Advances in Clinical and Experimental Medicine

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Advances in Clinical and Experimental Medicine

2023, vol. 32, nr 10, October, p. 1201–1210

doi: 10.17219/acem/171745

Publication type: research letter

Language: English

License: Creative Commons Attribution 3.0 Unported (CC BY 3.0)

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Zajkowska A, Czajka M, Gulik K, Gawrychowski K, Małecki M. Profiling of microRNA as a tool to introduce rAAV vectors in gene therapy of breast cancer: A preliminary report. Adv Clin Exp Med. 2023;32(10):1201–1210. doi:10.17219/acem/171745

Profiling of microRNA as a tool to introduce rAAV vectors in gene therapy of breast cancer: A preliminary report

Agnieszka Zajkowska1,A,B,C,D, Milena Czajka1,D,E, Krystian Gulik2,C, Krzysztof Gawrychowski1,B, Maciej Małecki1,3,A,E,F

1 Department of Applied Pharmacy, Faculty of Pharmacy, Medical University of Warsaw, Poland

2 Department of Physical Chemistry, Faculty of Pharmacy, Medical University of Warsaw, Poland

3 Laboratory of Gene Therapy, Faculty of Pharmacy, Medical University of Warsaw, Poland

Graphical abstract


Graphical abstracts

Abstract

Background. Despite the wide range of diagnostic and therapeutic methods, breast cancer is responsible for many deaths each year. One of the original and novel cancer therapeutic approaches is gene therapy based on recombinant adeno-associated viral vectors. Among the molecular factors with the potential to become useful diagnostic biomarkers, microRNA (miRNA) molecules are being considered for personalized therapies.

Objectives. The aim of the study was to examine the utility of miRNA profiling in the design of personalized recombinant adeno-associated virus (rAAV)-based gene therapy for breast cancer patients.

Materials and methods. The analysis of 754 miRNAs in 7 breast cancer samples and control samples was performed using real-time polymerase chain reaction (PCR) based on TaqMan® Low-density Array (TLDA) cards. Online repositories were used to explore the relationship between miRNAs and genes encoding rAAV receptors (KIAA0319L, HSPG2, FGFR1, c-MET, PDGFRA, ITGB5, and RPSA). Then, we performed a comparative analysis of the results to examine the possibility of using miRNA profiling in the design of rAAV-based therapeutic protocols.

Results. Fifty-two percent of tested miRNAs were noted in at least 1 analyzed breast cancer and control tissue. Thirteen miRNAs were selected due to being outliers in the tested samples. In total, 155 miRNAs targeted genes encoding rAAV receptors in the tested samples (29 miRNAs for KIAA0319L, 60 miRNAs for c-MET, 31 miRNAs for HSPG2, 43 miRNAs for FGFR1, 36 miRNAs for PDGFRA, 18 miRNAs for RPSA, and 25 miRNAs for ITGB5). The expression of the selected miRNAs was not homogeneous across the 7 samples.

Conclusions. Profiling of microRNA could be a significant factor in the design of rAAV-based personalized gene therapy for breast cancer patients.

Key words: breast cancer, microRNA, gene therapy, rAAV

Background

As of 2020, breast cancer has become the most diagnosed cancer worldwide. Despite the development of anticancer treatments, the number of cases is still estimated to be approx. 2.26 million, causing more than 680,000 deaths in 2020.1, 2 Undoubtedly, an important factor in the treatment of the disease is effective oncological therapy, which depends on the selection of the therapeutic protocol.3, 4, 5 Molecular diagnostics could be an extremely valuable tool in the treatment of breast cancer, which not only enables the detection of tumors but can also contribute to the selection of an appropriate oncological treatment protocol.6, 7, 8 A novel approach in the selection of a therapeutic protocol for breast cancer could be the profiling of microRNAs (miRNAs).8, 9, 10, 11, 12 MicroRNAs constitute small, single-stranded RNA molecules of 14–22 nucleotides, whose biological role is to inhibit gene expression at the level of translation.13, 14 In addition, miRNAs are characterized by their stability and tissue or cell specificity.15 Because of these characteristics, they represent an interesting diagnostic solution with a great potential for the detection of organ injury (e.g., miRNA-208 in the diagnosis of cardiac injury16, 17), determining the nature of the ongoing disease process (e.g., miRNA-21 as a biomarker for breast cancer18, 19, 20) or detecting congenital malformations of the fetus.21, 22, 23 Moreover, miRNAs are involved in numerous physiological and pathophysiological processes, including tumorigenesis, which gives them the potential as a tool to personalize treatment, including oncological treatments, e.g., for breast cancer.15, 24, 25

The anticancer treatments that have been proposed for the treatment of breast cancer do not provide satisfactory results.5, 26, 27 One of the novel therapeutic approaches could be gene therapy using recombinant adeno-associated virus (rAAV) vectors. The rAAV vectors are small, non-pathogenic, characterized by cell tropism, and display high transduction efficiency to selected cell types. Therefore, rAAV vectors are favorable candidates for the treatment of diseases, including cancer.28, 29, 30 The use of rAAV vectors in oncology treatment could be associated with an increased survival rate of patients.31, 32, 33 However, the transduction efficiency using rAAV vectors can be limited due to antigen-presenting cells, which stimulate the patient’s immune system against transgene products that are expressed after the introduction of the rAAV vectors. This effect has the potential to reduce the effectiveness of gene therapy.34, 35, 36, 37 Furthermore, the effectiveness of gene therapy based on the use of viral vectors depends on the delivery of the therapeutic gene into the cell. This effect is related to the efficient binding of the viral vector to target cell surface receptors.34, 35, 36, 37, 38, 39 It is well known that miRNAs are involved in the regulation of gene expression.13, 14 Using the miRNA profile, gene therapy with the use of rAAV vectors could be personalized, resulting in increased transduction efficiency of cells in, e.g., breast cancer. The miRNA profiling could be used to develop safe and effective anticancer therapies for breast cancer.28, 29, 30

Objectives

The study aimed to examine the utility of the miRNA profiling in the design of personalized rAAV-based gene therapy for breast cancer patients. In breast cancer, the presence of miRNAs targeting silencing of mRNAs encoding receptors for rAAV that condition rAAV vector transduction may result in ineffectiveness of rAAV vector-based anti-cancer therapy. Knowing the patient-specific miRNA signature could help select the best rAAV-based viral vector to guarantee the effectiveness of gene therapy.

Materials and methods

Breast cancer and control samples

Breast cancer tissue samples were collected from 7 patients diagnosed with breast cancer (age: 70.1 ±8.8 years). Normal breast tissue samples adjacent to the tumor were collected from 3 enrolled patients (age: 73 ±6.7 years). The experiment was performed after obtaining the approval of the Medical University of Warsaw (WUM) Bioethical Committee, Poland (approval No. KB/62/2017).

Isolation of miRNAs

Samples were stored at −80°C in RNAlater™ Stabilization Solution (Invitrogen, Waltham, USA) until the isolation of RNA. Total RNA containing the miRNA fraction was isolated from samples using the mirVana™ miRNA Isolation Kit (Ambion; Thermo Fisher Scientific, Waltham, USA), according to the manufacturer’s protocol. The RNA quality and quantity were assessed using spectrophotometric measurement (Q3000; Quawell Technology, San Jose, USA) and RNA integrity testing (Agilent 2100, RNA 6000 Nano Kit; Agilent Technologies, Santa Clara, USA), with the assessment of miRNA quantity (Agilent 2100; Small RNA Kit; Agilent Technologies).

Assessment of miRNA profiles in breast cancer/control samples

The miRNA profiling was performed using microfluidic cards with lyophilized TaqMan Molecular Probes (TaqMan® Low-density Array (TLDA) cards), namely TaqMan® Human MicroRNA Array A (Applied Biosystem, Foster City, USA) and TaqMan® Human MicroRNA Array B v3.0 (Applied Biosystem), to detect 754 different miRNAs and non-coding RNA molecules, known as endogenous controls (RNU44 (assay ID: 001094), RNU48 (assay ID: 001006) and U6 snRNA (assay ID: 001973). First, cDNA of all tested miRNAs and non-coding RNA was synthesized using the TaqMan® MicroRNA Reverse Transcription (RT) Kit (Applied Biosystems), according to the manufacturer’s protocol. An RT reaction was carried out under thermal conditions according to the manufacturer’s protocol. Then, a real-time polymerase chain reaction (PCR) reaction was performed. The obtained reaction mixture was dispersed into each port included in the TLDA card. The PCR reaction was carried out using a ViiA™ 7 Real-Time PCR thermocycler (Applied Biosystems), according to the thermal profile as recommended by the manufacturer. The PCR reaction was performed in duplicate for each analyzed sample. The results were examined with the use of ExpressionSuite Software v. 1.0.3 (Thermo Fisher Scientific). A manual threshold was set at 0.2 for all samples. The miRNAs whose copy threshold (Ct) values were found to be <35 in at least one of the analyzed samples, including the control sample, were included in the in silico analysis.40 The miRNA level was presented as an ΔCt value, which expresses the normalized Ct value relative to the endogenous control. In this study, RNU48 (assay ID: 001006) was used as the endogenous control, having the least variation within the tested samples. The results are presented using the 2−ΔCt method.

Bioinformatic analysis

Online repositories were used to perform the association analysis between breast cancer miRNAs and selected genes encoding receptors for rAAV-based vectors: miRTarBase v. 7.0 (https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2022/php/index.php); miRDB v. 5.0 (http://www.mirdb.org/), miRanda (https://bioweb.pasteur.fr/packages/pack@miRanda@3.3a), and TarBase v. 7.0 (http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=tarbase/index). The following genes encoding receptors for AAV-based vectors were selected for the analysis: KIAA0319L (NM_024874), HSPG2 (NM_005529), FGFR1 (NM_013439), c-MET (NM_000245), PDGFRA (NM_006206), ITGB5 (NM_002213), and RPSA (NM_002295).

Statistical analyses

The results are shown as mean and 95% confidence intervals (95% CIs). The one-way analysis of variance (ANOVA) was used to compare samples in relation to ΔCt. Tukey’s post-hoc test was performed to estimate statistically significant differences between the test groups. Grubbs test was used to analyze ΔCt outliers within the study samples. The results are shown with a significance level of p < 0.05 (see Supplementary Table 1 for details of statistical test assumptions). The statistical analysis was performed using Statistica v. 13.3 software (StatSoft Polska Sp. z o.o., Kraków, Poland).

Results

Profiling miRNAs in breast cancer patients

The miRNA profiling assay indicated that 52% of all miRNAs tested (representing 395 miRNAs) satisfied the sensitivity criteria of the method, i.e., showed Ct values <35 in at least one of the analyzed breast cancer and control tissues. A list of miRNAs present in breast cancer is available from the corresponding author. We found that the miRNA profile was found to be diversified across the 7 breast cancer samples (Figure 1). The analysis of the ΔCt values in the samples showed significant differences amongst themselves and when compared to the controls. The visualization of miRNA expression in breast cancer using the heatmap (Figure 1A) demonstrated heterogeneous levels of miRNAs, as shown for the p3 sample. It was characterized by the largest number of miRNA molecules with the highest levels of expression (highest 2−ΔCt value) and was statistically different from most of the analyzed samples (Figure 1B).

Analysis of the most significant miRNAs in breast cancer

The statistical analysis (Grubbs test) was used to distinguish 13 miRNAs which were the most outliers among all the miRNAs studied in breast cancer: let-7c-5p (p = 0.0475), miRNA-1290 (p = 0.040), miRNA-187-3p (p = 0.029), miRNA-224-5p (p = 0.024), miRNA-30d-3p (p = 0.036), miRNA-425-3p (p = 0.047), miRNA-454-3p (p = 0.038), miRNA-454-5p (p = 0.043), miRNA-501-5p (p = 0.034), miRNA-766-3p (p = 0.032), miRNA-770-5p (p = 0.047), miRNA-885-5p (p = 0.026), and miRNA-99b-3p (p = 0.042). These were not characterized by homogeneous levels or even presence defined as a Ct value <35 in the tested samples. Specifically, miRNA-187-3p and miRNA-766-3p were considered absent in 28.6% of the samples, while miRNA-454-5p and miRNA-770-5p failed to meet the criterion of Ct value <35 in 71.4% and 57.1% of breast cancer samples, respectively (Figure 2A,B).

Analysis of the miRNA profile in breast cancer showed genes encoding rAAV vector receptors

Four online repositories were analyzed to explore the potential of miRNAs to silence receptor gene expression in breast cancer. The miRNAs were selected to potentially silence the expression of any of the tested mRNAs, namely genes encoding receptors for rAAV-mediated cell transduction (KIAA0319L, c-MET, HSPG2, FGFR1, PDGFRA, RPSA, and ITGB5). The in silico analysis identified 155 miRNAs that could influence the mRNA expression of these cellular receptors for rAAV. Notably, heterogeneity was observed in miRNA levels between breast cancer samples. In addition, there were significant differences in the ΔCt values of miRNAs capable of regulating rAAV receptor expression between the control and 2 breast cancer samples (p3 and p4; Figure 3A,B).

miRNAs with the potential to inhibit the expression of particular rAAV receptors

Our studies have shown that all analyzed genes can be regulated by miRNAs identified in this study. The profile of the selected miRNAs was not homogeneous in the samples. However, there were no significant differences in the levels of miRNAs specific to any separately tested gene among breast cancer samples (Figure 3). Interestingly, the selected miRNAs with the potential to silence genes encoding receptors for rAAV had not been present unequivocally in the tested samples (Table 1).

We found that 36.8% of the selected miRNAs are complementary to more than one mRNA of the analyzed genes. It is particularly evident for miRNA-22-3p and miRNA-34a-5p, which have the potential to silence as many as 5 genes encoding rAAV receptors (c-MET, HSPG2, FGFR1, RPSA, ITGB5, and c-MET, FGFR1, RPSA, ITGB5, PDGFRA, respectively) (Table 1).

KIAA0319L, c-MET and HSPG2 genes

Among the analyzed miRNAs, 29 demonstrated a probability of silencing the KIAA0319L gene (Figure 4A), 60 could silence the c-MET gene (Figure 4C), and 31 could silence the HSPG2 gene (Figure 4E). The variation of miRNA levels was visualized using a heatmap, and cluster analysis was performed for the breast cancer samples (Figure 4A,C,E, respectively). The miRNA profile of the p3 sample is distinguished in the range of 2−ΔCt values (Figure 4A,C,E). Furthermore, ANOVA showed no statistical significance between the ΔCt values among the breast cancer samples tested for the KIAA0319L gene (Figure 4B) and HSPG2 gene (Figure 4F). However, it demonstrated significant differences between the ΔCt values of miRNAs in the p3 sample and control tissue (k) for the c-MET gene (Figure 4D).

FGFR1, PDGFRA, RPSA, and ITGB5 genes

The current study found 43 miRNAs capable of silencing the FGFR1 gene (Figure 5A), 36 miRNAs that could silence the PDGFRA gene (Figure 5C), 18 for the RPSA gene (Figure 5E), and 25 for the ITGB5 gene (Figure 5G). These miRNAs could all potentially silence the expression of the genes encoding receptors necessary for the entry of rAAV. No significant differences were observed in the ΔCt of either the breast cancer or control samples (Figure 5B,D,F,H).

Discussion

Breast cancer is the most common cancer diagnosed globally in female patients,1, 2, 41, 42 thus various innovative treatment options and diagnostics continue to be developed.5, 6, 27, 43, 44, 45 Certainly, one of the novel treatment approaches could be gene therapy.46, 47 In this regard, the most frequently utilized gene carriers are viral vectors.48, 49 The rAAV vectors are of particular interest because of their unique features.28, 29, 30, 31, 32 The synthesis of the corresponding protein is influenced by several factors, including miRNAs. Interestingly, previous studies demonstrated that the expression of up to 60% of human genes is regulated by miRNAs, which have the potential to inhibit the formation of correct protein. Therefore, miRNAs are extremely important in the design of gene therapy, including breast cancer therapy, in which personalization is the goal, and where the most ideal viral vector needs to be selected.35, 39 The purpose of this study was to highlight whether endogenous miRNAs can act as potential biomarkers in the personalization of rAAV vector-based gene therapy for breast cancer. We analyzed the miRNA profile in breast cancer samples, which were obtained from 7 patients using quantitative (q)PCR TLDA cards. The study demonstrated that 52% of miRNA molecules, representing 395 of the 754 molecules, were positive in at least 1 of the tested samples (Figure 1A). The paper also examined the diversity of levels of all included miRNAs. The level expressed as ΔCt was found to vary over the range of tested samples (Figure 1B). It is noteworthy that the p3 sample shows a high degree of variability and is significantly different from the other breast cancer and control samples (Figure 1A). Moreover, the miRNA profile for the individual probes varies, which has the potential to be a consequence of the molecular subtype of breast cancer. Blenkiron et al. showed that the profile of miRNA is variable and characteristic, depending on the molecular subtype of breast cancer.50 In addition, a cluster analysis of the breast cancer samples conducted by Blenkiron et al. presented a high degree of miRNA diversity. The authors suggested it to be a result of miRNA deregulation in cancer cells. Furthermore, Tsai et al. depicted the varying expression of miRNAs in breast cancer.51 The researchers demonstrated a heterogeneous profile of miRNA molecules that depend on the age of patients, the tumor lesion and the hormone receptor profile. Similar studies indicating the presence of miRNA heterogeneity in breast cancer were conducted by Sempere et al.52 In this study, the levels of miRNA heterogeneity were analyzed. In breast cancer samples tested herein, the levels of miRNA varied the most for the molecules presented in Figure 2. A distinguishing property of miRNAs is that they can function both as molecules that promote or inhibit the neoplastic process within the same tumor.53, 54 Fluctuations in the level of miRNAs observed herein suggest the individualism of the studied sample, which was also shown in the study by Galka-Marciniak et al.55 In this work, miRNAs in neoplastic tissue were analyzed in association with genes that are involved in the formation of protein surface receptors of cells targeted by rAAV vectors. These vectors are increasingly used in clinical trials of gene therapy, including anticancer therapy. More importantly, several drugs based on rAAV have been registered, including Zolgensma (U.S. Food and Drug Administration (FDA)), Luxturna (European Medicines Agency (EMA)) and Glybera (EMA). The in silico analysis showed that the expression of each of the selected genes could be regulated by dozens of miRNAs. Moreover, a single miRNA can influence the regulation of several genes encoding receptor proteins that target viral vectors. Examples include miR-22-3p and miR-34a-5p, both of which can silence 5 of the 7 genes tested in the rAAV vector (e.g., miR-22-3p inhibits the expression of c-MET, HSPG2, FGFR1, RPSA, and ITGB5) (Table 1). The significant variance across miRNA levels between the tested samples can influence the expression of receptor proteins, which are essential for the introduction of viral vectors into target cells. For instance, miRNA-224-5p is a molecular target of the PDGFRA gene, whose protein is a co-receptor for rAAV5. Conversely, miR-766-3p can potentially be involved in inhibiting the expression of the hspg2 protein, which is a receptor for the majority of rAAV vectors.38, 39 By identifying these miRNAs for individual patients, it is entirely appropriate to propose a gene therapy based on a particular targeted rAAV or to exclude it completely. It is also interesting to observe that out of the 754 miRNAs indicated in our breast cancer samples, 395 were identified with PCR, of which just 155 could potentially inhibit the expression of rAAV receptors (Figure 3A). These findings highlight that designing an appropriate vector for gene therapy of breast cancer could prove to be a significant achievement. Among the most aberrant miRNAs in the tested breast cancer samples (Figure 2A), 5 participated in the potential regulation of receptor protein expression for rAAV vectors. They were determinable for all samples except miR-766-3p, which was not determined in the p1 probe. Based on the results, the differences in ΔCt of miRNA levels for the p3 and p4 samples were observed (Figure 3A). The ΔCt levels were lower in p3 and p4 samples, indicating higher miRNA levels compared to other samples. This higher level of miRNAs could directly influence the expression of genes they control. Based on the patient’s susceptibility to a specific rAAV serotype, it was determined that the miRNA profile could be useful in determining the efficacy of gene therapy using viral vectors. Significant differences in miRNA expression can be observed in the investigated samples (Figure 1). These differences could indicate that miRNA profiling is a useful step in the development and implementation of the selected gene therapy protocol. Concerning rAAV receptors, for example, in the p5 sample, 22.6% of the total miRNA was undetected (data not shown). In contrast, only 7.1% of miRNA molecules in the p4 sample were not detected (data not shown). Based on these results, it could be concluded that the sample with the lowest number of undetectable miRNAs could be predisposed to gene therapy based on the rAAV vectors. Conversely, the sample with the highest number of miRNAs is less likely to be transduced by viral vectors. Among the miRNAs tested in the study, those not determined in association with silencing of the rAAV vector receptor genes made up a small percentage. The group of examined samples showed differences in the detection of 155 miRNAs, which were identified in this study as molecules that recognize receptor genes exploited by rAAVs.

Limitations

The limitation of the study is the relatively small number of tested breast cancer samples and insufficient knowledge of medical history and sample collection. However, a major advantage of the study is the broad examination of miRNAs.

Conclusions

In summary, the obtained results demonstrate that personalized gene therapy for breast cancer could be designed using rAAV vectors. According to the selected miRNA molecules in the performed studies, it would have been possible to estimate the presence of the receptor protein indirectly in a particular breast cancer patient. This novel approach could be of considerable clinical relevance and could indicate whether rAAVs are the ideal vectors for gene therapy in a particular patient. The main purpose of the work was to thoroughly investigate the molecular signature of breast cancer, which would be a prerequisite for personalized gene therapy.

Supplementary data

The supplementary materials are available at https://doi.org/10.5281/zenodo.8213977. The package contains the following files:

Supplementary Table 1. Test assumptions in the statistical analysis.

Tables


Table 1. miRNA with the potential to silence more than 1 gene encoding receptors for recombinant adeno-associated virus (rAAV)

Number of simultaneously silenced rAAV receptor genes (n = 6)

miRNA

5

hsa-miR-22-3p, hsa-miR-34a-5p

4

hsa-let-7a-5p, hsa-let-7b-5p, hsa-miR-130a-3p, hsa-miR-218-5p, hsa-miR-335-5p, hsa-miR-449a, hsa-miR-455-3p

3

hsa-let-7e-5p, hsa-let-7f-5p, hsa-let-7g-5p, hsa-miR-141-3p, hsa-miR-15a-5p, hsa-miR-16-5p, hsa-miR-200a-3p, hsa-miR-27b-3p, hsa-miR-34c-5p, hsa-miR-532-3p, hsa-miR-543

2

hsa-miR-101-3p, hsa-miR-125a-5p, hsa-miR-125b-5p, hsa-miR-130b-3p, hsa-miR-137, hsa-miR-146b-5p, hsa-miR-150-5p, hsa-miR-155-5p, hsa-miR-15b-5p, hsa-miR-17-5p, hsa-miR-181a-5p, hsa-miR-185-5p, hsa-miR-195-5p, hsa-miR-199a-3p, hsa-miR-20a-3p, hsa-miR-21-3p, hsa-miR-214-3p, hsa-miR-26b-5p, hsa-miR-27a-3p, hsa-miR-29a-3p, hsa-miR-29b-3p, hsa-miR-320a, hsa-miR-330-3p, hsa-miR-362-3p, hsa-miR-424-5p, hsa-miR-425-5p, hsa-miR-449b-5p, hsa-miR-450b-5p, hsa-miR-454-3p, hsa-miR-484, hsa-miR-486-3p, hsa-miR-486-5p, hsa-miR-345-5p

Figures


Fig. 1. miRNA present in breast cancer and control samples. A. Heatmap shows the differential expression of miRNAs between breast cancer patients and controls (2−ΔCt) (red color stands for the highest value of 2−ΔCt, and blue color indicates the lowest value of 2−ΔCt); B. The results are presented as a mean of ΔCt ±0.95 confidence interval. The statistical significance is shown (analysis of variance (ANOVA): F(7;3152) = 7.154; p < 0.0001; Tukey’s post-hoc test)
p1–p7 – breast cancer samples; k – control.
Fig. 2. The most outlier miRNAs in breast cancer sample (n = 7). A. Heatmap (red color stands for the highest value of 2−ΔCt, blue color indicates the lowest value of 2−ΔCt); B. The graph presents miRNA outliers for individual patients (p1–p7), the line indicates copy threshold (Ct) =35
Fig. 3. miRNAs potentially inhibit 7 genes encoding receptors for recombinant adeno-associated virus (rAAV). A. Heatmap shows the differential expression of miRNAs between breast cancer patients and controls (2−ΔCt). Red color stands for the highest value of 2−ΔCt, and blue color indicates the lowest value of 2−ΔCt; B. The results are presented as mean of ΔCt ±0.95 confidence interval. The statistical significance is presented (analysis of variance (ANOVA): F(7;1232) = 3.008; p = 0.004; post-hoc Tukey’s test)
p1–p7 – breast cancer samples; k – control.
Fig. 4. A,C,E. Heatmap showing the differential expression of miRNAs between breast cancer patients and controls (2−ΔCt) for KIAA0319L (A), c-MET (C) and HSPG2 (E) genes; B,D,F. The results are presented as mean of ΔCt ±0.95 confidence interval, and the outcomes of the analysis of variance for KIAA0319L (B), c-MET (D) and HSPG2 (F) genes. The statistical significance is presented (analysis of variance (ANOVA): F(7;224) = 0.718; p = 0.657 (B), F(7;472) = 2.201; p = 0.033 (D), F(7;240) = 0.527; p = 0.814 (F); Tukey’s post-hoc test (D)). Red color stands for the highest value of 2−ΔCt, and blue color indicates the lowest value of 2−ΔCt
p1–p7 – breast cancer samples; k – control.
Fig. 5. A,C,E,G. Heatmap showing the differential expression of miRNAs between breast cancer patients and controls (2−ΔCt) for FGFR1 (A), PDGFRA (C), RPSA (E), and ITGB5 (G) genes; B. The results are presented as mean, and the outcomes of the analysis of variance (ANOVA) for FGFR1 (B), PDGFRA (D), RPSA (F), and ITGB5 (H) genes are displayed. The statistical analysis is shown (ANOVA: F(7;336) = 0.818; p = 0.573 (B), F(7;280) = 0.825; p = 0.567 (D), F(7;136) = 0.510; p = 0.826 (F), F(7;192) = 0.303; p = 0.952 (H)). Red color stands for the highest value of 2−ΔCt, and blue color indicates the lowest value of 2−ΔCt
p1–p7 – breast cancer samples; k – control.

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