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Predicting SNPs in Mature MicroRNAs Dysregulated in Breast Cancer

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Thanh Thi Ngoc Nguyen, Thu Huynh Ngoc Nguyen, Luan Huu Huynh, Hoang Ngo Phan and Hue Thi Nguyen

Submitted: 14 May 2022 Reviewed: 23 May 2022 Published: 18 June 2022

DOI: 10.5772/intechopen.105514

Recent Advances in Noncoding RNAs IntechOpen
Recent Advances in Noncoding RNAs Edited by Lütfi Tutar

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Recent Advances in Noncoding RNAs

Edited by Lütfi Tutar

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Abstract

Breast cancer (BC) is the leading type of cancer among women. Findings have revolutionized current knowledge of microRNA (miRNA) in breast tumorigenesis. The seed region of miRNA regulates the process of gene expression negatively. The presence of SNPs in the seed regions of miRNA dramatically alters the mature miRNA function. Additionally, SNPs in the out-seed region of miRNAs have a significant impact on miRNA targeting. This study focuses on the in silico analysis procedure of mature miRNA SNPs and their impact on BC risk. The database annotated SNPs on mature miRNAs was used. Also, target gene alterations, miRNAs function in BC, and the interaction of miRNAs with targets were predicted. A list of 101 SNPs in 100 miRNAs with functional targets in BC was indicated. Under the SNPs allele variation, 10 miRNAs changed function, 6 miRNAs lost targets, 15 miRNAs gained targets, 48 onco-miRNAs remained unchanged, and 21 tumor suppressor miRNAs remained unchanged. At last, a list of 89 SNPs, which alter miRNA function and miRNA-mRNA interaction, were shown to be potentially associated with BC risk. This research theoretically generated a list of possible causative SNPs in the mature miRNA gene that might be used in future BC management studies.

Keywords

  • mature microRNA
  • SNP
  • breast cancer
  • bioinformatics
  • in silico

1. Introduction

Breast cancer (BC) is the most prevalent cancer among women across the world. The malignant growth begins in the ducts (85%) or lobules epithelium (15%) (“in situ”), where it typically causes no symptoms and has a minimal chance of spreading (metastasis). These in situ (stage 0) tumors may increase substantially, infiltrating neighboring breast tissue (invasive BC) and expanding to nearby lymph nodes (regional metastasis) or organs (distant metastasis). This cancer is generally deadly due to metastasis spread [1]. BC was diagnosed in 2.3 million women in 2020, with 685,000 fatalities worldwide [2]. This cancer was also found in 7.8 million women living in the last 5 years, making it the world’s most frequent malignancy by the end of 2020 [2]. BC affects women at all ages following puberty in every country worldwide, with incidence increasing with age. Therefore, many researchers and clinicians are focused on the etiology of the disease to enhance current medications and discover novel treatments.

MicroRNAs (miRNAs), short non-coding RNAs of approximately 18–24 nucleotides in length, affect gene expression negatively by directly binding the 3′-untranslated region (UTR) of the target messenger RNA (mRNA) and diminishing its stability and translatability. Numerous miRNAs have roles in cell signaling, including proliferation, death, differentiation, and immunity [3]. It has been proposed that they have a role in the formation and development of human malignancies, making them important markers of cancer [4]. The involvement of microRNAs in complex diseases like BC has focused on recent studies [5]. Various miRNAs have been related to regulating genes that have a role in the development of BC. MiR-21, miR-26a, miR-155, miR-221/miR-222, and miR-495 are some of the onco-miRs involved in tumor proliferation and angiogenesis [6, 7, 8, 9, 10, 11, 12]. Various miRNAs (miR-100, miR-125b, miR-126, miR-145, miR-200c, miR-298, and miR-335) have been involved in cell cycle regulation, hypoxia and stress response, and apoptotic induction [13, 14, 15, 16, 17, 18, 19, 20, 21, 22]. In addition, McAnena et al. [23] found that circulating miR-332 and miR-195 may be utilized to discriminate between local and metastatic BC. Meanwhile, Sathipati et al. [24] indicated that 34 miRNAs could be employed to classify the early and late stages of BC development. These findings support the concept that a small selection of miRNAs can be considered biomarkers for BC risk prediction or prognosis [25].

The seed region of miRNA, located between the second and eighth positions in miRNA, regulates gene expression negatively [26]. As miRNAs are small, even a single alteration in the mature sequence of miRNAs might impact the development of miRNAs, which leads to the production of new miRNA [27]. A novel repertoire of target genes is produced by the presence of SNPs in the seed regions of miRNA, which dramatically alters the biological activity of the miRNA [28]. Additionally, SNPs in the out-seed region of miRNAs significantly impact miRNA targeting enhancements [29].

Therefore, in this study, the functional effect of SNPs in miRNAs regulating the genes implicated in BC has been predicted by bioinformatics approaches, including (1) screening miRNAs that reveal SNPs in mature sequences, (2) identifying miRNAs’ target genes related to BC, (3) predicting miRNAs function in BC, and (4) estimating the degree of interaction between miRNAs and target mRNAs. The results may be beneficial in determining potential SNPs to analyze further and investigate SNPs that are causative to BC. Furthermore, the findings may potentially help generate theories and evaluate therapies for BC.

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2. Materials and methods

2.1 SNPs in miRNA mature sequences database

In this study, the list of SNPs in mature miRNA sequences was extracted from the “SNPs in pre-miRNAs” database provided by miRNASNP-v3 [30] (http://bioinfo.life.hust.edu.cn/miRNASNP#W/). Mature miRNA location, positioning, and sequences were obtained from miRbase (www.mirbase.org), release 22 [31]. SNP position were acquired from dbSNP [32] (MAF > 1%) (https://www.ncbi.nlm.nih.gov/projects/SNP/snp_summary.cgi).

2.2 In silico target prediction in BC pathways

The miRDB (http://mirdb.org/) was adapted to input both miRNA reference and SNP altered sequences for target prediction [33]. Target prediction was performed for all miRNAs containing reference and SNP alternative sequences. All predicted targets with prediction scores ≥80 that are most likely to be accurate [34], were collected and applied to conduct Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses for the identified target genes in the BC pathway (hsa05224). KEGG is a knowledge base for systematic analysis of gene functions [35] (https://www.genome.jp/pathway/hsa05224). MiRNAs that target genes in the BC pathway are considered to be involved in BC formation. The target with the highest predicted target score from miRDB is considered the gene most likely to affect BC via the miRNA regulation.

2.3 Function prediction of target genes and miRNAs in BC

The target genes with the highest target score from miRDB of each miRNA were further analyzed. The published expression level (Log2FC value) of genes in BC was collected from GENT2. GENT2 is a platform for exploring Gene Expression patterns across Normal and Tumor tissues [36] (http://gent2.appex.kr). From Log2FC value, target genes were considered as oncogenes (Log2FC > 0, P < 0.05) or tumor suppressors of BC (Log2FC < 0, P < 0.05).

MiRNAs may contribute to (onco-miRNAs) or repress (tumor suppressor miRNAs) the cancer phenotype by inhibiting the expression of tumor suppressor genes or oncogenes, respectively. Therefore, the role of miRNAs in BC can be predicted based on the role of target genes in BC. MiRNAs are considered oncogenic miRNAs when their target genes act as tumor suppressors. Furthermore, conversely, miRNAs were considered tumor-suppressive miRNAs when they target oncogenes. The presence of SNPs on the seed region can alter the target, thereby changing the role of miRNAs in BC.

2.4 Predicting the interaction between miRNAs and targets

The interaction between mature miRNA sequences, in both reference and alternative allele versions of the SNP, and their target genes were analyzed based on the number of seed binding sites of a miRNA at the 3’UTR by TargetScan 8.0 [37] and the Minimum Free Energy (MFE) of the whole mature miRNA sequence with the 3’UTR by RNAhybrid. The target sequences extracted from TargetScan 8.0 (http://www.targetscan.org/vert_80/). The miRNA sequences were further included in RNAhybrid (https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid), a tool for finding the MFE hybridization of a long and a short RNA [38]. The MFE values should be between −10 and − 30 kcal/mole. The more negative the MFE value (closer to −30), the stronger the interaction of the mature miRNA sequence with the 3’UTR. A miRNA can bind at multiple sites on the 3’UTR of its target, and each interaction site has a corresponding MFE value. Therefore, the interaction between the 3’UTR and the whole mature sequence of miRNA was determined via the mean value of MFEs from all miRNA binding sites on 3’UTR (Mean MFE).

The level of miRNA inhibition on target genes could be influenced by the presence of an alternative allele SNP, which could be shown through the difference between the mean MFE value of the alternative and reference allele version (ΔG). A non-zero ΔG value means a difference in the interaction degree with the same target between miRNA reference and alteration, indicating the SNP is potentially associated with BC risk. A ΔG value of 0 indicates no change in the level of interaction between the miRNA reference and the miRNA variation with the same target, suggesting that the SNP is unlikely to relate to BC risk.

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3. Results

3.1 SNPs in miRNA mature sequences

This study collected a list of 169 SNPs on mature sequences of 161 miRNAs from miRNASNP-v3. The mature reference sequences of 161 miRNAs were obtained from miRBase. The 169 alternative mature sequences of miRNAs corresponding to positional changes of 169 SNPs identified from dbSNP are described in Supplement 1.

3.2 In silico target prediction in BC pathways

After predicting the target genes of the reference and alternative sequences by miRDB (score ≥ 80) and comparing them in the list of genes on the BC pathway of KEGG, out of 161 miRNAs, there are 100 miRNAs with functional targets in BC (Supplement 2). Thirty-eight miRNAs with 38 in-seed SNPs had altered targets, and 62 with 63 out-seed SNPs did not change targets. The most likely target gene, which has the highest target score, of each miRNA was selected to analyze the possible effect on BC further.

3.3 Function prediction of target genes and miRNAs in BC

The functional target genes in BC were determined from the GENT2 database. Hence, the function of miRNAs in BC was also predicted. Of the 38 miRNAs with altered targets, the targets of 31 miRNAs were functionally altered in BC under the influence of SNPs (Table 1). Of these 31 miRNAs, 4 predicted onco-miRNAs were predicted to change to tumor suppressor miRNAs because of their new target working as oncogenes (hsa-miR-4257, hsa-miR-499a-3p, hsa-miR-501-3p, hsa-miR-593-5p), 5 predicted onco-miRNAs were predicted to lost targets in BC (hsa-miR-1178-5p, hsa-miR-4482-5p, hsa-miR-4661-3p, hsa-miR-5589-3p, hsa-miR-7854-3p), 6 predicted tumor suppressor miRNAs were predicted to change to onco-miRNAs because of their new target working as tumor suppressor genes (hsa-miR-1302, hsa-miR-2682-3p, hsa-miR-3117-3p, hsa-miR-4695-5p, hsa-miR-5692b, hsa-miR-6810-5p), 1 tumor suppressor miRNA was predicted to lost targets in BC (hsa-miR-627-5p), 10 miRNAs were predicted to gain onco-miRNAs (hsa-miR-4513, hsa-miR-4707-3p, hsa-miR-4741, hsa-miR-4781-3p, hsa-miR-4804-5p, hsa-miR-5090, hsa-miR-662, hsa-miR-6796-3p, hsa-miR-6826-5p, hsa-miR-6879-3p), and 5 miRNAs were predicted to gain tumor suppressor miRNAs (hsa-miR-1269b, hsa-miR-4467, hsa-miR-6717-5p, hsa-miR-6763-3p, hsa-miR-6777-5p). By altering the function of miRNAs, 31 in-seed SNPs on these miRNAs are likely to be associated with BC risk (Table 1).

miRNASNPReferenceAlternative
IDPositionAlleleTargetRole of miRNAAlleleTargetRole of miRNA
GeneLog2FCExpression trendCategoryGeneLog2FCExpression trendCategory
hsa-miR-1178-5prs7311975chr12:119713688TFGF9−1.22DownTumor suppressor genesOnco-miRNAC
hsa-miR-1269brs7210937chr17:12917329GCDVL30.28UpOncogenesTumor suppressor miRNA
hsa-miR-1302rs74647838chr12:112695096GESR10.95UpOncogenesTumor suppressor miRNAACTNNB1−0.40DownTumor suppressor genesOnco-miRNA
hsa-miR-2682-3prs74904371chr1:98045291CTNFSF111.80UpOncogenesTumor suppressor miRNATSOS1−0.43DownTumor suppressor genesOnco-miRNA
hsa-miR-3117-3prs12402181chr1:66628488GKRAS0.31UpOncogenesTumor suppressor miRNAASOS2−0.37DownTumor suppressor genesOnco-miRNA
hsa-miR-4257rs74743733chr1:150551992GMAPK1−0.09DownTumor suppressor genesOnco-miRNAAE2F21.20UpOncogenesTumor suppressor miRNA
hsa-miR-4467rs115101071chr7:102471476GAIGF1R0.41UpOncogenesTumor suppressor miRNA
hsa-miR-4482-5prs45596840chr10:104268396GTCF7−0.47DownTumor suppressor genesOnco-miRNAA
hsa-miR-4513rs2168518chr15:74788737GANCOA1−0.21DownTumor suppressor genesOnco-miRNA
hsa-miR-4661-3prs12335005chr8:91205534GNOTCH2−0.60DownTumor suppressor genesOnco-miRNAT
hsa-miR-4695-5prs79637190chr1:18883265CSHC10.21UpOncogenesTumor suppressor miRNATMAPK1−0.09DownTumor suppressor genesOnco-miRNA
hsa-miR-4707-3prs2273626chr14:22956973CAFGF9−1.22DownTumor suppressor genesOnco-miRNA
hsa-miR-4741rs7227168chr18:22933411CTFZD8−0.70DownTumor suppressor genesOnco-miRNA
hsa-miR-4781-3prs74085143chr1:54054127GAFGF2−1.44DownTumor suppressor genesOnco-miRNA
hsa-miR-4804-5prs266435chr5:72878605CGWNT2B−0.96DownTumor suppressor genesOnco-miRNA
hsa-miR-499a-3prs3746444chr20:34990448ATCF7L2−0.68DownTumor suppressor genesOnco-miRNAGWNT40.38UpOncogenesTumor suppressor miRNA
hsa-miR-501-3prs149912461chrX:50009773AHEY1−0.39DownTumor suppressor genesOnco-miRNAGFGF40.30UpOncogenesTumor suppressor miRNA
hsa-miR-5090rs3823658chr7:102465754GAFZD4−1.31DownTumor suppressor genesOnco-miRNA
hsa-miR-5589-3prs116796353chr19:10038396AMAP2K1−0.16DownTumor suppressor genesOnco-miRNAG
hsa-miR-5692brs451887chr21:42951004TFGF180.34UpOncogenesTumor suppressor miRNACEGF−0.71DownTumor suppressor genesOnco-miRNA
hsa-miR-593-5prs73721294chr7:128081882CSP1−0.29DownTumor suppressor genesOnco-miRNATKRAS0.31UpOncogenesTumor suppressor miRNA
hsa-miR-627-5prs2620381chr15:42199650ADVL30.28UpOncogenesTumor suppressor miRNAC
hsa-miR-662rs9745376chr16:770249GAIGF1−1.13DownTumor suppressor genesOnco-miRNA
hsa-miR-6717-5prs117650137chr14:21023373GASHC10.21UpOncogenesTumor suppressor miRNA
hsa-miR-6763-3prs3751304chr12:132582046CTWNT40.38UpOncogenesTumor suppressor miRNA
hsa-miR-6777-5prs56155608chr17:17813539GACSNK1A10.13UpOncogenesTumor suppressor miRNA
hsa-miR-6796-3prs3745198chr19:40369893CGCDK6−0.67DownTumor suppressor genesOnco-miRNA
hsa-miR-6810-5prs62182086chr2:218341922ACSNK1A10.13UpOncogenesTumor suppressor miRNAGFGF19−1.16DownTumor suppressor genesOnco-miRNA
hsa-miR-6826-5prs6771809chr3:129272155TCMTOR−0.22DownTumor suppressor genesOnco-miRNA
hsa-miR-6879-3prs74814065chr11:65018557CTFGF9−1.22DownTumor suppressor genesOnco-miRNA
hsa-miR-7854-3prs2925980chr16:81533949AAKT3−0.41DownTumor suppressor genesOnco-miRNAG

Table 1.

Prediction of the effect of in-seed SNPs in 31 miRNAs on BC development.

Ref = The allele in the reference genome. Alt = Any other allele found at that locus. Target gene with score ≥ 80. Log2FC = Log 2 Fold Change with P < 0.05.

The remaining 7 of 38 miRNAs with altered targets were predicted to remain functional despite the allele change of the SNPs (Table 2). Four (hsa-miR-146a-3p, hsa-miR-4284, hsa-miR-4731-3p, hsa-miR-557) and three miRNAs (hsa-miR-3622a-5p, hsa-miR-449c-3p, hsa-miR-548 t-3p) were predicted to be onco-miRNAs and tumor suppressor miRNAs, respectively.

MiRNAIDPositionReferenceAlternativeΔG
AlleleTargetRole of miRNAMean MFEAlleleTargetRole of miRNAMean MFE
GeneLog2FCExpression trendCategoryGeneLog2FCExpression trendCategory
has-miR-146a-3prs2910164chr5:160485411CFZD4−1.31DownTumor suppressor genesOnco-miRNA−16.55GWNT9B−0.40DownTumor suppressor genesOnco-miRNA−18.2−1.7
hsa-miR-3622a-5prs66683138chr8:27701697GE2F30.25UpOncogenesTumor suppressor miRNA−22.6ASHC10.21UpOncogenesTumor suppressor miRNA−19.43.2
hsa-miR-4284rs11973069chr7:73711334CFZD4−1.31DownTumor suppressor genesOnco-miRNA−23.7TDLL1−0.64DownTumor suppressor genesOnco-miRNA−20.53.2
hsa-miR-449c-3prs35770269chr5:55172296AKRAS0.31UpOncogenesTumor suppressor miRNA−18.2TAKT20.05UpOncogenesTumor suppressor miRNA−17.60.6
hsa-miR-4731-3prs66507245chr17:15251649TNOTCH2−0.60DownTumor suppressor genesOnco-miRNA−14.7ACTNNB1−0.39DownTumor suppressor genesOnco-miRNA−19.7−5.0
hsa-miR-548 t-3prs73872515chr4:173268209ANRAS0.21UpOncogenesTumor suppressor miRNA−15.2CIGF1R0.41UpOncogenesTumor suppressor miRNA−16.7−1.5
hsa-miR-557rs78825966chr1:168375591CRPS6KB1−0.05DownTumor suppressor genesOnco-miRNA−18.8TPGR−0.85DownTumor suppressor genesOnco-miRNA−18.50.3

Table 2.

Prediction of the effect of in-seed SNPs in 7 miRNAs on the degree of miRNA role in BC.

Log2FC = Log 2 Fold Change with P < 0.05.

MFE: Minimum Free Energy between miRNA and 3’UTR (kcal/mol).

ΔG: The difference of mean MFE between alterative allele and reference allele.

For the 62 miRNAs with 63 out-seed SNPs, the targets were not changed because the seed sequence was preserved between the two alleles of the SNPs (Table 3). The result showed that 44 and 18 miRNAs were predicted to function as oncogene and tumor suppressors, respectively. The predictive results do not sufficiently demonstrate an association of these 70 SNPs with BC risk. Therefore, the next step is further to predict the interaction of 69 miRNAs and targets to elucidate the potential association of these 70 SNPs on BC risk.

MiRNAIDPositionAllele Ref./Alt.TargetRole of miRNAMean MFEΔG
GeneLog2FCExpression trendCategoryRef.Alt.
hsa-miR-449b-5prs10061133chr5:55170716A/GDLL1−0.64DownTumor suppressor genesOnco-miRNA−25.8−24.61.2
hsa-miR-6801-3prs10412196chr19:52222085T/CAKT3−0.41DownTumor suppressor genesOnco-miRNA−20.8−19.81.1
hsa-miR-548ae-5prs10461441chr5:58530093A/GCDK6−0.67DownTumor suppressor genesOnco-miRNA−14.2−12.91.3
hsa-miR-4700-3prs1055070chr12:120723245T/GNCOA3−0.01DownTumor suppressor genesOnco-miRNA−18.1−18.6−0.5
hsa-miR-4302rs11048315chr12:25874055G/ACDK6−0.67DownTumor suppressor genesOnco-miRNA−19.4−19.00.4
hsa-miR-5579-3prs11237828chr11:79422176T/CJAG1−0.21DownTumor suppressor genesOnco-miRNA−18.6−18.60.0
hsa-miR-501-3prs112489955chrX:50009781G/ACDK6−0.67DownTumor suppressor genesOnco-miRNA−18.3−18.30.0
hsa-miR-5691rs112511786chr11:9090358G/CMAPK1−0.09DownTumor suppressor genesOnco-miRNA−19.1−19.7−0.6
hsa-miR-4786-5prs115063401chr2:239943064G/AAKT3−0.41DownTumor suppressor genesOnco-miRNA−23.7−23.70.0
hsa-miR-580-5prs115089112chr5:36147955T/CFZD30.31UpOncogenesTumor suppressor miRNA−13.4−13.9−0.5
hsa-miR-3124-3prs115160731chr1:248826432C/APGR−0.86DownTumor suppressor genesOnco-miRNA−16.0−17.0−1.0
hsa-miR-942-3prs115372145chr1:117094703C/TSOS2−0.37DownTumor suppressor genesOnco-miRNA−18.3−18.30.0
hsa-miR-4457rs115769169chr5:1309319C/TAPC−0.22DownTumor suppressor genesOnco-miRNA−16.7−16.10.6
hsa-miR-3130-3prs115772313chr2:206783285G/AE2F30.25UpOncogenesTumor suppressor miRNA−17.3−17.30.0
hsa-miR-4514rs116034786chr15:80997457A/GESR10.95UpOncogenesTumor suppressor miRNA−22.1−20.41.7
hsa-miR-196a-3prs11614913chr12:53991815C/TJAG1−0.21DownTumor suppressor genesOnco-miRNA−19.9−18.91.0
hsa-miR-548at-5prs11651671chr17:42494785G/APGR−0.86DownTumor suppressor genesOnco-miRNA−15.3−13.81.5
hsa-miR-3192-3prs11907020chr20:18470681T/CNCOA3−0.01DownTumor suppressor genesOnco-miRNA−14.0−14.00.0
hsa-miR-5700rs12314280chr12:94561809T/CAXIN2−0.51DownTumor suppressor genesOnco-miRNA−12.9−14.4−1.5
hsa-miR-4433a-5prs12473206chr2:64340782C/GKRAS0.31UpOncogenesTumor suppressor miRNA−17.0−16.60.4
hsa-miR-6077rs1280409926chr1:148388307C/TSP1−0.29DownTumor suppressor genesOnco-miRNA−21.7−21.9−0.2
hsa-miR-548 lrs13447640chr11:94466555G/ASP1−0.29DownTumor suppressor genesOnco-miRNA−14.5−15.9−1.5
hsa-miR-548ar-3prs141659366chr13:114244551G/AFZD4−1.31DownTumor suppressor genesOnco-miRNA−14.4−12.91.5
hsa-miR-4444rs142357696chr3:75214531A/GE2F11.18UpOncogenesTumor suppressor miRNA−24.7−22.12.6
hsa-miR-888-5prs143634721chrX:145994837C/APGR−0.86DownTumor suppressor genesOnco-miRNA−16.2−15.30.9
hsa-miR-892brs146806052chrX:145997215A/GDLL1−0.64DownTumor suppressor genesOnco-miRNA−21.0−24.2−3.2
hsa-miR-8060rs1514422chr3:96360020G/AKRAS0.31UpOncogenesTumor suppressor miRNA−18.3−18.5−0.2
hsa-miR-6887-5prs1688017chr19:35122719G/APIK3R30.33UpOncogenesTumor suppressor miRNA−28.7−25.23.5
hsa-miR-1304-3prs2155248chr11:93733700G/TNOTCH2−0.60DownTumor suppressor genesOnco-miRNA−18.3−18.6−0.3
hsa-miR-3130-5prs2241347chr2:206783257C/TTCF7−0.47DownTumor suppressor genesOnco-miRNA−22.3−22.6−0.4
hsa-miR-1343-5prs2986407chr11:34941869T/CCSNK1A10.13UpOncogenesTumor suppressor miRNA−21.7−20.90.8
hsa-miR-5087rs2992458chr1:148334528A/GGSK3B0.14UpOncogenesTumor suppressor miRNA−17.2−16.21.0
hsa-miR-5189-3prs35613341chr16:88468999C/GJAG1−0.21DownTumor suppressor genesOnco-miRNA−25.6−28.3−2.7
hsa-miR-8084rs404337chr8:93029770G/ANOTCH1−0.28DownTumor suppressor genesOnco-miRNA−12.7−13.4−0.7
hsa-miR-548ap-5prs4414449chr15:85825667G/ACDK6−0.67DownTumor suppressor genesOnco-miRNA−14.0−13.00.9
hsa-miR-608rs4919510chr10:100975021C/GCDKN1A−0.36DownTumor suppressor genesOnco-miRNA−30.7−31.2−0.5
hsa-miR-7157-3prs56148568chr2:140586631T/CEGFR−1.44DownTumor suppressor genesOnco-miRNA−19.1−19.10.0
hsa-miR-6744-3prs56310773chr11:1256664C/TPIK3R1−0.81DownTumor suppressor genesOnco-miRNA−32.5−30.02.5
hsa-miR-6805-3prs56312243chr19:55388234C/TMAPK1−0.09DownTumor suppressor genesOnco-miRNA−19.5−19.7−0.3
hsa-miR-6071rs56790095chr2:85783659C/GWNT30.34UpOncogenesTumor suppressor miRNA−17.0−17.00.0
hsa-miR-548abrs59323834chr3:103524093C/TCDK6−0.67DownTumor suppressor genesOnco-miRNA−14.6−12.12.5
hsa-miR-3928-5prs5997893chr22:31160117A/GESR10.95UpOncogenesTumor suppressor miRNA−17.7−15.62.1
hsa-miR-596rs61388742chr8:1817259T/CPTEN−0.26DownTumor suppressor genesOnco-miRNA−19.7−17.52.2
hsa-miR-3922-5prs61938575chr12:104591665G/ASHC10.21UpOncogenesTumor suppressor miRNA−28.6−27.31.4
hsa-miR-4772-5prs62154973chr2:102432320C/TFOS−1.17DownTumor suppressor genesOnco-miRNA−20.3−20.30.0
hsa-miR-646rs6513497chr20:60308547T/GPIK3R1−0.81DownTumor suppressor genesOnco-miRNA−23.4−20.92.6
hsa-miR-8063rs7162033chr15:36972836C/GIGF1−1.13DownTumor suppressor genesOnco-miRNA−17.6−15.91.7
hsa-miR-8063rs7183051chr15:36972838G/AIGF1−1.13DownTumor suppressor genesOnco-miRNA−17.6−16.90.7
hsa-miR-6868-3prs7208391chr17:76098024C/GAXIN2−0.51DownTumor suppressor genesOnco-miRNA−18.8−18.60.2
hsa-miR-4679rs72810954chr10:89063382G/AWNT160.12UpOncogenesTumor suppressor miRNA−17.0−17.00.0
hsa-miR-4799-5prs72955519chr4:147782619G/AMAPK1−0.09DownTumor suppressor genesOnco-miRNA−18.8−16.32.5
hsa-miR-4999-5prs72996752chr19:8389352A/GCTNNB1−0.40DownTumor suppressor genesOnco-miRNA−18.5−17.41.1
hsa-miR-624-3prs73251987chr14:31014677C/GGSK3B0.14UpOncogenesTumor suppressor miRNA−15.5−13.71.9
hsa-miR-4727-5prs73295187chr17:38825855A/CCDK6−0.67DownTumor suppressor genesOnco-miRNA−21.4−19.61.8
hsa-miR-4739rs73410309chr17:79707227G/CBAK10.49UpOncogenesTumor suppressor miRNA−32.5−31.60.9
hsa-miR-6504-5prs74469188chr16:81611365T/CFZD5−0.56DownTumor suppressor genesOnco-miRNA−21.9−21.50.4
hsa-miR-323b-5prs75330474chr14:101056252C/TPIK3R30.33UpOncogenesTumor suppressor miRNA−18.5−16.52.0
hsa-miR-6841-3prs76347846chr8:24953808A/GGADD45A−0.30DownTumor suppressor genesOnco-miRNA−19.1−15.23.9
hsa-miR-4704-3prs76595065chr13:66218307T/CGSK3B0.14UpOncogenesTumor suppressor miRNA−23.2−23.4−0.2
hsa-miR-6839-5prs7804972chr7:64679085G/ACDK6−0.67DownTumor suppressor genesOnco-miRNA−19.4−18.11.3
hsa-miR-6885-5prs78293125chr19:6389688A/GTCF7L2−0.68DownTumor suppressor genesOnco-miRNA−24.4−24.40.0
hsa-miR-4520-3prs8078913chr17:6655449C/GGRB20.35UpOncogenesTumor suppressor miRNA−24.2−21.23.0
hsa-miR-548 h-5prs9913045chr17:13543607G/ACDK6−0.67DownTumor suppressor genesOnco-miRNA−13.8−13.80.0

Table 3.

Prediction of the effect of 63 out-seed SNPs in 62 miRNAs on the degree of miRNA role in BC.

Ref = The allele in the reference genome. Alt = any other allele found at that locus.

Log2FC = Log 2 fold change with P < 0.05.

MFE: Minimum free energy between miRNA and 3’UTR (kcal/mol).

Mean MFE: the mean value of MFEs from all miRNA binding sites on 3’UTR.

ΔG: The difference of mean MFE between alterative allele and reference allele.

3.4 Predicting the interaction between miRNAs and targets

Among the 69 miRNAs, the interaction of 12 miRNAs with their targets did not differ under the allele variation of the SNPs (ΔG = 0) (Table 3), indicating 12 SNPs in these 12 miRNAs are not likely to be associated with BC risk. The remaining 57 miRNAs have different levels of interaction with their targets under the allele variation of the SNPs (ΔG ≠ 0) (Tables 2 and 3). It suggested that 58 SNPs in these miRNAs are potentially associated with BC risk (Tables 2 and 3).

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4. Discussion

In knowledge and improvements in bioinformatics, computational predictions of causative factors are being used as a supplementary approach to support the practical assessment of multifactorial disorders. Although miRNA can modulate up to 92% of mammalian genes, only a few target pairings of miRNAs have been experimentally verified [39]. Numerous technical challenges, such as tissue selectivity, poor expression, 3’ UTR selection, and miRNA preservation, make existing methodologies difficult for experimental confirmation of interactions between miRNAs and their mRNA targets [40]. Recognizing functional SNPs in genes and examining their impacts on phenotypes may allow for more in-depth knowledge of the possible consequences of making such changes. Biogenesis, expression level, and biological function are all influenced by SNPs in human miRNA genes. For identifying the possible impacts of SNPs, several useful bioinformatics tools have been developed. All mature microRNAs with SNPs implicated in BC and their target genes were obtained. In addition, bioinformatics strategies for predicting these functional SNPs were presented. All steps and results are summarized in Figure 1.

Figure 1.

The methodology and summary results of study.

The results showed that 12 SNPs did not appear to be associated with BC risk. Thirty-one in-seed SNPs are likely to be strongly associated with disease risk by altering miRNA function in BC (Table 1). 58 SNPs appear to be moderately or weakly associated with BC risk by altering the degree of interaction between miRNAs and their targets (Tables 2 and 3). According to the most significant difference between Mean MFE values in the hsa-miR-4731-3p, the effect of T/A replacement can significantly increase the change of interaction (ΔG = −5); thus, it increases the effect of SNP rs66507245 on the risk of BC (Table 2). It seems that the association of rs66507245 with BC risk was moderate among the group of 70 SNPs predicted based on the ΔG value. The most negligible difference occurs in hsa-miR-6077, hsa-miR-8060, hsa-miR-4704-3p, and hsa-miR-6868-3p, indicating the effect of the SNPs on BC risk were weak (Table 3).

These SNPs were not found in GWASs since GWAS is a whole-genome sequencing approach that does not determine SNPs in non-coding regions. Only four SNPs (rs3746444, rs2910164, rs11614913, and rs4919510) have been investigated for the association with BC risk in case–control studies. The presence of the C allele of rs2910164, an in-seed SNP, in hsa-miR-146a-3p was associated with an increased risk of BC (C vs. G: OR = 1.4, 95% CI = 1.03–1.85, p = 0.03) [41]. Allele C of rs11614913 in hsa-miR-196a-3p was found to be significantly associated with decreased risk of BC (C vs. T: OR = 0.64, 95%CI = 0.49–0.85, p = 0.0019) [42]. Allele G of rs3746444 in seed region of hsa-miR-499a-3p significantly increased BC susceptibility only among Asians (G vs. A: OR = 1.12, 95% CI = 1.00–1.26, P = 0.04) [43]. The G allele rs4919510 in hsa-miR-608 decreased the risk of BC (G vs. C: OR = 0.53, 95%CI 0.30–0.92, p = 0.024) [44]. The remaining 97 SNPs have not been identified for an association with BC risk in any association studies. However, 7 of these (rs7210937, rs12402181, rs2273626, rs2620381, rs35770269, rs10061133, and rs6513497) are associated with the risk of other cancers, including oral and pharyngeal squamous cell carcinoma [45], acute lymphoblastic leukemia [46], leukopenia [47], gastric cancer [48], colon cancer [49], esophageal squamous cell carcinoma [50], and hepatocellular carcinoma [51]. These shreds of evidence suggest that candidate SNPs in this study can be associated with risk in BC and other types of cancer.

At the level of investigating the role of miRNAs in BC, 31 miRNAs and 69 miRNAs were predicted to change and remain unchanged function in the presence of SNP allele changes, respectively. Among them, 7 miRNAs (miR-4513, miR-501-3p, miR-580-5p, miR-3130-3p, miR-196a-3p, miR-8084, and miR-3922-5p) had predicted roles in BC consistent with the results of previous functional studies. Hsa-miR-4513, has-miR-196a-3p, and has-miR-8084 suggested functions as an oncogene in the progression of BC [52, 53, 54] were also predicted as onco-miRNAs in this study (Tables 1 and 3). Hsa-miR-501-3p, has-miR-580-5p, has-miR-3130-3p, and has-miR-3922-5p indicated tumor suppressor function [55, 56, 57, 58] were also predicted as tumor suppressor miRNAs (Tables 1 and 3).

Our work gives vital insights into the pathophysiology and progression of BC by presenting essential information on the likely influence of SNPs and distinct regulation patterns on miRNA production and function. Consequently, genetic variations appear to be the appropriate criterion for early diagnosis of BCs in future.

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5. Conclusion

In brief, numerous resources were applied following a comprehensive screening of mature miRNAs, including SNPs that play a deciding role on BC to identify SNP’s functional effect in the miRNA gene. We conducted a thorough investigation into the influence of the mined SNPs on miRNA function, including target prediction, miRNA-target interaction, and target expression level. Theoretically, this work revealed a list of possible causative SNPs in the mature miRNA gene that may be addressed for further practical research in BC management.

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Acknowledgments

This research was funded by Vietnam National University, Ho Chi Minh City (VNU-HCM), under grant number 562-2020-2118-02.

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Conflict of interest

The authors declare no potential conflicts of interest.

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Credit authorship contribution statement

Thanh Thi Ngoc Nguyen was involved in conceptualization, methodology, formal analysis, data curation, software, writing—original draft, writing—editing, visualization, project administration, and funding acquisition. Thu Huynh Ngoc Nguyen was involved in investigation, validation, and writing—review. Luan Huu Huynh and Hoang Ngo Phan were involved in investigation and writing—review. Hue Thi Nguyen was involved in resources, conceptualization, methodology, supervision, writing—review, project administration, and funding acquisition.

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Written By

Thanh Thi Ngoc Nguyen, Thu Huynh Ngoc Nguyen, Luan Huu Huynh, Hoang Ngo Phan and Hue Thi Nguyen

Submitted: 14 May 2022 Reviewed: 23 May 2022 Published: 18 June 2022

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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