Abstract
Background
The high mortality rate of breast cancer and the difficulties
associated with therapeutic resistance, especially in cases where
targeted treatments are unavailable, make it a serious threat to
women's health. This study examines the relationship between three
mature microRNAs (miRNAs) that are clustered together, namely miR- 23a,
miR- 27a, and miR- 24–2, as well as their potential correlation with
breast cancer.
Methods
We identified common gene targets of miR- 23a, miR- 27a, and miR- 24–2
using computational analysis. We also checked for the levels of miR-
23a, miR- 27a, and miR- 24–2 in 26 breast tumor tissues (with their
matched control) as well as MCF7 and MDA-MB- 231 cell lines using
qRT-PCR. Dual-luciferase reporter assay was conducted to validate the
binding site of the microRNAs in their target gene. Western blot was
performed to study the expression of various breast cancer related
genes in the presence of the three microRNAs. In addition, the effect
of microRNAs in cancer cell metastasis and cell division was carried
out using invasion and cell cycle assay.
Results
Computational analysis identified key genes, including GSK3β, NCOA1 and
SP1, which are functionally linked to tumor progression and various
other malignancies. All three microRNAs were found to be significantly
downregulated in the breast cancer tissue samples in comparison to
their respective controls. Kaplan–Meier plot analysis revealed that the
expression levels of these genes and associated microRNAs correlates
with breast cancer patient survival rates. Reduced SP1 and NCOA1 levels
predicted a worse prognosis, but elevated levels of GSK3β were linked
with decreased survival. Moreover, miR- 23a and miR- 24–2 specifically
target GSK3β, potentially disrupting the Wnt/β-catenin pathway involved
in breast cancer development. Functional tests showed that miR- 23a,
miR- 27a and miR- 24–2 affect expression of EMT related genes,
influencing cell invasion and migration, impacting ERK signaling and
EMT, critical in the spread of breast cancer.
Conclusion
This study unlocks the potential of targeting the microRNA cluster as a
therapeutic approach and emphasizes the complex regulatory roles of
each individual members of the miR- 23a/27a/24–2 cluster in the
pathogenesis of breast cancer.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12885-025-14045-8.
Keywords: Breast cancer, MicroRNA, Migration, Invasion, GSK3β, SP1
Background
Breast cancer is one of the most frequently diagnosed cancers among
women globally, with a complex and heterogeneous nature characterized
by diverse cell compositions, proliferation rate and responses to
treatments, resulting in varying patient outcomes [[42]1, [43]2]. This
diversity is evident in the different molecular subtypes, defined by
the expression of specific proteins such as estrogen (ER) and
progesterone (PR) receptors, human epidermal growth factor receptor 2
(HER2) and the proliferation marker, Ki67 [[44]3, [45]4]. Triple
Negative Breast Cancer (TNBC) which lacks the expression of ER, PR and
HER2 receptors accounts for nearly 15–20% of newly diagnosed breast
cancer cases and poses a significant challenge due to absence of
targeted treatments, contributing substantially to breast
cancer-related mortality [[46]5].
MicroRNAs are short, non-coding RNA sequences typically spanning around
18 to 25 nucleotides [[47]6]. They play a crucial role in regulating
gene expression by binding to complementary sequences in the 3’
untranslated region (UTR) of target mRNAs, leading to either inhibition
of translation or degradation of the mRNAs [[48]6, [49]7]. MiRNAs have
a substantial role in controlling several physiological processes, such
as cell division, proliferation, and apoptosis [[50]8]. Evidence
suggest that distinct types of human cancers exhibit unique miRNA
expression patterns, making them useful biomarkers for cancer
diagnosis, progression, and prognosis [[51]9].
Our group's earlier research has demonstrated that aberrant gene
expression of the miR- 23a, miR- 27a and miR- 24–2 in HEK293 cells can
induce apoptosis [[52]10]. Moreover, ER stress is identified by
microarray analysis as the primary initiator of the intrinsic pathway
of apoptosis [[53]11]. The miR- 23a/27a/24–2 cluster encodes three
mature miRNAs derived from a single transcript, which display varying
expression levels across multiple disease states [[54]11]. Further,
these three miRNAs' dysregulated expression has been linked to several
human malignancies [[55]11]. In the current study our findings suggest
the roles and relationships of individual members of miR- 23a/27a/24–2
cluster in breast cancer pathogenesis, focusing on their effects on
common target genes (NCOA1, GSK3β, and SP1), along with their influence
on epithelial-mesenchymal transition (EMT), cancer cell invasion and
cell cycle. The study seeks to explore the potential of targeting these
miRNAs for therapeutic interventions in breast cancer.
Materials and methods
In-silico prediction of microRNAs
TargetScan 8.0 ([56]www.targetscan.com) was used to predict the targets
of hsa-miR- 23a- 3p, hsa-miR- 24- 3p, and hsa-miR- 27a- 3p. Highly
conserved targets of these three miRNAs were taken into consideration
and Venny 2.1.0 was used to find the common genes between them
([57]https://bioinfogp.cnb.csic.es/tools/venny/). Pathway enrichment
analysis of these common target genes was done using the DAVID pathway
analysis tool ([58]https://david.ncifcrf.gov/tools.jsp; v2023q3)
[[59]12, [60]13] The list of highly conserved predicted target genes of
hsa-miR- 23a- 3p, hsa-miR- 24- 3p, and hsa-miR- 27a- 3p is provided in
Supplementary Table 1.
Construction of the protein–protein interaction (PPI) network
To study the interaction of the common target genes with other genes,
we used STRING 12.0 ([61]https://string-db.org/). The protein–protein
interactions score was set to a medium confidence of 0.400 (default
setting) and were searched against the organism Homo sapiens.
Plasmid constructs
miRBase was used to retrieve the miRNA sequences
([62]http://microrna.sanger.ac.uk/sequences/). Primers were designed
using Primer 3 software ([63]http://frodo.wi.mit.edu). Hsa-miR- 23a,
hsa-miR- 24-2, and hsa-miR- 27a were cloned in pSilencer 4.1 vector
(Ambion, Austin, TX, USA) and now onwards designated as miR- 23a, miR-
24-2 and miR- 27a. ENSEMBL was used to retrieve the 3’ UTR of the GSK3β
sequence ([64]www.ensembl.org/) and human genomic DNA was used to
amplify it. Two different regions encompassing the binding sites for
miR- 23a, miR- 24-2, and miR- 27a were amplified and cloned in
psiCHECK2 reporter vector backbone (Promega, Madison, WI, USA) between
Not1 and Xho1. The clones were designated as GSK3β 3’UTR 1 (containing
seed sequence of miR- 23 and miR- 27, 840 bp) and GSK3β 3’UTR 2
(containing the seed sequence of miR- 24-2, 141 bp). The accuracy of
the all the clones was confirmed by sequencing. Primers used are listed
in Table [65]1.
Table 1.
Primer sequences used in this study
Gene name Primer type Primer sequence
hsa-miR- 23 - 3p Stem loop
5′-CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGAATGTGAT- 3′
Forward primer 5′-ACACTCCAGCTGGGATCACATTGCCAGGG- 3′
hsa-miR- 24 - 3p Stem loop
5′-CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGCTGAGCCA- 3′
Forward primer 5′-ACACTCCAGCTGGGTGGCTCAGTTCAGCAG- 3′
hsa-miR- 27 - 3p Stem loop
5′-CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGACTGTGAA- 3′
Forward primer 5′-ACACTCCAGCTGGGTTCACAGTGGCTAAG- 3′
miR- 23 - 3p, miR- 24 - 3p, miR- 27 - 3p Universal reverse primer
5′-GTGTCGTGGAGTCGGCAATTC- 3′
hsa-sno- 234 Stem loop 5'-CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAG- 3’
Forward primer 5′-GGCTTTTGGAACTGAATCTAAGT- 3′
Reverse primer 5′-GAGGTATTCGCACCAGAGGA- 3′
U6 Forward primer 5’-CTCGCTTCGGCAGCACA- 3’
Reverse primer 5’-AACGCTTCACGA ATTTGC GT- 3’
GSK3β 3’UTR 1 Forward primer 5’-CCGCTCGAGTTGCCTTTGGCATGTTGGTG- 3′
Reverse primer 5’-ATAAGAATGCGGCCGCTCTTAACTGGGTGTGGGGGA- 3′
GSK3β 3’UTR 2 Forward primer 5’-CCGCTCGAGACACTTTTCTCCCCTGTGTGG- 3′
Reverse primer 5’-ATAAGAATGCGGCCGCGCCTCTCAAAGGTAGATCTCAGT- 3′
[66]Open in a new tab
Cell culture and transfections
Human breast adenocarcinoma cell lines, MCF- 7 and MDA-MB- 231 were
procured from the National Centre for Cell Science (NCCS), Pune, India.
Cells were cultured using DMEM-high glucose media (D7777, MERCK,
Darmstadt, Germany) supplemented with 10% fetal calf serum, 100
units/ml penicillin and 100 µg/ml streptomycin, and 0.25 μg/ml
amphotericin (Gibco, Thermo Fisher Scientific, MA, USA) and grown in a
humidified chamber supplemented with 5% CO[2] at 37° C. For
overexpression studies, cells were transfected with Empty pSilencer
vector 4.1 (pSil) or miR- 23a or miR- 24-2 or miR- 27a using
Lipofectamine 3000™ (Invitrogen, CA, USA) reagent as recommended by the
manufacturers. Anti-miR- 23 - 3p, anti- miR- 24 - 3p, and anti-miR- 27
- 3p were used at 100 nM (Ambion, TX, USA) wherever indicated and
anti-miR inhibitor negative control (NC) (AM17010, Ambion) was used as
a control. The cells were harvested 24 h post transfection and used for
all experiments as described in our previous lab publication [[67]10].
Collection of primary tissues and patient characteristics
In our study primary- paired tissues from 26 breast cancer patients
were obtained after surgical resection from the Surgery Department at
All India Institute of Medical Sciences, Delhi, India. The paired
adjacent tissue was at least 2 cm away from the tumor. The research
complies with all the relevant national regulations, institutional
policies and is in accordance with the tenets of the Helsinki
Declaration and has been approved by the Institutional Ethical
Committee of CSIR-IGIB, Delhi and AIIMS, New Delhi (IEC-
726/01.10.2021, RP- 67/2022). A written and signed informed consent
form was obtained from all the participants.
Bioinformatics based assessment of prognostic significance of genes
Correlation of genes with their expression and overall-survival rates
in breast cancer patients was assessed by Kaplan–Meier plots generated
through Kaplan–Meier plotter ([68]www.kmplot.com) [[69]14]. METABRIC
database was used over TCGA for its longer follow-up duration and
better cohort. GSK3β [Affymetryx ID 209945_s_at], SP1 [Affymetryx ID
1553685_s_at], and NCOA1 [Affymetryx ID 209105_at] were used to
generate Kaplan–Meier plots for genes as well as for microRNA hsa-miR-
23a, hsa-miR- 24 and hsa-miR- 27. We excluded biased arrays from our
analysis.
Luciferase assay
MCF- 7 cells were seeded in 24-well plate at a density of 30,000 cells
per well and co transfected with GSK3β 3’UTRs plasmid (150 ng) and
either with 400 ng of miR- 23a or miR- 24–2 or miR- 27a or 40 nm of
anti-miR- 23 - 3p, anti- miR- 24 - 3p, and anti-miR- 27 - 3p along with
their respective controls using Lipofectamine 3000 (Invitrogen,CA,
USA). Cells were lysed to measure the activity of firefly and renilla
luciferase using the dual-luciferase reporter assay (Promega, MA, USA)
according to the manufacturer’s instructions. Renilla reporter was used
to normalize the firefly luciferase activity. These experiments were
performed in triplicates.
Total RNA isolation and miRNA quantification
RNA was isolated from cells (MCF- 7 and MDA-MB- 231) and primary breast
cancer tissues using Trizol reagent (Invitrogen, CA, USA) and miReasy
Tissue/cells Advanced micro kit (Qiagen, Hilden, Germany) as per
manufacturer’s protocol respectively. Reverse transcription was carried
out with 500 ng of RNA using M-MuLV reverse transcriptase High-capacity
cDNA reverse transcription kit (Applied Biosciences, USA) as per the
instructions provided by the manufacturer. miRNA quantification was
done using stem-loop primers as mentioned in Table [70]1. Sno- 234
expression was used for normalization and U6 primers were used for
breast cancer patient tissue samples. Results were analysed using
Pfaffl's method [[71]15].
Western blot
Protein isolation was done using RIPA lysis buffer supplemented with
protease inhibitor cocktail mix (Merck, Darmstadt, Germany). Protein
estimation was done by BCA (Sigma, USA). 40–60 ug protein was subjected
to electrophoresis using 12% SDA-PAGE at 100 V for 3 h and transferred
onto PVDF membrane (MDA, Advanced Microdevices, India). Blots were
probed with primary antibodies overnight at 4 °C and were developed
using enhanced chemiluminescence assays (ECL detection system; G
Biosciences, USA). The integrated densitometric values (IDV) were
measured using Alphaimager software (Alpha Innotech Corporation, CA,
USA). GSK3β (D5 C57), β-catenin (D2U84), NCOA1 (ab1093281), claudin- 1
(ab101430), Keratin8/18 (4546S), Fibronectin (E5H6X) and GAPDH (D1GH11)
antibodies were purchased from Cell Signalling Tech (Cell Signalling
Tech, Danver, MA, USA). SP1 (ab308364) and cyclin D1 (ab101430)
antibody were procured from Abcam (Abcam, MA, USA). E- cadherin (sc-
7870), pERK (sc7383), total ERK (sc- 514302) and c-myc (sc- 764) was
from Santa Cruz Biotechnology (Santa Cruz Biotechnology, CA, USA).
GAPDH was used as house-keeping gene control.
Migration and invasion assay
BioCoat™ matrigel invasion chambers (BD Biosciences, NJ, USA) were used
to assess the invasive capacity of cells after miRNA or AM
transfections. Briefly, invasion chambers were kept at room temperature
and hydrated with serum-free media before adding the cells. 24 h post
treatment with miR- 23a, miR- 24–2, and miR- 27a and their respective
Anti-miRs, cells were trypsinized, counted and suspended in an
incomplete (serum-free) medium. Approximately 40,000 cells per well of
a 12 well plate were seeded in the upper chamber and were allowed to
invade for 24 h. After completion of the incubation period, non-invaded
cells were wiped off using cotton bud and invaded cells were stained
with crystal violet and images were taken as described previously.
Migration and invasion of cells was analysed through colorimetric assay
by dissolving the membrane in 10% acetic acid and absorbance was taken
at 570 nm. Fold change was calculated for same by dividing absorbance
of samples by absorbance of controls for both the cell lines.
Cell cycle analysis
For cell cycle distribution analysis, cells were initially fixed for
2 h using ice-cold 70% ethanol. Subsequently, they were treated with
RNase A and labelled with propidium iodide (Sigma, USA) for 1 h in
darkness. The data was acquired through a flow cytometer (BD FACSAria™
III, USA) with a minimum of 10,000 events counted.
Statistical analysis
Statistical analysis of the results was performed using unpaired
student's t-test using GraphPad Prism Software (v.10.2.2 GraphPad,
Inc., CA, USA). We performed an independent student’s two-tailed,
paired t test for the analysis of qRT-PCR of breast cancer patients
along with their adjacent control and for cell cycle analysis. Unpaired
t test was used for the analysis of western blots and qRT-PCR of miRNAs
in MCF- 7 and MDA-MB- 231 cells. Mean of n = 3 ± SEM was plotted.
P-value ≤ 0.05, < 0.01, < 0.001 and < 0.0001 corresponds to *, ** and
***, ****respectively, on plots.
Results
Target prediction and pathway enrichment analysis of miR- 23a, miR- 24-2 and
miR- 27a target genes
Derived from gene duplication within the human genome, the miR-23–27-24
cluster has 2 paralogs: miR- 23a cluster (miR- 23a/27a/24–2) situated
on chromosome 9q22 and miR- 23b cluster (miR- 23b/27b/24–1) located on
chromosome 19p13 [[72]11, [73]16]. MiR- 23a cluster codes for miR- 23a,
− 27a, and − 24–2 and all these are known to be involved in various
cellular processes, like proliferation, cell cycle, differentiation and
several human diseases including cancer [[74]11, [75]17].
In the current study, we retrieved highly conserved predicted targets
of the hsa-miR- 23a- 3p (1342 genes), hsa-miR- 24 - 3p (761 genes), and
hsa-miR- 27a- 3p (1421 genes) using target prediction database
TargetScan version 8.0 as shown in Supplementary Table 1. We next used
Venny 2.1.0 tool and found a total of 42 common predicted gene targets
between the three miRNAs (Fig. [76]1A and Table [77]2). Functional
enrichment analysis of 42 common genes was next done using DAVID
software [[78]12, [79]13]. Results of the analysis demonstrated
enrichment of a total of four KEGG pathways, namely, MAPK signaling,
Hedgehog signaling, Cushing syndrome, and Breast cancer as shown in
Fig. [80]1B [[81]18, [82]19]. Breast cancer showed significant
enrichment (p-value = 0.027) with three core genes SP1, GSK3
[MATH: β :MATH]
, and NCOA1. Simultaneously we also checked the protein–protein
interaction network of these 42 common target genes using STRING
version 12.0. The interaction score was set to 0.400 and within the PPI
network GSK3
[MATH: β :MATH]
, SP1, and NCOA1 emerged to have a significant network with APC
(Adenomatous polyposis coli) and CSNK1A1 (Casein kinase 1 alpha) (FDR
= 0.0097) as shown in Fig. [83]1C. APC and CSNK1A1 are important
components of the destruction complex and are known to negatively
regulate canonical Wnt-signaling by phosphorylating β-catenin in the
absence of Wnt ligands, leading to its ubiquitination and ultimate
proteasomal destruction [[84]20]. Abnormal activation of Wnt-signaling
is known to be linked with cell proliferation, evasion of apoptosis,
and metastasis [[85]21, [86]22].
Fig. 1.
[87]Fig. 1
[88]Open in a new tab
Bioinformatic analysis and association of the components of miR-
23a/27a/24–2 cluster in Breast Cancer using string analysis and
Kaplan–Meier survival curve. (A) Venn diagram and list of highly
conserved predicted target genes of miR- 23a, miR- 24–2, and miR- 27a
using TargetScan (B) Bubble plot represents the pathway enrichment
analysis of common predicted target genes using DAVID tool. Gene count
is represented by the size of the bubbles and colour gradation
represents the negative log10 of p-value. (C) PPI network of common
predicted target genes was constructed using STRING database (D)
Kaplan–Meier curves of GSK3β, SP1 and NCOA1 showing survival
probabilities of the two contrasting groups in breast cancer
Table 2.
Forty-two common gene targets of individual members of miR-
23a/24–2/27a cluster and their function in cancer
Gene symbols Gene name Function in cancer Reference
NEK6 NIMA related kinase 6 Regulates cancer cell invasion and
metastasis [[89]23]
NR6A1 Nuclear Receptor Subfamily 6, Group A, Member 1 Overexpressed in
prostate cancer cases [[90]24]
ZNF395 Zinc finger protein 395 It promotes progression of cancer
[[91]25]
SOCS6 Suppressor of cytokine signalling 6 It is downregulated in
various carcinomas [[92]26]
EBF3 EBF Transcription factor 3 They regulate developmental pathways
including B cell differentiation, and development of bone [[93]27]
MEIS2 Meis Homeobox 2 It is involved in ontogeny and differentiation as
well as organ development [[94]28]
DCUN1D5 Defective In Cullin Neddylation 1 Domain Containing 5 Play a
role in DNA damage response to genotoxic stress [[95]29]
PLEKHH2 Pleckstrin Homology, MyTH4 and FERM Domain Containing H2
Promotes the malignancy of non-small cell lung cancer [[96]30]
HIC1 HIC ZBTB Transcriptional Repressor 1 It is a tumor suppressor
[[97]31]
NCOA1 Nuclear Receptor Coactivator 1 Promotes metastasis of breast
cancer [[98]32]
CELF2 CUGBP, Elav-like family member 2 It negatively regulates breast
cancer cell invasion [[99]33]
NFIB Nuclear Factor I/B Promotes angiogenesis at the site of metastasis
[[100]34]
RAP1B Ras associated protein 1 Promotes invasiveness and tumorigenicity
of breast cancer [[101]35]
C17orf85 Nuclear Cap Binding protein 3 Also called NCPB3, it is an mRNA
protein [[102]36]
REPS2 RALBP1 associated Eps domain containing 2 Dysregulated during
prostate cancer progression [[103]37]
GSK3β Glycogen Synthase Kinase 3 Beta It negatively regulates breast
cancer tumorigenicity [[104]37]
SMAD5 SMAD family member 5 It induces chemoresistance in breast cancer
[[105]38]
CBX5 Chromobox 5 It functions as an oncogene [[106]39]
UBN2 Ubinuclein 2 It promotes tumor progression in colorectal cancer
[[107]40]
KPNA3 Karyopherin subunit alpha 3 It positively regulates EMT in triple
negative breast cancer [[108]41]
CSNK1G1 casein kinase 1 gamma 1 It is overexpressed in triple negative
breast cancer [[109]42]
MBD5 methyl-CpG-binding domain (MBD) 5 It promotes tumor cell growth
[[110]43]
STRN Striatin It forms signalling hubs with different kinases and
phosphatases and other signalling proteins called STRIPAK [[111]44]
CLCN3 Chloride Channel 3 It prevents proliferation of ovarian cancer
cells [[112]45]
RSBN1L Round Spermatid Basic Protein 1 Like Its overexpression is
involved with better prognosis of breast cancer [[113]46]
PLAG1 Pleomorphic adenoma gene 1 It promotes cell proliferation in
various cancers [[114]47]
DTNA Dystrobrevin Alpha It is involved in cardiovascular malignancies
[[115]47]
ADAM19 A disintegrin and metalloproteinase 19 It is overexpressed in
breast invasive carcinoma [[116]48]
SHROOM2 Shroom family member 2 It plays a role in EMT and cancer cell
metastasis inhibition [[117]49]
LIMK2 LIM domain Kinase 2 It is overexpressed in triple negative breast
cancer [[118]50]
MIDN Midnolin Involved in kinase binding and plays a role in
Parkinson’s Disease [[119]51]
TAOK1 Thousand and one amino acid kinase 1 It promotes proliferation
and invasion of non-small cell lung cancer [[120]52]
PDE3A Phosphodiesterase 3 A It is upregulated in breast cancer
[[121]53]
WDFY2 WD repeat and FYVE domain-containing protein 2 It is a tumor
suppressor gene [[122]54]
NLK Nemo like kinase It is involved in cancer cell cycle progression
and tumorigenesis [[123]55]
IPMK Inositol Polyphosphate Multikinase It has tumor -suppressive role
[[124]56]
SP1 Specificity Protein 1 Overexpressed in numerous cancer [[125]57]
NRP2 Neuropilin 2 Associated with poor prognosis in breast cancer
[[126]58]
PURA Purine Rich Element Binding Protein A Overexpressed in tumor
[[127]59]
BNIP3L BCL2 Interacting Protein 3 Like Upregulated expression in breast
cancer [[128]60]
UBE2K Ubiquitin Conjugating Enzyme E2 K It promotes tumor malignancies
[[129]61]
WISP1 WNT1 Inducible Signaling Pathway Protein 1 It positively
regulates cancer cell progression and metastasis [[130]62]
[131]Open in a new tab
In a step forward to understanding the diagnostic significance of the
core target genes in breast cancer patients we plotted the Kaplan Meier
(KM) survival curve for GSK3
[MATH: β :MATH]
, SP1, and NCOA1 respectively using KMplotter. Glycogen synthase kinase
3 beta (GSK3β) is known to play a role in invasion, migration,
survival, proliferation, and chemo resistance [[132]22]. Suppression of
GSK3β reduces the chemoresistance, invasive, and stemness properties of
cancer cells [[133]63, [134]64]. As shown in Fig. [135]1D, the
Kaplan–Meier curves depict poor relapse-free survival with high
expression of GSK3β (p = 0.017). In some cases GSK3β is associated with
cancer progression by reducing autophagic flux, whereas in other cases
it is known to harbor an antiproliferative effect by inhibiting
β-catenin and c-Myc [[136]65]. TNBC patients showed higher expression
of GSK3β which is correlated with poor prognosis of TNBC patients
[[137]66].
SP1 (Specificity protein 1) belongs to the SP transcription factor
family which regulates the transcription of housekeeping genes by
recruiting the transcription machinery in the absence of TATA box
[[138]67]. The survival analysis curve for SP1 shows that low
expression of SP1 is related to worse relapse-free survival of breast
cancer patients (Fig. [139]1D).
The dysregulation of nuclear receptor coactivator 1 (NCOA1) has also
been found in various cancer types including breast cancer [[140]32,
[141]68]. NCOA1 is overexpressed in breast cancer and is known to
promote breast cancer metastasis [[142]32]. As shown in Fig. [143]1D,
the Kaplan–Meier curves depicted high relapse-free survival with high
expression of NCOA1 (p < 0.0001).
All these findings suggest the importance of GSK3β, SP1 and NCOA1 in
breast cancer.
Differential expression and prognostic significance of the members of miR-
23a/27a/24–2 cluster in breast cancer
The METABRIC database which includes a relative study of gene
expressions in 1262 primary breast cancer cases was used to generate
the Kaplan–Meier plot to study the correlation between miR- 23a, miR-
24 and miR- 27a expression levels and the overall survival rates. As
shown in Fig. [144]2B, high expression of miR- 24 is responsible for
significantly low survival probability of breast cancer patients (p
value < 0.0001) with a cut-off value of 12.53 (expression range:
11–15). On the other hand, high expression levels of miR- 23a and miR-
27a indicated better overall survival of breast cancer patients with a
p-value of 0.054 and 0.073 respectively (Fig. [145]2A, C). The cut-off
value for miR- 23a was 13.39 (expression range: 10–16) and for miR- 27a
was 13.48 (expression range: 11–15).
Fig. 2.
[146]Fig. 2
[147]Open in a new tab
The role of miR- 23a, miR- 24–2 and miR- 27a in breast cancer patients.
Kaplan–Meier curves showing survival probabilities of the two
contrasting groups based on (A) miR- 23a, (B) miR- 24, and (C) miR- 27a
expression. (D) The log2 Fold change expression of miR- 23a/miR-
24–2/miR- 27a of 26 paired primary tumor tissue samples quantified
using qRT-PCR. The log2 fold change was measured as Mean ± SEM. p-value
< 0.05 was considered to statistically significance
We also collected breast tumor tissues along with their adjacent
controls from 26 breast cancer patients. Table [148]3 shows the
clinical and pathological characteristics of breast cancer patients.
Around 46% of the women were below the age of 50 whereas the rest 54%
were above the age of 50. We have classified the patients in TNBC (n =
3) and DPBC (n = 13) groups. Any patients not falling in either of the
two groups were part of a separate group (n = 10). We evaluated the
expression of the mature miRNAs, miR- 23a, miR- 24–2, and miR- 27a in
breast cancer tissue and matched normal control tissues using qRT-PCR.
Comparative analysis of the expression levels of these miRNAs between
breast cancer and matched normal breast tissues demonstrated
significant downregulation in the levels of miR- 23a, miR- 24–2, and
miR- 27a breast cancer tissue in comparison to their adjacent control
(Fig. [149]2D). The log2 fold change values of miR- 23a were (− 1.53 ±
0.69) fold, miR- 24–2 was (− 3.47 ± 0.63) while miR- 27a was (− 2.45 ±
0.90). This provides clues to the tumor-suppressive nature of miR- 23a,
miR- 24–2 and miR- 27a in breast cancer.
Table 3.
Baseline characteristics of breast cancer subjects
Characteristics Number %
Age (years)
≤ 50 12 46
> 50 14 54
Laterality
Left 13 50
Right 13 50
Tumor size
≤ 9 cm^2 13 50
> 9 cm^2 12 46
Tumor grade
1 1 4
2 17 65
3 4 15
4 2 8
ER status
Positive 18 69
Negative 7 27
PR status
Positive 15 58
Negative 10 38
Her2 status
Positive 5 19
Negative 25 96
Lymph node status
N0 17 65
N1 9 35
Therapy
Post chemotherapy 8 31
Therapy naïve 18 69
Diagnosis
Invasive Carcinoma of Breast 20 77
Invasive Ductal Carcinoma 4 15
Invasive Lobular Carcinoma 1 4
Papillary Ductal Carcinoma In-situ 1 4
[150]Open in a new tab
Overexpression of the members of miR- 23a/27a/24–2 provide clues towards
GSK3β as a direct target
To evaluate the impact of individual members of this cluster in breast
cancer, we overexpressed and down-regulated miR- 23a, miR- 24–2, and
miR- 27a using their plasmids (as described in Materials and Methods)
and/antisense mimics (AMs) respectively in MCF- 7 and MDA-MB- 231
breast cancer cell lines. 24 h post transfection with miRNA plasmids
led to an increase in mature microRNA levels in MCF- 7 cell lines as
observed by Quantitative real-time PCR analysis (Fig. [151]3A).
Specifically, miR- 27a was upregulated by 4.29-fold, while miR- 23a and
miR- 24–2 were upregulated by 1.25-fold and 1.26-fold respectively in
MCF- 7 cells. However, we did not find any significant change in the
miR- 23 and miR- 27a levels but a slight decrease in miR- 24–2 levels
were observed after treatment with respective AMs in MCF- 7 cells.
Similarly, in MDA-MB- 231 cells, we observed an overexpression of miR-
23a by 1.31 fold (p-value = 0.04), miR- 24–2 by 1.4 folds and miR- 27a
by 2.27 fold (p-value = 0.02) upon treatment with miRNAs
(Fig. [152]3B). However, upon AM treatment there was decrease in mir-
23a levels by 0.67 folds, no change in the levels of miR- 24–2 and a
slight increase in miR- 27a.
Fig. 3.
[153]Fig. 3
[154]Open in a new tab
Members of miR- 23a/27a/24–2 cluster targets GSK3β. Estimation of miR-
23a, miR- 24–2 and miR- 27a levels upon miR and anti-miR treatments in
(A) MCF- 7 and (B) MDA-MB- 231 breast cancer cells using qRT-PCR. (C)
Predicted binding sites of miR- 23a/miR- 24–2/miR- 27a in the 3’UTR of
GSK3β (D) Dual-luciferase assay to determine the direct binding of miR-
23a/miR- 24–2/miR- 27a in 3’ UTR sequence of GSK3β. Translational
quantification of GSK3β (E) in MCF-7, and (F) MDA-MB- 231 cells and (G)
β-catenin in MCF-7 cells by western blot analysis 24 h post
transfection. GAPDH was used as a loading control. Data is
representative of the mean ± SEM of 3 independent experiments. p-value
< 0.05 was considered to indicate significance
Interestingly our in silico analysis identified highly conserved
binding sites of all three miRNAs in the 3′UTR of GSK3β (miR- 23a- 3p
at position 1001–1008, miR- 24 - 3p at position 4111–4118 and miR- 27a-
3p at position 920–927) (Fig. [155]3C), in 3′UTR of NCOA1 (miR- 23a- 3p
at position 197–204, miR- 24 - 3p at position 441–447 and miR- 27a- 3p
at position 1218–1224) (Supplementary Fig. 1 A) and in 3′UTR of SP1
(miR- 23a- 3p at position 3774–3781, miR- 24 - 3p at position 542–548
and miR- 27a- 3p at position 1663–1669) genes (Supplementary Fig. 1B).
Hence, we constructed two reporter plasmids containing the 3′UTR region
of GSK3β mRNA for miR- 27a and miR- 23a (UTR1), and miR- 24–2 (UTR2) to
perform the dual-luciferase reporter assay. As represented in
Fig. [156]3D, we noted a marked reduction in luciferase activity by
0.58-fold in the case of miR- 23a (p-value = 0.0035), 0.27-fold in the
case of miR- 24–2 (p value = 0.03), and significant upregulation of
1.15-fold in the case of miR- 27a (p value = 0.04) upon miRNA
transfections in MCF- 7 cells. However, upon anti-miR transfection, we
found a significant decrease in luciferase activity by 0.87-fold in
antimiR- 24 (p-value < 0.0001) and 0.50-fold in antimiR- 27 (p-value
= 0.0004). Along with GSK3β, we also performed luciferase assay for the
3’ UTR of NCOA1 but we did not observe any significant change in the
luciferase activity upon miRNA and AM treatment (data not shown).
GSK3β and β-catenin are known to play a critical role in the regulation
of various cellular processes via the Wnt/β-catenin signaling pathway.
Herein, we also evaluated the expression of GSK3β and β-catenin at the
translational level. As shown in Fig. [157]3E the western blot analysis
showed a significant increase in GSK3β protein levels after miR- 23a
(1.19-fold, p-value = 0.002) as well as miR- 24–2(1.14-fold, p-value
= 0.01) overexpression in MCF- 7 cells. However, no change in the GSK3β
protein levels was observed upon miR- 27a overexpression. On anti-miR
treatment, we observed no significant difference in the levels of GSK3β
in the case of anti-miR- 23, anti-miR- 24, and anti-miR- 27 treatment
in MCF- 7 cells. As shown in Fig. [158]3F, we also performed GSK3β
western blot in MDA-MB- 231 cells and observed no significant changes
in the protein levels upon transfection with all three microRNAs. Upon
treatment with anti-miRs, we observed little to no changes in the
protein levels. However a significant decrease by 0.74 folds was
observed after treating the cells with anti-miR- 27 (p-value = 0.009).
On the other hand, β-catenin levels upon overexpression of all three
miRNAs were found to be slightly downregulated but were not
statistically significant (Fig. [159]3G). A significant increase in the
levels of β-catenin protein was observed after anti-miR- 23 and
anti-miR- 24 by 1.22-fold (p-value = 0.04) and 1.39-fold (p-value
= 0.01) respectively in MCF- 7 cells. (Fig. [160]3G). Together these
results show that GSK3β is a direct target of miR- 23a and miR- 24–2.
Cells may activate compensatory mechanisms to maintain GSK3β expression
levels in response to decreased β-catenin levels. These compensatory
mechanisms could involve feedback loops or alternative signaling
pathways that upregulate GSK3β expression to counterbalance the effects
of β-catenin downregulation [[161]69, [162]70]. Further research in
this area is warranted.
Individual members of miR- 23a/24–2/27a cluster downregulates SP1 in breast
cancer cells
SP1 is a transcription factor that controls the transcription of
housekeeping genes, as well as genes involved in the progression of
cell cycle and differentiation [[163]71] and is often found to be
overexpressed in several cancers, including breast cancer [[164]57,
[165]71, [166]72]. NCOA1 is the crucial regulator of steroid hormone
receptor and is linked to the promotion of metastasis [[167]32].
Extracellular signal-related kinase (ERK) plays a major role in cell
proliferation, EMT as well as cell migration, and its elevated
expression is found in various cancers [[168]73]. Keeping this in mind,
we next assessed the levels of SP1, NCOA1, and ERK after transfection
with miRNAs and their respective anti-miRs in MCF- 7 cells. As
illustrated in Fig. [169]4A, the overexpression of miR- 24–2 and miR-
27a led to a decrease in the expression of SP1 by 0.69 and 0.77 folds.
However, there was a decrease in the level of protein level by 0.95 and
0.83 folds upon treatment with anti-miR- 24 (p value = 0.007) and
anti-miR- 27 respectively. On the other hand there was no change in SP1
levels upon miR- 23a (0.94 folds) as well as anti-miR- 23 (0.98 folds)
treatments. NCOA1 did not show any significant alterations upon
treatment with both miRNAs and anti-miRNAs (Fig. [170]4B). On the other
hand, overexpression of all three miRNAs increased the expression of
total ERK by 1.15 folds (miR- 23a), 1.47 folds (miR- 24–2), and 1.35
folds (miR- 27a) respectively (Fig. [171]4C). Conversely, there was no
change in total ERK expression upon treatment with anti-miR- 23 (1.00
fold) and anti-miR- 27 (0.96 folds). However, there was an increase in
its levels by 1.23 folds upon treatment with anti-miR- 24. As shown in
Fig. [172]4D, treatment with miR- 23a, miR- 24–2 and miR- 27a did not
alter pERK expression levels, though miR- 23a showed a statistically
significant p-value of 0.03. In contrast, inhibition of miR- 23a and
miR- 24–2 led to increased pERK levels, while anti-miR- 27 treatment
resulted in a decreased expression. Together, SP1, NCOA1, and ERK
coordinate cellular responses, ensuring proper cell growth,
differentiation, and tissue homeostasis.
Fig. 4.
[173]Fig. 4
[174]Open in a new tab
Regulation of genes in cell proliferation via miR- 23a, miR- 24–2 and
miR- 27a in MCF- 7 cells. Western blot technique was used to quantify
the expression of (A) SP1, (B) NCOA1, (C) Total ERK, (D) p-ERK, at
translational level in MCF- 7 cells upon miRs and anti-miRs treatment.
GAPDH was used as a loading control. Data is represented as Mean ± SEM
for 3 independent experiments. p-value < 0.05 was considered to
indicate significance
Overexpression of individual components of miR- 23a/27a/24–2 cluster
supresses migration and invasive capacity of breast cancer cells
Transwell invasion assay was performed to determine the invasive
capacity of cells after treatment with individual components of miR-
23a/27a/24–2 cluster. As shown in Fig. [175]5A all three miRNAs miR-
23a/miR- 24–2/miR- 27a showed inhibition of the invasive capacity of
the cells by 0.90, 0.80 and 0.63 folds in MCF-7 cells respectively as
compared to pSil. Among them miR- 24–2 showed a statistically
significant decrease in the invasive capacity. The migration of MDA-MB-
231 cells was also inhibited upon treatment of miR- 23a by 0.70 folds
and miR- 24–2 by 0.90 folds whereas we did not see any changes in miR-
27a (Fig. [176]5B) as compared to the pSil control. In contrast AM
treatment by anti-miR- 23/anti-miR- 24/anti-miR- 27 enhanced the
invasion capacity of both MCF-7 and MDA-MB- 231 cells respectively in
comparison to negative control. This evidence supports the intricate
regulatory roles of individual miRNA components in controlling cell
invasion and hold tremendous potential as miRNA-based therapeutics for
breast cancer.
Fig. 5.
[177]Fig. 5
[178]Open in a new tab
Regulation of Invasion and EMT pathway by miR- 23a, miR- 24–2, and miR-
27a in breast cancer cells. Cell invasion assay was used to assess the
invasion capacity in (A) MCF-7, and (B) MDA-MB-231 breast cancer cells
after overexpression or downregulation of individual components of miR-
23a/27a/24–2 cluster. The images were captured at 10 × magnification.
(C) E- Cadherin, (D) Claudin 1, (E) Keratin 8/18, and (F) Fibronectin
expressions were quantified for epithelial-mesenchymal transition using
western blot. GAPDH was used as a loading control. Data is
representative of the mean ± SEM for three independent experiments.
p-value < 0.05 was considered to indicate significance
In order to understand the molecular mechanism underlying regulated
invasion of breast cancer cells, we further investigated the
involvement of miR- 23a, miR- 24–2 and miR- 27a in regulating EMT
(Epithelial-mesenchymal transition) markers. Our findings demonstrated
that overexpression of all three microRNAs increased the levels of
epithelial markers, including E-cadherin, claudin 1, and Keratin 8/18
as shown in Fig. [179]5C-E. MiR- 23a overexpression led to significant
upregulation of E-cadherin by 1.18 fold and insignificant changes in
E-Cadherin were observed after miR- 24–2 and miR- 27a over expression
in MCF7 cells by 1.11 and 1.13 fold change respectively. Overexpression
of miR- 23a, miR- 24–2 and miR- 27a led to increase in claudin 1 levels
by 1.46, 1.48 and 1.33-fold respectively, whereas their anti-miRs led
to a decrease in claudin 1 levels in MCF- 7 cells. In case of Keratin
8/18 significant upregulation was seen upon overexpression of miR- 27a,
by 1.18-fold and it was downregulated in the case of anti-miR- 23a, 24
and 27a by 0.85, 0.85 and 0.97-folds, respectively in MCF- 7 Cells.
Fibronectin, a mesenchymal marker, was also examined as shown in
Fig. [180]5F. Of these, miR- 23a overexpression exhibited a significant
upregulation by 1.14-fold, while miR- 24–2 and miR- 27a did not show
any change. On transfection with anti-miR- 23, 24 and 27, we observed a
significant upregulation in fibronectin expression by 1.24, 1.19, and
1.32-fold, respectively. These findings demonstrate that individual
members of the miR- 23a/27a/24–2 cluster suppresses migration,
invasion, and metastasis in breast cancer cells.
Members of miR- 23a/27a/24–2 cluster modulates cell cycle in breast cancer
cells
Cell cycle disruption is the defining characteristic of cancer,
resulting in uncontrolled cell division. Hence, it is important to
investigate the impact of miR- 23a/27a/24–2 cluster on cell cycle
progression. The cell cycle assay in MCF-7 cells was studied using
Propidium Iodide (PI) staining. As shown in Fig. [181]6A, we observed a
decreased number of cells in G1 phase as well as an increase in cell
population in S phase upon treatment with miR- 23a, miR- 24–2 and miR-
27a respectively as compared to pSil. This is followed by the decrease
in number of cells in G2 phase upon overexpression of individual
members of cluster. In addition, as shown in Fig. [182]6B, upon
overexpression of miR- 23a, 24–2, and 27a, we observed an upregulation
in the expression of cyclin D1 by 1.16-folds, 1.22-folds, and
1.14-folds, respectively. Conversely, upon treatment with their
respective anti-miRNAs, cyclin D1 was downregulated by 0.87, 0.77 and
0.9-folds, respectively. However, we did not observe any changes in
c-Myc expression levels upon miR and anti-miR treatment (Fig. [183]6C).
The above result suggests that these miRNAs promote cell cycle
progression from G1 to S phase, followed by the inhibition of
transition to G2 phase.
Fig. 6.
[184]Fig. 6
[185]Open in a new tab
Modulation of cell cycle progression by the members of miR-
23a/24–2/27a cluster. Cell cycle distribution of MCF-7 cells
transfected with miR- 23a, miR- 24–2, and miR- 27a (A) After PI
staining, flow cytometry of MCF- 7 cells was performed to assess cell
cycle progression 24 h post transfection. (B) Cyclin D1 (C) c-Myc
expressions were quantified for cell proliferation using western blot.
GAPDH was used as a loading control. Data is representative of the mean
± SEM. p-value < 0.05 was considered to indicate significance
Discussion
Breast cancer poses a notable risk to women’s health, primarily due to
its high mortality rate, often stemming from therapy failures [[186]1].
Resistance to chemotherapy and the risk of relapse present major
challenges in breast cancer management [[187]74]. Whilst miRNAs are
involved in the control of apoptosis and the prevention of cancer,
changes in their expression may also be linked to the hallmarks of
carcinogenesis, such as activation, invasion, and metastasis, as well
as the encouragement of tumor growth and proliferation and the
suppression of apoptosis [[188]75].
Herein, we discuss the association between three mature microRNAs in
the miR- 23a/27a/24–2 cluster and breast cancer. In brief, miR-
23a/27a/24 − 2 cluster is a polycistronic miRNA, encoding miR- 23a,
miR- 27a and miR- 24–2 but their biological functions are distinct due
to variations in expression regulation. The expression of miRNAs is
regulated by several factors (transcriptional and post-transcriptional)
and molecular mechanisms causing oncogenic or tumour suppressive
functions. Some studies have shown that upregulation of miR- 23a
cluster enables it to serve as potential biomarker for diagnostic and
progression of disease/metastasis for lung cancer, colorectal cancer,
gastric cancer and for other metabolic diseases [[189]17, [190]76,
[191]77]. In breast cancer research, findings have been inconsistent. A
study by Li et al. (2023), reported that the expression levels of miR-
23a, miR- 24–2 and miR- 27a were significantly elevated in breast
cancer with lymph node metastasis, compared to those without metastasis
or normal and played an oncogenic role [[192]78]. However, in the
current study our findings from both patient samples and in vitro
models suggest that the miR- 23a/27a/24–2 cluster may have a
tumor-suppressive role in breast cancer.
In silico analysis of the overlapping predicted target genes of the
cluster revealed NCOA1, GSK3β, and SP1 to be interacting with each
other in a hub. While direct interactions between all three genes might
not be extensively documented, their individual roles suggest potential
interconnectedness. GSK3β dysregulation has been linked to several
malignancies, including breast cancer, where it had an impact on the
survival and proliferation of cells [[193]79, [194]80]. In cancer, SP1
is frequently overexpressed and has a role in tumor development,
angiogenesis, and metastasis [[195]57]. NCOA1 by acting as a
transcriptional coactivator frequently plays a role in progression of
various cancers [[196]81]. The development and spread of tumors may be
influenced by the interactions between these variables.
Herein, Kaplan–Meier plots were used to evaluate the correlation
between the expression of these genes as well as miRNAs and the overall
survival rates of patients with breast cancer. GSK3β and NCOA1 were
among the significant genes. Compared with healthy mammary cells,
breast tumors having increased expression of GSK3β are correlated with
a reduced life expectancy. On the other hand, higher expression of
NCOA1 and SP1 in breast tumors was associated with better relapse-free
survival rate. According to the log-rank test while plotting the
Kaplan–Meier graph, miR- 24–2 was found to be particularly significant.
Hsa-miR- 24–2 could be a novel biomarker for breast cancer patients as
it is found highly expressed in FFPE tissues of breast cancer patients.
It's interesting to note that a higher survival rate for breast cancer
patients is linked to increased expression of miR- 23a and miR- 27a.
We have further shown that GSK3β is a direct target of miR- 23a and
miR- 24–2 in breast cancer cells. The Wnt/β-catenin pathway is one of
those pathways that are particularly important for the development of
breast cancer [[197]22]. Overexpression of individual miRNAs in the
miR- 23a/27a/24–2 cluster stimulated Wnt/β-catenin signaling in breast
cancer. Simultaneously, SP1 is frequently overexpressed in breast
cancer and encourages tumor development and metastasis. While it is
frequently overexpressed in multiple cancers and is associated with
poor clinical outcomes, its function remains complex, as it can both
promote and suppress the expression of oncogenes and tumor suppressors.
Due to its involvement in critical cancer pathways, SP1 has been
considered a potential therapeutic target; however, its diverse
regulatory roles pose significant challenges for targeted treatment
strategies [[198]57, [199]71]. In our investigation, we observed SP1
downregulation upon overexpression of the three members of the cluster.
Further research is required to confirm SP1 as the target of the
cluster.
Overexpression/inhibition of individual members in the miR-
23a/27a/24–2 cluster, modulated the expression of proteins involved in
the ERK pathway, resulting in the upregulation of total ERK levels.
Research has shown that the upregulated ERK signaling can promote EMT
in breast cancer cells by activating key transcription factors
[[200]82]. The potential of GSK3β to impede the development of EMT is
demonstrated by the reduction in the expression of mesenchymal
phenotypic markers upon inhibition. Functional assays, such as the
invasion assay, have frequently been used to demonstrate the
enhancement of the migratory potential of the cells, which contributes
to the highly aggressive and metastatic nature of the cells that have
undergone EMT [[201]83]. Various studies have highlighted the
significance of miR- 23a in regulating migration and invasive capacity
of breast cancer by targeting CDH1 gene which plays a pivotal role in
EMT [[202]84]. On the other hand, miR- 27a targets FBXW7 (F-box and WD
repeat domain containing 7) gene, which positively impacts the breast
cancer survival rate by inhibiting EMT thus influencing the migration
of cancer cells [[203]85]. miR- 24–2 overexpression is also known to
inhibit ING5 (member of Inhibitor of Growth family) resulting in
increased proliferation and invasion of breast cancer cells [[204]86].
Although the role of these miRNAs individually has been studied in the
context of breast cancer, their collective impact remains unexplored.
As represented in Fig. [205]7, the overexpression of miR- 23a/24–2/27a
cluster in breast cancer cell line induced several molecular and
phenotypic changes. We observed an increase in GSK3β expression and an
associated decrease in β-catenin as well as SP1 expression. According
to literature, elevated GSK3β levels promotes the phosphorylation and
subsequent proteasomal destruction of β-catenin and SP1. Reduced SP1
and increased GSK3β expression suggests a decrease in the
transcriptional activation of genes involved in cell growth and
proliferation. These observations were accompanied by a decrease in
epithelial-to-mesenchymal transition (EMT), evidenced by the increased
expression of epithelial markers such as E-cadherin, Claudin 1 and
Keratin 8/18 followed by a decrease in mesenchymal marker, Fibronectin.
The reduced EMT is associated with a decrease in migration and invasive
capacities of cancer cells. Also, the over-expression of the cluster
led to an S phase arrest in MCF-7 cells along with an increase in cell
cycle marker, Cyclin D1.
Fig. 7.
[206]Fig. 7
[207]Open in a new tab
Proposed mechanism of individual components of miR-23a/27a/24–2 cluster
in various pathways in breast cancer
Several studies including our current study suggested that expression
of all three miRNA in miR- 23a cluster, both individually and as part
of the cluster are implicated in breast cancer via association with
metastasis, migration, and invasion [[208]11, [209]16, [210]84,
[211]86].
Conclusion
Our study clarifies the important role of the individual members of the
miR- 23a/27a/24–2 cluster in breast cancer providing new therapeutic
approach. In our study we observed that the breast cancer tissues have
decreased expression of individual members of miR- 23a/27a/24–2
compared to control. However, breast cancer being heterogeneous in
nature and having different subtypes, would require validation of the
same in large number of breast cancer samples. The study also
highlights the important role of miR- 23a/27a/24–2 cluster in
regulating key genes linked to breast cancer progression, namely,
GSK3β, NCOA1 and SP1. Our observations regarding the dysregulation of
miRNA expression in breast cancer tissues underscore the importance of
further research to validate these findings across diverse patient
populations and tumor subtypes. This work opens avenues for future
investigations into personalized treatment strategies and the clinical
utility of targeting the miR- 23a/27a/24–2 cluster in breast cancer
therapy.
Supplementary Information
[212]Supplementary Material 1.^ (47.6KB, xlsx)
[213]Supplementary Material 2^ (92.5MB, pptx)
[214]Supplementary Material 3^ (264KB, pptx)
[215]Supplementary Material 4^ (10.4MB, pptx)
Acknowledgements