95 Chan M, 2013 [22] Serum up 0.023
96 Chan M, 2013 [22] Serum up 0.008
100 Zhang L, 2015 [30] Serum up 0.79 0.003
101 Zhang L, 2015 [30] Serum up 0.024
103 Wu Q, 2012 [21] Serum up
107 Schrauder MG, 2012 [20] Blood down 0.68 0.041
Chan M, 2013 [22] Serum up 0.013
Kodahl AR, 2014 [26] Serum up 0.006
Shen J, 2014 [28] Plasma up 0.87 <0.001
126 Ng E K, 2013 [25] Plasma down
Shen J, 2014 [28] Plasma up 0.77 <0.001
Zearo S, 2014 [29] Serum up <0.001
127 Cuk K, 2013 [23] Plasma up 0.459
Shen J, 2014 [28] Plasma up 0.75 <0.001
128 Chan M, 2013 [22] Serum up 0.010
Zhang L, 2015 [30] Serum up 0.039
134 Chan M, 2013 [22] Serum up 0.044
Hamam R, 2016 [33] Blood up 0.042
136 Shen J, 2014 [28] Plasma up 0.87 <0.001
139 Cuk K, 2013 [23] Plasma down 0.320
Kodahl AR, 2014 [26] Serum down 0.623
Shen J, 2014 [28] Plasma up 0.79 <0.001
140 Zearo S, 2014 [29] Serum up <0.001
141 Zhang L, 2015 [30] Serum up 0.89 0.027
142 Chan M, 2013 [22] Serum down 0.001
Shen J, 2014 [28] Plasma up 0.82 <0.001
143 Chan M, 2013 [22] Serum up <0.001
Kodahl AR, 2014 [26] Serum down 0.073
Shin VY, 2015 [32] Plasma down <0.05
144 Chan M, 2013 [22] Serum up <0.001
Shen J, 2014 [28] Plasma down 0.94 <0.001
145 Chan M, 2013 [22] Serum up 0.036
Ng E K, 2013 [25] Plasma down
Kodahl AR, 2014 [26] Serum down <0.001
Jusoh A, 2021 [34] Plasma up 0.82 0.040
149 Godfrey AC, 2013 [24] Serum up 0.030
150 Ng E K, 2013 [25] Plasma up
Hamam R, 2016 [33] Blood up 0.033
151 Godfrey AC, 2013 [24] Serum up 0.030
Shen J, 2014 [28] Plasma up 0.88 <0.001
152 Shen J, 2014 [28] Plasma up 0.75 0.002
154 Ng E K, 2013 [25] Plasma up
155 Zearo S, 2014 [29] Serum up 0.008
Zhang L, 2015 [30] Serum up 0.017
182 Schrauder MG, 2012 [20] Blood down 0.71 0.008
Chan M, 2013 [22] Serum up 0.009
183 Zhang L, 2015 [30] Serum up 0.79 0.003
184 Cuk K, 2013 [23] Plasma up 0.332
185 Chan M, 2013 [22] Serum up <0.001
Shin VY, 2015 [32] Plasma down <0.05
186 Ng E K, 2013 [25] Plasma up
Zearo S, 2014 [29] Serum up <0.001
188 Hamam R, 2016 [33] Blood up 0.004
190 Cuk K, 2013 [23] Plasma up 0.459
191 Ng E K, 2013 [25] Plasma up
Zearo S, 2014 [29] Serum up <0.001
Zhang L, 2015 [30] Serum up 0.018
192 Wu Q, 2012 [21] Serum up
194 Wu Q, 2012 [21] Serum up
Shen J, 2014 [28] Plasma down 0.81 0.002
195 Chan M, 2013 [22] Serum up 0.007
202 Schrauder MG, 2012 [20] Blood up 0.72 0.020
Zhang L, 2015 [30] Serum down 0.005
205 Chan M, 2013 [22] Serum up 0.011
206 Cuk K, 2013 [23] Plasma down 0.320
210 Chan M, 2013 [22] Serum up 0.044
Ng E K, 2013 [25] Plasma up
214 Chan M, 2013 [22] Serum up <0.001
Záveský L, 2022 [35] Plasma up 0.017
221 Shen J, 2014 [28] Plasma up 0.84 <0.001
Shin VY, 2015 [32] Plasma down <0.05
222 Wu Q, 2012 [21] Serum up
Godfrey AC, 2013 [24] Serum up 0.020
Zearo S, 2014 [29] Serum up <0.001
223 Wu Q, 2012 [21] Serum up
Chan M, 2013 [22] Serum down <0.001
296 Chan M, 2013 [22] Serum up <0.001
320 Ng E K, 2013 [25] Plasma down
Zearo S, 2014 [29] Serum up <0.001
324 Ng E K, 2013 [25] Plasma down
Zhang L, 2015 [30] Serum up 0.88 <0.001
326 Shen J, 2014 [28] Plasma up 0.88 <0.001
328 Ng E K, 2013 [25] Plasma up
Shen J, 2014 [28] Plasma up 0.80 <0.001
330 Záveský L, 2022 [35] Plasma up 0.017
331 Shen J, 2014 [28] Plasma up 0.71 0.006
335 Schrauder MG, 2012 [20] Blood up 0.74 0.040
Chan M, 2013 [22] Serum up 0.009
Shen J, 2014 [28] Plasma up 0.73 0.006
338 Chan M, 2013 [22] Serum down <0.001
339 Chan M, 2013 [22] Serum down 0.021
Shen J, 2014 [28] Plasma up 0.76 <0.001
342 Zearo S, 2014 [29] Serum up <0.001
Shin VY, 2015 [32] Plasma up <0.05
363 Chan M, 2013 [22] Serum up 0.003
Godfrey AC, 2013 [24] Serum up 0.030
Záveský L, 2022 [35] Plasma down 0.011
365 Kodahl AR, 2014 [26] Serum down 0.006
374 Záveský L, 2022 [35] Plasma down 0.022
375 Shen J, 2014 [28] Plasma down 0.74 0.003
378 Chan M, 2013 [22] Serum up 0.013
382 Shen J, 2014 [28] Plasma up 0.72 <0.001
409 Cuk K, 2013 [23] Plasma up 0.332
Shen J, 2014 [28] Plasma up 0.78 <0.001
421 Chan M, 2013 [22] Serum up 0.009
423 Chan M, 2013 [22] Serum up <0.001
Shen J, 2014 [28] Plasma up 0.82 <0.001
424 Cuk K, 2013 [23] Plasma up 0.322
Zhang L, 2015 [30] Serum up 0.86 0.002
Hamam R, 2016 [33] Blood up 0.044
425 Chan M, 2013 [22] Serum up 0.020
Kodahl AR, 2014 [26] Serum up 0.119
Zearo S, 2014 [29] Serum up <0.001
Ferracin M, 2015 [31] Plasma up
429 Wu Q, 2012 [21] Serum up
451 Chan M, 2013 [22] Serum up 0.002
Ng E K, 2013 [25] Plasma up
454 Zearo S, 2014 [29] Serum up <0.001
483 Zearo S, 2014 [29] Serum up 0.016
Hamam R, 2016 [33] Blood up 0.038
Záveský L, 2022 [35] Plasma up 0.004
484 Chan M, 2013 [22] Serum up 0.008
Shen J, 2014 [28] Plasma up 0.84 <0.001
Zearo S, 2014 [29] Serum up <0.001
485 Ng E K, 2013 [25] Plasma up
Shen J, 2014 [28] Plasma up 0.87 <0.001
486 Chan M, 2013 [22] Serum up <0.001
Ng E K, 2013 [25] Plasma up
Zearo S, 2014 [29] Serum up <0.001
494 Ng E K, 2013 [25] Plasma down
495 Shen J, 2014 [28] Plasma up 0.85 <0.001
497 Schrauder MG, 2012 [20] Blood up 0.75 0.010
501 Chan M, 2013 [22] Serum up 0.023
543 Shen J, 2014 [28] Plasma up 0.87 <0.001
564 Schrauder MG, 2012 [20] Blood down 0.67 0.012
571 Cuk K, 2013 [23] Plasma down 0.100
574 Chan M, 2013 [22] Serum up 0.027
Ng E K, 2013 [25] Plasma up
Zearo S, 2014 [29] Serum up <0.001
576 Chan M, 2013 [22] Serum up <0.001
584 Chan M, 2013 [22] Serum up 0.005
598 Chan M, 2013 [22] Serum up 0.020
605 Godfrey AC, 2013 [24] Serum down 0.050
624 Chan M, 2013 [22] Serum up 0.027
625 Schrauder MG, 2012 [20] Blood down 0.77 0.002
627 Chan M, 2013 [22] Serum up 0.030
629 Chan M, 2013 [22] Serum up 0.009
Godfrey AC, 2013 [24] Serum up 0.050
652 Godfrey AC, 2013 [24] Serum up 0.030
660 Chan M, 2013 [22] Serum up 0.004
664 Chan M, 2013 [22] Serum down 0.050
671 Godfrey AC, 2013 [24] Serum up 0.010
Záveský L, 2022 [35] Plasma down 0.029
718 Schrauder MG, 2012 [20] Blood down 0.77 0.004
744 Godfrey AC, 2013 [24] Serum up 0.020
760 Godfrey AC, 2013 [24] Serum down 0.020
762 Hamam R, 2016 [33] Blood up 0.042
766 Chan M, 2013 [22] Serum down 0.011
Shen J, 2014 [28] Plasma up 0.86 <0.001
Ferracin M, 2015 [31] Plasma down
801 Cuk K, 2013 [23] Plasma up 0.320
874 Schrauder MG, 2012 [20] Blood down 0.74 0.001
Ng E K, 2013 [25] Plasma down
877 Chan M, 2013 [22] Serum up 0.043
922 Schrauder MG, 2012 [20] Blood up 0.65 0.030
1202 Hamam R, 2016 [33] Blood up 0.006
1207 Hamam R, 2016 [33] Blood up 0.020
1225 Hamam R, 2016 [33] Blood up 0.004
1234 Godfrey AC, 2013 [24] Serum down 0.030
1290 Hamam R, 2016 [33] Blood up 0.022
1323 Schrauder MG, 2012 [20] Blood up 0.69 0.040
1469 Schrauder MG, 2012 [20] Blood down 0.68 0.008
1471 Schrauder MG, 2012 [20] Blood down 0.70 0.012
1827 Godfrey AC, 2013 [24] Serum up 0.010
1914 Hamam R, 2016 [33] Blood up 0.044
1915 Schrauder MG, 2012 [20] Blood down 0.75 0.002
1974 Shen J, 2014 [28] Plasma up 0.85 <0.001
2355 Schrauder MG, 2012 [20] Blood down 0.73 0.004
3130 Schrauder MG, 2012 [20] Blood down 0.73 0.004
3136 Godfrey AC, 2013 [24] Serum up 0.050
3141 Hamam R, 2016 [33] Blood up 0.029
3156 Ferracin M, 2015 [31] Plasma down
3186 Schrauder MG, 2012 [20] Blood down 0.75 0.002
3652 Hamam R, 2016 [33] Blood up 0.044
4257 Schrauder MG, 2012 [20] Blood up 0.65 0.040
4270 Hamam R, 2016 [33] Blood up 0.001
4281 Hamam R, 2016 [33] Blood up 0.019
4298 Hamam R, 2016 [33] Blood up 0.035
4306 Schrauder MG, 2012 [20] Blood up 0.71 0.020
Godfrey AC, 2013 [24] Serum up 0.030
106a Chan M, 2013 [22] Serum up <0.001
Ng E K, 2013 [25] Plasma down
Zhang L, 2015 [30] Serum up 0.018
Záveský L, 2022 [35] Plasma down 0.038
106b Schrauder MG, 2012 [20] Blood up 0.72 0.010
Záveský L, 2022 [35] Plasma down 0.017
10a Wu Q, 2012 [21] Serum up
Chan M, 2013 [22] Serum up 0.029
Ng E K, 2013 [25] Plasma up
10b Chan M, 2013 [22] Serum up <0.001
1255a Godfrey AC, 2013 [24] Serum up <0.01
125a Wu Q, 2012 [21] Serum up
Ferracin M, 2015 [31] Plasma up
125b Zhang L, 2015 [30] Serum up 0.017
Záveský L, 2022 [35] Plasma down 0.014
130a Chan M, 2013 [22] Serum up 0.020
Shen J, 2014 [28] Plasma up 0.87 <0.001
130b Chan M, 2013 [22] Serum up 0.002
Godfrey AC, 2013 [24] Serum up 0.030
133a Chan M, 2013 [22] Serum up <0.001
Kodahl AR, 2014 [26] Serum down 0.479
Shen J, 2014 [28] Plasma up 0.80 <0.001
133b Chan M, 2013 [22] Serum up <0.001
135b Zhang L, 2015 [30] Serum up 0.87 <0.001
146b Zearo S, 2014 [29] Serum up <0.001
148a Ng E K, 2013 [25] Plasma up
148b Cuk K, 2013 [23] Plasma up 0.320
Shen J, 2014 [28] Plasma up 0.81 <0.001
15a Kodahl AR, 2014 [26] Serum up =1
15b Chan M, 2013 [22] Serum up 0.003
181a Wu Q, 2012 [21] Serum up
Chan M, 2013 [22] Serum down 0.023
Godfrey AC, 2013 [24] Serum up 0.050
Ferracin M, 2015 [31] Plasma down
Zhang L, 2015 [30] Serum up 0.86 <0.001
181b Wu Q, 2012 [21] Serum up
181c Chan M, 2013 [22] Serum down 0.038
18a Chan M, 2013 [22] Serum up 0.004
Godfrey AC, 2013 [24] Serum up 0.040
Kodahl AR, 2014 [26] Serum up 0.007
18b Chan M, 2013 [22] Serum up 0.002
Godfrey AC, 2013 [24] Serum down 0.040
193a Schrauder MG, 2012 [20] Blood down 0.79 <0.001
Cuk K, 2013 [23] Plasma down 0.320
Ng E K, 2013 [25] Plasma down
193b Wu Q, 2012 [21] Serum up
Ng E K, 2013 [25] Plasma up
Zhang L, 2015 [30] Serum up 0.80 0.002
Záveský L, 2022 [35] Plasma up 0.017
196b Záveský L, 2022 [35] Plasma down 0.041
199a Chan M, 2013 [22] Serum down 0.013
Shen J, 2014 [28] Plasma up 0.84 <0.001
Shin VY, 2015 [32] Plasma down <0.05
Zhang L, 2015 [30] Serum up 0.84 0.001
19a Chan M, 2013 [22] Serum up 0.016
Záveský L, 2022 [35] Plasma down 0.038
200b Wu Q, 2012 [21] Serum up
200c Wu Q, 2012 [21] Serum up
Ng E K, 2013 [25] Plasma up
20a Chan M, 2013 [22] Serum up <0.001
Záveský L, 2022 [35] Plasma down 0.017
20b Chan M, 2013 [22] Serum up 0.001
Záveský L, 2022 [35] Plasma down 0.011
23a Wu Q, 2012 [21] Serum up
23b Wu Q, 2012 [21] Serum up
Shen J, 2014 [28] Plasma up 0.76 0.009
Shin VY, 2015 [32] Plasma up <0.05
26a Wu Q, 2012 [21] Serum up
26b Chan M, 2013 [22] Serum down 0.005
Záveský L, 2022 [35] Plasma down 0.011
27a Wu Q, 2012 [21] Serum up
Ng E K, 2013 [25] Plasma up
27b Wu Q, 2012 [21] Serum up
Jusoh A, 2021 [34] Plasma up 0.82 0.010
29a Wu Q, 2012 [21] Serum up
Zearo S, 2014 [29] Serum up <0.001
Zhang L, 2015 [30] Serum up 0.029
29b Wu Q, 2012 [21] Serum up
29c Wu Q, 2012 [21] Serum up
Zhang L, 2015 [30] Serum up 0.81 0.001
30a Chan M, 2013 [22] Serum up 0.029
30b Chan M, 2013 [22] Serum down 0.027
Shen J, 2014 [28] Plasma up 0.76 <0.001
30c Shen J, 2014 [28] Plasma up 0.77 <0.001
30d Chan M, 2013 [22] Serum up 0.008
30e Wu Q, 2012 [21] Serum up
320a Wu Q, 2012 [21] Serum up
Chan M, 2013 [22] Serum up <0.001
Ferracin M, 2015 [31] Plasma up
320b Chan M, 2013 [22] Serum up <0.001
320d Godfrey AC, 2013 [24] Serum up 0.040
33a Shen J, 2014 [28] Plasma up 0.79 <0.001
34a Hamam R, 2016 [33] Blood up 0.044
374a Shen J, 2014 [28] Plasma up 0.75 0.004
374b Chan M, 2013 [22] Serum down 0.007
376a Cuk K, 2013 [23] Plasma up 0.386
376c Cuk K, 2013 [23] Plasma up 0.224
449b Zhang L, 2015 [30] Serum up 0.89 <0.001
516b Schrauder MG, 2012 [20] Blood up 0.67 0.030
Zhang L, 2015 [30] Serum up 0.038
519a Cuk K, 2013 [23] Plasma down 0.407
519c Zhang L, 2015 [30] Serum up 0.85 0.003
520c Zhang L, 2015 [30] Serum up 0.80 0.003
526a Schrauder MG, 2012 [20] Blood down 0.72 0.013
526b Cuk K, 2013 [23] Plasma down 0.386
548b Záveský L, 2022 [35] Plasma up 0.001
548c Záveský L, 2022 [35] Plasma up 0.035
548d Godfrey AC, 2013 [24] Serum down 0.010
Záveský L, 2022 [35] Plasma up 0.002
551a Chan M, 2013 [22] Serum down 0.002
642b Hamam R, 2016 [33] Blood up 0.020
92a Wu Q, 2012 [21] Serum up
Chan M, 2013 [22] Serum up <0.001
Shin VY, 2015 [32] Plasma up <0.05
92b Chan M, 2013 [22] Serum up 0.003
99b Shen J, 2014 [28] Plasma up 0.81 <0.001
let7a Schrauder MG, 2012 [20] Blood up 0.65 0.030
let-7a Chan M, 2013 [22] Serum up 0.005
let-7b Chan M, 2013 [22] Serum up <0.001
Zearo S, 2014 [29] Serum up <0.001
Záveský L, 2022 [35] Plasma down 0.026
let-7c Chan M, 2013 [22] Serum up 0.009
Záveský L, 2022 [35] Plasma down 0.038
let-7d Shen J, 2014 [28] Plasma up 0.89 <0.001
let-7f Chan M, 2013 [22] Serum up 0.016
Shen J, 2014 [28] Plasma up 0.81 <0.001
let-7g Chan M, 2013 [22] Serum up 0.002
Ng E K, 2013 [25] Plasma up
let-7i Chan M, 2013 [22] Serum up <0.001
U6 snRNA Záveský L, 2022 [35] Plasma up 0.004
Open in a new tab

To summarize the results of the studies, we decided not to discriminate between mature miRNAs originating from the opposite arms of the same precursor miRNA (i.e., we did not include suffixes such as ‘−3p’ or ‘−5p’ in the tables and figures).

The most interesting miRNAs that appear to be cfmiRNAs deserving validation in further studies are MIR16, MIR145, MIR106a, MIR193b, and MIR199a. In fact, these specific cfmiRNAs emerged in at least two independent papers for each sample type, both in serum and plasma studies as potential candidates for validation studies (Figure 4). Moreover, only MIR193b showed a coherent direction among the cases and controls.

Figure 4.

Figure 4

Figure 4

Open in a new tab

Pyramidal graph of the direction of miRNA expression (microRNA concentration in breast cancer cases versus controls) by type of specimens (only microRNAs that were analyzed in two or more independent studies). (A) Plasma; (B) serum.

The data in McDermott [27] were not included in Table 2 due to the lack of information on the direction, AUC, and p-value. Suffixes such as ‘−3p’ or ‘−5p’ are not considered in the cfmiRNA description.

The two cfmiRNAs that were selected as the most interesting in terms of coherence among studies in the previous metanalyses (MIR21 and MIR155) [13,14] emerged as statistically significant in the discovery phases only in plasma or serum, respectively.

Forty-two other cfmiRNAs other than MIR21 and MIR155 showed statistically significant different concentrations between the BC cases and healthy controls in at least two studies.

Unfortunately, the considered articles do not provide adequate data to draw a metanalysis forest plot.

4. Discussion

In a previous paper by our group, we conducted a systematic review of clinical studies on cfmiRNAs for the diagnosis of BC [13]. The review encompassed all studies that validated or analyzed candidate genes. In that study, we found a lack of consistency in the circulating cfmiRNAs identified across various studies. Similar results have been described in previous reviews [14,15,16,17].

This lack of replication among studies could be attributed to several factors, such as variations in the methods used for selecting cfmiRNAs, the absence of standardized techniques (including differences in sample collection and preservation, laboratory methodologies, cfmiRNA measurement and normalization, and cut-off values), inconsistent patient selection, limited cfmiRNA abundance, small sample sizes, and inadequate statistical analysis.

Recognizing the discovery phase as a potential contributor to inconsistency in the results, we performed a review of the studies that involved a discovery phases. The aim of the present work was to describe and resume the results of discovery phase studies to find the most promising cfmiRNAs that could be replicated in future candidate cfmiRNA studies. Furthermore, we will try to at least explain the lack of reproducibility of the previous candidate studies.

In general, the accurate quantification of cfmiRNAs in body fluids poses several challenges due to their low abundance and small size. This is particularly challenging for discovery studies that, in order to detect large numbers of cfmiRNAs simultaneously, use microarray profiling, quantitative RT-PCR profiling, or targeted assays of specific cfmiRNAs.

The most common biofluids used for cfmiRNA analysis are whole blood, serum, and plasma. Moreover, using the same sample type, different methods of sample preparation, anticoagulation, centrifugation, and storage properties, especially if the same high-throughput technique were used, contributed to variability and inconsistencies between reported results.

Another critical step in discovery studies is normalization, which contributes to the heterogeneity of the results. Deng et al. proposed a solution to the normalization issue which might produce more consistent results [36]; however, very few studies applied this method.

Furthermore, the same normalization issue was encountered in the collection of fold changes; they could not be compared as they were constructed using different methods, resulting in varying normalizations with the 2-delta method [37], percentage variations, and concentration ratios.

This highlights the necessity of standardized statistical analyses during discovery phases, especially when comparing cfmiRNAs concentrations between cases and controls. An illustrative example of the lack of standardization is the observed omission of multiple testing adjustment, a factor that could potentially introduce bias. In fact, implementing a p-value cutoff for candidate selection could introduce inflated effect sizes, thereby potentially distorting results. For this reason, it is essential to strike a careful balance between not adjusting for multiple comparison and diminishing statistical power due to the selection of a reduced number of candidate biomarkers.

Due to the considerable variability in the outcomes of cfmiRNA studies, as described above, consolidating the findings of diverse studies through systematic reviews enables an improvement in the body of evidence. In particular, five cfmiRNAs (MIR16, MIR145, MIR106a, MIR193b, and MIR199a) emerged from the discovery phases both in serum and in plasma in at least two independent papers as potential candidates for validation studies for BC diagnosis. This result is weakened by the fact that these miRNAs except one (MIR193b) showed varying counts between cases and controls, with inconsistent directions across different studies.

In addition to the necessity of conducting well-designed rigorous studies, there exists a critical need to enhance the reporting of scientific research. Checklists designed to assist authors in reporting biomarker studies, such as those provided by the STROBE-ME (Strengthening the Reporting of Observational studies in Epidemiology—Molecular Epidemiology) initiative, could significantly aid in crafting scientific papers with essential information concerning the collection, handling, and storage of biological samples; laboratory methods; the validity and reliability of biomarkers; nuances of study design; and ethical considerations [38].

Accurate and standardized reporting has the potential to greatly contribute to the accumulation of information in systematic reviews, which, in turn, can facilitate the advancement of our understanding of miRNA dynamics and their associations with various cancers.

5. Conclusions

The discovery phases of studies on biomarkers are crucial for identifying interesting signals to translate into clinical diagnostics. The bias encountered in this phase could cause a suboptimal discovery of new candidate biomarkers and could nullify the research effort.

For the aforementioned reason, we express our hope that forthcoming studies on cfmiRNAs that remain a promising biomarker to be implemented in liquid biopsies for BC diagnosis will have robust design and standardized procedures.

Studies including Black or Hispanic populations, other age groups, and patients with other medical conditions should be run. Additionally, it would be beneficial to capitalize on high-throughput laboratory technologies to conduct discovery studies using an appropriate sample size; to adopt a prospective design; and to adhere to standardized protocols for sample preparation, normalization, and data analysis.

Finally, researchers publishing articles on miRNAs and breast cancer should adhere to the STROBE-ME checklist when composing their papers. This approach is poised to significantly enhance the quality of their work and to propel advancements in knowledge within this domain.

Author Contributions

Conceptualization, C.S., L.D.M. and F.R.; methodology, L.P., V.F., G.M., F.R. and C.S.; validation, C.S., L.D.M. and M.T.G.; formal analysis, L.P., L.M. and A.M.; data curation, L.P., L.M., A.M. and C.S.; writing—original draft preparation, C.S. and L.P.; writing—review and editing, all authors; supervision, all authors; funding acquisition, C.S. and L.D.M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Funding Statement

This research was funded by the Italian Ministry of Health (project n. RF 2018 12366921).

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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