Skip to main content
Download PDF

MicroRNAome profiling of breast cancer unveils hsa-miR-5683 as a tumor suppressor microRNA predicting favorable clinical outcome

Cancer Cell International volume 24, Article number: 377 (2024) Cite this article

Abstract

Background

Breast cancer is a heterogeneous disease with diverse molecular subtypes, underscoring a better understanding of its molecular features and underlying regulatory mechanisms. Therefore, identifying novel prognostic biomarkers and therapeutic targets is crucial for advancing the current standard of care for breast cancer patients.

Methods

Ninety-six formalin-fixed paraffin-embedded (FFPE) breast cancer samples underwent miRNAome profiling using QIAseq microRNA library kit and sequencing on Illumina platform. Mature miRNA quantification was conducted using CLC Genomics Workbench v21.0.5, while Relapse-free survival (RFS) analysis was conducted using RStudio 2023.09.1. Gain-of-function studies were conducted using miRNA mimics, while the effects of miRNA exogenous expression on cancer hallmark were assessed using 2-dimentional (2D) proliferation assay, three-dimensional (3D) organotypic culture, and live-dead staining. TargetScan database and Ingenuity Pathway Analysis (IPA) were used for miRNA target identification.

Results

Hierarchical clustering based on miRNA expression revealed distinct patterns in relation to PAM50 classification and identified miRNAs panels associated with luminal, HER2, and basal subtypes. hsa-miR-5683 emerged as a potential prognostic biomarker, showing a favorable correlation with RFS and suppressing tumorigenicity under 2D and 3D conditions in triple-negative breast cancer (TNBC) models. Findings were further extended to the MCF7 hormone receptor positive (HR+) model. Transcriptomic profiling of hsa-miR-5683 overexpressing TNBC cells revealed its potential role in key oncogenic pathways. Integration of downregulated genes and CRISPR-Cas9 perturbational effects identified ACLY, RACGAP1, AK4, MRPL51, CYB5B, MKRN1, TMEM230, NUP54, ANAPC13, PGAM1, and SOD1 as bona fide gene targets for hsa-miR-5683.

Conclusions

Our data provides comprehensive miRNA expression atlas in breast cancer subtypes and underscores the prognostic and therapeutic significance of numerous miRNAs, including hsa-miR-5683 in TNBC. The identified gene targets unravel the intricate regulatory network in TNBC progression, suggesting promising avenues for further research and targeted therapeutic interventions.

Background

Breast cancer stands as the leading cause of cancer-related mortalities among women, with an estimated 297,790 new cases and 43,170 deaths in 2023 in the United States alone [1]. Similar statistics were also seen around the globe with more than 2.26 million new cases of breast cancer were reported in women in 2020 [2]. Despite recent advancements in the field, invasive breast carcinoma remains a significant health concern, where infiltrating ductal carcinomas (IDC) constitute 80% of invasive breast cancers, followed by invasive lobular carcinomas. In situ carcinomas are predominantly ductal, comprising 80%, with lobular types accounting for approximately 10% [3]. The age-standardized incidence rates (ASRs) of breast cancer continue to rise globally, with the Middle East and North Africa (MENA) region reporting ASRs ranging from 9.5 to 50 cases per 100,000 women annually. In Qatar, breast cancer is the most commonly diagnosed cancer among women. This emphasizes the urgency for a better understanding of the disease, and the identification of novel biomarkers and therapeutic targets [2, 4, 5].

Early detection plays a pivotal role in reducing mortality rates associated with breast cancer [6, 7]. However, despite numerous efforts to enhance early detection, a significant proportion of breast cancer patients are still diagnosed at an advanced stage [8]. While Oncotype DX [9] and MammaPrint [10] have demonstrated acceptable performances in predicting the prognosis and treatment benefit of early-stage breast cancer, the identification of additional predictors for relapse-free survival (RFS) could offer an opportunity for patient stratification and personalized therapy.

The impact of breast cancer subtypes on prognosis is substantial, with four main classifications: luminal A (LumA), luminal B (LumB), HER2, and basal, based on the differential expression of certain genes. Prognosis is notably better for patients with ER + and/or PR + and HER2- tumors, compared to those with HER2 overexpressing or basal [11, 12]. Efforts to classify breast tumors have led to the development of surrogate markers based on immunohistochemistry, with ER, PR, and HER2 being pivotal for stratification. The PAM50 classification has proven superior to immunohistochemistry-based surrogates, offering better prognostic and treatment prediction capabilities [13,14,15].

Non-coding RNA, particularly microRNAs (miRNAs), emerged over the past years as a key player in cancer progression and development, functioning primarily through posttranscriptional gene regulation. Their involvement in various human diseases, including cancer, highlights their potential role as tumor suppressors or oncogenes [16,17,18]. Our recent work has suggested a plausible role for miRNAs in shaping breast cancer subtypes [19]. In the current study, we characterized the miRNA catalogue in a cohort of breast cancer patients from the MENA region. We subsequently delineated the miRNA expression profile in relation to the PAM50 intrinsic subtypes and relapse free survival (RFS). Notably, our analysis revealed hsa-miR-5683 as a predictor of better prognosis. Mechanistically, the exogenous expression of hsa-miR-5683 suppressed breast cancer proliferation, cell cycle progression, and growth under three-dimensional (3D) conditions. Through integration with CRISPR-Cas9 essentiality data, our findings unveiled several crucial genes as bona fide targets for hsa-miR-5683, suggesting the potential utilization of hsa-miR-5683 as prognostic biomarkers and therapeutic targets, particularly for TNBC. Concordant with our data, miR-5683 was recently shown to promote apoptosis in gastric cancer [20].

Methods

Ethics statement and study cohort

The study received MRC-01–19–142 ethical approval from Hamad Medical Corporation (HMC) and QBRI-IRB 2020–09–035 approval from Qatar Biomedical Research Institute (QBRI). Clinicopathological features of the study cohort are presented in Table 1. Detailed description of the study cohort and inclusion/exclusion criteria can be found in our recent publication [19].

Table 1 Clinical characteristics of study cohort​
Full size table

Total RNA isolation and next-generation sequencing (NGS)

Total RNA isolation from FFPE tissues was conducted as we described before [19]. For miRNA library preparation, 100 ng of total RNA was used as input for 3′ ligation, followed by 5′ ligation and reverse transcription using the QIAseq miRNA library kit (QIAGEN, Hilden, Germany). The resulting cDNA libraries were quantified using the Qubit dsDNA HS assay kit and assessed for size distribution using the Agilent 2100 Bioanalyzer DNA1000 chip. Pooled libraries underwent sequencing on the Illumina platform. The miRNA transcriptomic data were deposited in the SRA repository under BioProject number PRJNA953015. For miRNA analysis, FASTQ files were mapped to the miRBase v22 database and the miRNA expression (total counts) were calculated using the small RNA analysis workflow in CLC Genomics Workbench 21.0.5. Expression data were then imported into iDEP.951, normalized (CPM, count per million), and log-transformed using EdgeR (log2(CPM + c)). miRNAs with a minimum expression of 5 CPM in at least 10 samples were retained. Hierarchical clustering and identification of differentially expressed miRNAs (DEMs) in relation to PAM50 intrinsic subtypes were conducted in iDEP.951 using 1.5-fold change (FC) and false discovery rate (FDR) p < 0.05.

Survival analysis

To identify the set of miRNAs correlating with ten-year patients’ RFS, the normalized miRNA expression data (log2 transformed) underwent RFS analysis using RStudio 2021.09.2. The ‘survival’ package in R was employed to compute the log-rank p values and hazard ratio (HR). Initial identification of potential miRNA candidates associated with RFS was conducted through univariate survival analysis and subsequently, these identified miRNA candidates underwent multivariate cox regression survival analysis using IBM SPSS Statistics v26 to adjust for potential confounding factors (molecular subtype, tumor grade, and age). The survival plot was generated by stratifying the patient cohort into high and low groups based on the median hsa-miR-5683 expression. Log-rank p value was used for curve comparison.

Validation of miR-5683 expression and survival analysis in additional breast cancer datasets

To validate the expression of hsa-miR-5683 in additional breast cancer cohorts, we investigated its expression in different breast cancer subtypes compared to 104 normal controls from the ExplORRnet database (https://mirna.cs.ut.ee/). Survival analyses based on hsa-miR-5683 expression were subsequently validated using the KM Plotter database (https://kmplot.com/analysis/index.php?p=background). Correlation between hsa-miR-5683 and its target mRNA expression were calculated using the ENCORI database (https://rnasysu.com/encori/).

Cell culture and transfection

The human TNBC cell lines (MDA-MB-231 and BT-549) were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with D-glucose at a concentration of 4500 mg/L, 2–4 mM L-glutamine, 10% fetal bovine serum, and 1× penicillin-streptomycin (Pen-Strep), all purchased from GIBCO-Invitrogen (Waltham, MA, USA). Cells were maintained in humidified CO2 (5%) incubator at 37 °C. The hsa-miR-5683 mirVana miRNA mimic (Assay ID: MC22887) and miRNA negative control (scrambled) were purchased from Thermo Fisher Scientific (Thermo Fisher Scientific, Waltham, MA). In the current study, we utilized the reverse transfection protocol where miRNA mimic and control were diluted in 50 µL of Opti-MEM (GIBCO, Carlsbad, CA, USA), and 1.5 µL of Lipofectamine 2000 (Thermo Fisher Scientific, Waltham, MA) was diluted in 50 µL of Opti-MEM. The resulting miRNA and Lipofectamine 2000 mixtures were combined and incubated at room temperature for 20 min. The cell count per well was 1.68 × 105 cells (MDA-MB-231) and 8.4 × 104 cells (BT-549) in 2.4 mL transfection medium (complete DMEM without Pen-Strep). This was followed by the addition of 0.8 mL of the transfection mixture to the 6-well tissue culture plate resulting in final concentration of 30 nM. After 24 h, the transfection cocktail was topped up with complete DMEM. Transfection of the MCF7 HR + breast cancer model was carried as detailed above, using 0.75 µl of lipofectamine 2000.

Cell line authentication by STR

Genomic DNA (gDNA) extracted from MDA-MB-231 and BT-549 TNBC lines was used as input for STR profiling using AmpFLSTR Identifiler PCR amplification kit (Thermo Fisher Scientific, Inc., Waltham, MA, USA) following the manufacturer’s protocol. Briefly, starting from 1 ng of gDNA, DNA was subjected to PCR amplification. Positive and negative controls were run in parallel with the samples. After amplification, PCR products were prepared for electrophoresis by adding Hi-Di Formamide and size standard mixture to each sample and allelic ladder. Electrophoresis was performed in the Genetic Analyzer 3500xl DX system. Allelic calls analysis is performed by Gene Mapper software from Applied Biosystems/Thermo Fisher Scientific. Reference STR profiles were retrieved from ATCC (https://www.atcc.org/).

Reverse transcriptase quantitative polymerase chain reaction (RT-qPCR)

To validate hsa-miR-5683 overexpression efficiency, total RNA was extracted 72 h post transfection using miRNeasy Mini Kit (Qiagen Inc., Hilden, Germany). The concentration and quality of extracted RNA was measured using NanoDrop 2000 (Thermo Scientific, DE, USA). Following the RNA extraction, reverse transcription PCR was performed using the miRCURY LNA RT Kit (Qiagen Inc., Hilden, Germany). The generated cDNA was then used to perform qPCR using the miRCURY LNA SYBR Green PCR kit (Qiagen Inc., Hilden, Germany) to measure the level of hsa-miR-5683 expression in mimic and control transfected TNBC cells. The relative FC in miRNA expression was calculated using the 2−ΔΔCt method, where the average of ΔCt values for the target amplicon was normalized to that of SNORD44 endogenous control and compared to negative control transfected samples.

The candidate genes identified as bona fide targets for hsa-miR-5683 were validated in both MDA-MB-231 and BT-549 post miRNA mimic transfection using RT-qPCR. RNA extracted from both TNBC cell lines (500 ng) were reverse transcribed to cDNA using the High-Capacity cDNA Reverse Transcription kit (Thermo Fisher Scientific, Waltham, MA). Subsequently, qPCR was performed using specific primer pairs as detailed in Table S1 and the PowerUp SYBR Green Master Mix (Thermo Fisher Scientific, Waltham, MA) on QuantStudio 6 Flex qPCR system (Applied Biosystems). The mRNA transcript levels of the target genes were determined based on their respective CT values, normalized against β-actin (ACTB) transcript levels, and presented as FC using the 2−ΔΔCt method compared to control cells.

Proliferation assay

MDA-MB-231 and BT-549 cells were transfected with hsa-miR-5683 mimic or negative miRNA control as described above. Subsequently, the ramifications of hsa-miR-5683 expression on TNBC cells were assessed using proliferation assay. On day 7, cells were washed twice using phosphate-buffered saline (PBS). Subsequent staining with crystal violet (0.1% in 10% ethanol) was conducted and plates were placed on a shaker for 2–3 h. Plates were then air-dried at room temperature before imaging, and quantification by dissolving crystal violet in 10% SDS, and subsequent measurement of absorbance at 590λ.

Three-dimensional (3D) and spheroid culture

For the generation of 3D cultures, cells under different treatment conditions were pelleted at a concentration of 0.25 × 106 cells/mL and were subsequently combined with Matrigel (Corning; 356231; Growth Factor Reduced Basement Membrane Matrix). Subsequently, several drops of the cell suspension (one drop/well, approximately each drop containing 10,000 cells in 40 µl of Matrigel) were dispensed into pre-warmed (37 °C) Ultra-Low Attachment 24-well Culture plates (Corning Corp., Bebford MA, USA). The plates were then inverted and placed in a 37 °C, 5% CO2 cell culture incubator for 20 min to allow the droplets to solidify and form dome structures. Subsequently, 1–2 mL of expansion medium was added to cover the dome. Organoid formation under different experimental conditions were observed using microscope on the indicated dates. For spheroid formation, MDA-MB-231 and BT-549 under different treatment conditions were trypsinized, and re-suspended in culture media, and were then seeded in 60 mm low cell binding dishes (Corning Corp., Bebford MA, USA) at a density of 6 × 104 cells/dish, following our previously described protocol [21]. On day 10, multicellular tumor spheroids were observed using an inverted microscope (Axio Observer-A1, Carl Zeiss, Germany) at 4X magnification.

Dead/live cells staining using AO/EtBr fluorescent microscopy

Fluorescence microscopy was employed for the detection of cell death under different experimental conditions. Briefly, the AO/EtBr fluorescence staining method was used to assess cell death on day 5 post transfection of TNBC cell with hsa-miR-5683 mimic or negative control. Subsequently, cells were washed twice using PBS before staining with a dual fluorescent solution containing 100 µg/mL AO and 100 µg/mL EtBr (AO/EtBr, Sigma Aldrich, St. Louis, MO, USA) for 2 min. The stained wells were then observed and imaged using an Olympus IX73 fluorescence microscope (Olympus, Tokyo, Japan). AO staining was used to visualize nuclei, while EtBr-positive cells indicated the presence of necrotic cells.

Cell cycle analysis using flow cytometry

Flow cytometry was employed for cell cycle analysis of MDA-MB-231 and BT-549 post-transfection with hsa-miR-5683 mimic or negative control. On day 4 post-transfection, both floating and adherent cells were collected and were then washed, fixed with ice-cold 70% ethanol, and stored at -20 °C. The cells were washed twice using PBS and were subsequently incubated in RNase A (100 µg/mL) followed by addition of propidium iodide (PI; 50 µg/mL). Stained cells were run through a BD LSRFortessa X-20 flow cytometer (BD Biosciences, CA, USA) and events were recorded using the FL3 channel. Data were subsequently analyzed using FlowJo software (FlowJo 10.7.2, BD Biosciences, CA, USA).

Identification of potential gene targets for hsa-miR-5683

To identify potential gene targets for hsa-miR-5683, MDA-MB-231 and BT-549 were transfected with hsa-miR-5683 mimic or negative control. At 72 h, total RNA was extracted from transfected cells, and subsequently library preparation was done using the TruSeq Stranded Total RNA Library (Illumina Inc., San Diego, CA, USA) followed by NGS using the Illumina HiSeq 4000 at approximately 30 million paired end reads (2 × 75 bp) per sample as we described before [22]. The generated FASTQ files were subsequently aligned and mapped to the hg38 reference genome using the CLC Genomics Workbench v21.0.5. Subsequently, iDEP.951 was employed for differential expression analysis to identify differentially expressed genes (DEGs) in hsa-miR-5683 compared to negative control transfected TNBC cells using 2.0 FC and p (FDR) < 0.1. To identify bona fide gene targets for hsa-miR-5683, the TargetScan (version 80) in silico miRNA target prediction database was employed. The microRNA Target Filter in Ingenuity Pathway Analysis (IPA) was utilized to construct the hsa-miR-5683-mRNA network. The identified targets resulting from this integration were further validated using RT-qPCR.

Statistical analysis

For DEGs and DEMs, analyses were performed in iDEP.951. A FC of 1.5 and an FDR-adjusted p-value < 0.05 served as the cutoff unless otherwise specified. Graphing and pairwise statistical analyses were executed in GraphPad Prism v9 and GraphPad Prism v10.

Results

Comparative miRNA expression profiling in relation to breast cancer intrinsic subtypes

The overall study design is illustrated in Fig. 1A. Heatmap visualization of the miRNAome from ninety-six breast cancer samples, providing an overview of miRNA expression patterns across the entire cohort (Fig. 1B). The heatmap utilized a color gradient ranging from blue (indicating low expression) to red (indicating high expression) to depict miRNA expression levels. Notably, the heatmap revealed distinct miRNA expression patterns, discriminating luminal, basal, HER2, and normal-like (designated as normal) subtypes. Hierarchical clustering based on the top 200 variable miRNAs demonstrated a distinct separation of luminal and basal subtypes, while a close clustering between the basal and HER2 subtypes was observed (Fig. 1B).

Fig. 1
figure 1

Hierarchical clustering and differential miRNA expression analysis in breast cancer. A Illustration of overall study design. B Heatmap depicting clustering of 96 breast cancer patients according to top 200 most variable miRNAs in relation to PAM50 intrinsic subtype classification (Luminal, Basal, HER2, and Normal-like (designated as Normal)). Color scale depicts the expression level of each miRNA. Each row represents a single miRNA, and each column represents a sample. C Bar chart (upper panel) depicting the number of DEMs in Luminal vs. Normal, HER2 vs. Normal, HER2 vs. Luminal, Basal vs. Normal, Basal vs. Luminal, and Basal vs. HER2 using 1.5 FC and < 0.05 FDR, after adjusting for age and tumor grade. Volcano plot (lower panel) illustrating the DEMs in Basal vs. Luminal subtypes with upregulated (red) and downregulated (blue) miRNAs highlighted. D Violin plots illustrating the top five upregulated (top) and five downregulated (bottom) miRNAs in Basal bs Luminal subtypes. * p < 0.05, ** p < 0.005, **** p < 0.00005

Full size image

Subsequently, we aimed to identify specific miRNAs with varying expression levels when comparing luminal vs. normal-like, HER2 vs. normal-like, basal vs. normal-like, and basal vs. HER2, using a 1.5 FC and an adjusted FDR of p < 0.05, while adjusting for age, tumor grade, and ethnicity. The analysis of DEMs (Fig. 1C, upper panel) revealed the largest numbers when comparing basal vs. luminal, with 83 upregulated and 42 downregulated miRNAs. Basal vs. HER2 showed 38 upregulated and 13 downregulated miRNAs, while HER2 vs. luminal identified 16 upregulated and 12 downregulated miRNAs. The comparison of luminal and normal-like had the fewest DEMs, with 3 upregulated and 13 downregulated miRNAs. Similarly, HER2 vs. normal showed 7 upregulated and 11 downregulated miRNAs, and basal vs. normal identified 18 upregulated and 8 downregulated miRNAs. A volcano plot highlighting DEMs with selected upregulated (red) and downregulated (blue) miRNAs in the basal vs. luminal subtypes shown on the plot (Fig. 1C, lower panel). A representative set of DEMs (top 5 upregulated and top 5 downregulated) in the basal vs. luminal comparison are illustrated as violin plots in Fig. 1D. The top upregulated miRNAs included hsa-miR-135-3p, hsa-miR-577, hsa-miR-934, hsa-miR-135-5p, and hsa-miR-4488, whereas the top downregulated miRNAs included hsa-miR-375-3p, hsa-miR-196a-3p, hsa-miR-190b-5p, hsa-miR-342-5p, and hsa-miR-342-3p (Fig. 1D).

Identification of hsa-miR-5683 as a favorable prognostic biomarker in breast cancer

To identify the set of miRNAs associated with breast cancer patients’ RFS, the expression data from a total of 84 patients (with available RFS follow-up data) were subjected to univariate survival and hazard ratio regression analysis, which identified 12 miRNAs as unfavorable, and 11 miRNAs as favorable prognostic biomarkers in our cohort (Table S2). Among those, hsa-miR-5683 exhibited the highest favorable prognostic value (HR = 0.32, p = 0.006). To assess the prognostic value of hsa-miR-5683, while adjusting for age, tumor grade, and molecular subtypes, a multivariate Cox regression model was employed affirming the favorable prognostic value of hsa-miR-5683 in our cohort (Fig. 2A). Concordantly, the downregulated expression of hsa-miR-5683 was validated in different breast cancer subtypes compared to normal breast samples, which revealed significant downregulation of hsa-miR-5683 in all breast cancer subtypes, thus providing a therapeutic window for potential intervention (Fig. 2B). Concordantly, the prognostic value of hsa-miR-5683 was further validated in the TCGA BCRA cohort where elevated expression were associated with better survival (HR = 0.63 (0.43 − 0.9), P = 0.01) as illustrated in Figure S1.

Fig. 2
figure 2

Hsa-miR-5683 predicts a better prognosis and suppresses tumorigenicity of TNBC. A Kaplan Meier survival plot for 84 breast cancer patients stratified according to median hsa-miR-5683 expression, after adjusting for age, tumor grade, and molecular subtypes. P value for curve comparison is indicated on the plot. B Violin plot illustrating the expression of hsa-miR-5683 in normal breast tissue (n = 104) compared to normal-like (n = 31), LumA (n = 451), LumB (n = 186), HER2 (n = 71), and Basal (n = 149) breast cancer subtypes based on ExplORRnet database. C Representative images illustrating effects of exogenous expression of hsa-miR-5683 on MDA-MB-231 and BT-549 cell proliferation. D Quantification of proliferation potential in hsa-miR-5683 mimic compared to negative control transfected cells. Data are presented as mean ± S.D., n = 4. Representative images illustrating suppression of 3D (E) and spheroid (F) growth of MDA-MB-231 and BT-549 in response to exogenous expression of hsa-miR-5683

Full size image

Exogenous expression of hsa-miR-5683 impairs proliferation and induces cell death in TNBC models

We subsequently assessed the biological functions of hsa-miR-5683 in breast cancer cells using the proliferation assay. Exogenous transfection of hsa-miR-5683 mimics in MDA-MB-231 and BT-549 TNBC models led to significant upregulation of hsa-miR-5683 expression (Figure S2) and significant suppression of proliferation potential of both TNBC models (Fig. 2C). Concordantly, quantitative analysis of cell proliferation highlighted catastrophic effects for hsa-miR-5683 expression on TNBC (Fig. 2D). Our findings were further extended to the MCF7 HR + breast cancer model. Exogenous expression of hsa-miR-5683 suppressed MCF7 proliferation in vitro (Figure S3). In contrast, forced expression of hsa-miR-5683 had no impact on the proliferation potential of the MCF10A normal breast epithelial cells (Figure S4). We subsequently assessed the effects of hsa-miR-5683 on the growth of TNBC cells under 3D conditions, which potentially resemble the in vivo tumor microenvironment. Data presented in Fig. 2E revealed significant suppression of 3D growth of MDA-MB-231 and BT-549 TNBC models, upon hsa-miR-5683 re-expression. Similarly, the ability of TNBC cells to form spheroids was substantially halted in response to hsa-miR-5683 overexpression in both cell lines (Fig. 2F), thus corroborating a tumor suppressor role for hsa-miR-5683 in breast cancer.

We subsequently utilized the AO/EtBr uptake assay to assess the effects of miR-5683 expression on TNBC cells. Our data revealed a remarkable increase in cell death (red staining) in TNBC cells transfected with hsa-miR-5683 mimics when compared to the negative control transfected cells, suggesting a tumor suppressor function for this miRNA (Fig. 3A). Chromatin condensation and nuclear blebbing were also observed, suggesting induction of apoptotic cell death, in addition to necrosis, in TNBC cells in response to forced expression of hsa-miR-5683 (Fig. 3A). Concordant with fluorescence microscopy, cell cycle analysis also revealed a substantial increase in apoptotic (sub-G0) and a reduction in the G1 phase of the cell cycle (Fig. 3B).

Fig. 3
figure 3

Dead-live staining and cell cycle distribution of TNBC cells in response to exogeneous expression of hsa-miR-5683. A Representative fluorescence images for MDA-MB-231 and BT-549 TNBC models post transfection with hsa-miR-5683 mimics compared to negative control. Cells were stained on day 5 with AO/EtBr to detect dead cells (red; necrotic). B Cell cycle analysis depicting the proportion of cells in the indicated stage of cell cycle in hsa-miR-5683 overexpressing and control TNBC cells. Data are presented as mean ± S.D., n = 4

Full size image

Transcriptomic profiling of hsa-miR-5683 overexpressing TNBC cells

To elucidate the underlying mechanisms of hsa-miR-5683-mediatd TNBC suppression, MDA-MB-231 and BT-549 cells were transfected with hsa-miR-5683 mimics, and 72 h later, RNA was extracted for whole transcriptome RNA-Seq analysis. Our analysis revealed 138 downregulated genes (2.0-FC, p (FDR) < 0.1), while 107 genes were upregulated in response to hsa-miR-5683 overexpression (Fig. 4A and Table S3). The gene ontology enrichment tree demonstrated suppression of various biological processes in hsa-miR-5683-overexpressing TNBC, including glycolysis, nuclear division, and many others (Fig. 4B).

Fig. 4
figure 4

Transcriptomic profiling of hsa-miR-5683 overexpressing cells revealed a role for hsa-miR-5683 in regulating key oncogenic pathways in TNBC. A Heatmap illustrating alterations in gene expression in MDA-MB-231 and BT-549 cells overexpressing hsa-miR-5683 compared to control cells. B Enrichment tree depicting top affected GO pathways based on DEGs in TNBC cell overexpressing hsa-miR-5683. C Bubble chart illustrating activated (orange) and suppressed (blue) canonical pathways in TNBC cells overexpressing hsa-miR-5683 employing IPA. D Identification of bona fide gene targets for hsa-miR-5683 employing IPA tool. The shape indicates the class of each identified gene target according to the figure legend

Full size image

The DEGs were further subjected to IPA for canonical, disease and function, and network analyses. Figure 4C illustrates the suppression of numerous canonical pathways related to cellular growth, proliferation, and development, among others, in hsa-miR-5683-transfected TNBC cells. Concordantly, disease and function analysis revealed significant suppression of cellular growth and proliferation (Figure S5 and Table S4). Network analysis highlighted numerous affected networks such as cancer, cell death and survival, organismal injury, and abnormalities in hsa-miR-5683 transfected cells (Table S5). The microRNA target filter in IPA was subsequently employed to identify potential bona fide gene targets for hsa-miR-5683 in TNBC. The hsa-miR-5683-mRNA network in Fig. 4D illustrates the regulation of several genes by hsa-miR-5683 based on RNA-Seq and IPA predictions.

Identification of hsa-miR-5683 gene targets essential for TNBC survival

We subsequently employed a tri-modality approach to identify hsa-miR-5683 gene targets essential for TNBC cell proliferation and survival. Firstly, RNA-Seq analysis from hsa-miR-5683 overexpressing TNBC identified 138 downregulated genes. Subsequently, in silico predicted gene targets for hsa-miR-5683 based on the TargetScan database led to the identification of 87 potential gene targets that were also downregulated upon hsa-miR-5683 overexpression (Fig. 5A). To identify the set of hsa-miR-5683 gene targets with potential role in TNBC, the gene effect score for the identified 87 potential gene targets were retrieved from the dependency map database, revealing eleven genes (ACLY, RACGAP1, AK4, MRPL51, CYB5B, MKRN1, TMEM230, NUP54, ANAPC13, PGAM1, and SOD1) as an hsa-miR-5683 target genes essential for TNBC survival (gene effect score ≤ -0.3, Fig. 5B). The suppressed expression of these 11 genes was subsequently validated in MDA-MB-231 and BT-549 cells overexpressing hsa-miR-5683 by RT-qPCR (Fig. 5C), confirming the involvement of this identified miR-5683-gene circuit in TNBC. To further support our findings, correlation analysis in a large cohort of breast cancer samples revealed an inverse correlation between hsa-miR-5683 and the 11 identified gene targets in the current study (Figure S6). Taken together, our comprehensive investigation integrated transcriptomic, in silico predictions, and dependency map data, thus establishing a robust miR-5683-gene circuit implicated in TNBC pathogenesis.

Fig. 5
figure 5

Identification of hsa-miR-5683 gene targets essential for TNBC. A Venn diagram illustrating the overlap between downregulated genes in TNBC cells overexpressing hsa-miR-5683 and predicted hsa-miR-5683 gene targets based on TargetScan database. B Gene effect plot illustrating the gene effect score (y-axis) for the identified hsa-miR-5683 gene targets (x-axis) employing CRISPR-Cas9 screen data from dependency map. C Validation of eleven identified hsa-miR-5683 gene targets in MDA-MB-231 and BT-549 TNBC cells overexpressing hsa-miR-5683 or negative control using RT-qPCR. Data are presented as mean ± S.D. from two independent experiments, n = 6. *p < 0.05, **p < 0.005, ***p < 0.0005. D Schematic presentation illustrating elevated expression of hsa-miR-5683 to predict favorable prognosis. Reinstated expression of hsa-miR-5683 suppressed cancer hallmarks, in part, through regulation of the eleven indicated bona fide gene targets

Full size image

Discussion

In the current study, we aimed to define the microRNAome profile in a local cohort of 96 breast cancer patients. This study revealed distinct clustering according to the miRNA transcriptome of breast cancer intrinsic subtypes and highlighted the prognostic and therapeutic significance of hsa-miR-5683 in TNBC. Hierarchical clustering based on miRNA expression demonstrated a clear separation between luminal and basal subtypes, consistent with a prior study demonstrating similar clustering of breast cancer subtypes based on mRNA expression [23]. Additionally, a close clustering between the basal and HER2 subtypes was observed, concordant with our recently published data [19]. The identified largest number of DEMs in basal vs. luminal comparison shed light on the pronounced distinctions between these two subtypes. Moreover, it suggests that alterations in miRNA-mediated regulatory pathways are involved in discrimination between basal and luminal subtypes, which influence the phenotype and clinical characteristics of patients with these two subtypes. Conversely, the comparison of luminal and normal-like had the fewest DEMs, with 3 upregulated and 13 downregulated miRNAs. This small degree of divergence implies that there is a close molecular similarity between the luminal and normal-like subtypes, however this correlation warrants further validation in larger breast cancer cohorts.

Notably, our data is the first to report hsa-miR-5683 as a favorable prognostic biomarker and therapeutic target in breast cancer. This miRNA exhibited the highest favorable prognostic value (HR = 0.32, p = 0.006). Additionally, the biological functions of this candidate miRNA were assessed employing proliferation, growth under 3D conditions, and spheroids formation ability. The overexpression of hsa-miR-5683 in TNBC cells (MDA-MB-231 and BT-549) led to a remarkable suppression of proliferation potential. In addition, a substantial suppression of 3D growth and spheroids formation was observed. This noticed decrease in TNBC cell growth suggests a potential function of this miRNA in controlling cell proliferation. Colony formation ability is considered a hallmark characteristic of tumor cells [24]. Therefore, the alignment in these three outcomes elucidated the tumor suppressive function of hsa-miR-5683 in TNBC, which was further extended to an HR + breast cancer model. The lack of significant inhibition of cell growth in the normal MCF10A model further supports the therapeutic potential of hsa-miR-5683 in breast cancer.

Building upon the findings of hsa-miR-5683 tumor suppressive function, we subsequently sought to characterize alterations in the transcriptome of hsa-miR-5683 overexpressing cells. Our data revealed a decrease in the expression of 138 genes. The observed inhibition of glycolysis and nuclear division implies a regulatory effect of hsa-miR-5683 on essential cellular functions. The notable downregulation in glycolysis points to potential changes in the cellular metabolism process [25]. Suppressing nuclear division causes several consequences, including reduced mitosis and cellular proliferation. Having established the regulatory effects of hsa-miR-5683 on cellular processes, we subsequently sought to identify bona fide hsa-miR-5683 gene targets essential for TNBC. In pursuit of this aim, several approaches were executed including gene effect score analysis (based on genome-wide CRISPR screen), experimentally downregulated mRNA transcript, RT-qPCR validation, and correlation analysis within an additional breast cancer cohort. Based on the gene effect scores, we identified 11 potential miR-5683 gene targets as highly relevant to the pathogenesis of TNBC. RT-qPCR validated the downregulated expression of those 11 gene targets in cells overexpressing hsa-miR-5683, affirming the association of this miR-5683-gene circuit in the pathogenesis of this disease. Among the identified gene targets, SOD1 was previously shown to exhibit high expression in breast cancer cells, thereby promoting cancer cells survival [26]. Similarly, several of the identified hsa-miR-5683 gene targets, including ACLY [27], RACGAP1 [28], AK4 [29], MRPL51 [30], and PGAM1 [31] were previously shown to play a role in breast cancer. Overall, the observed results identified hsa-miR-5683 as a master tumor suppressor miRNA predicting favorable clinical outcomes in breast cancer patients.

In summary, this study investigated miRNA expression in breast cancer, identifying hsa-miR-5683 as a potential prognostic biomarker in breast cancer. This miRNA demonstrated a favorable correlation with RFS and suppressed tumor growth under 2D and 3D organotypic settings. These findings suggest a promising translational application for this miRNA in improving prognosis and therapeutic interventions for TNBC. Additionally, the study pinpointed essential genes, as bona fide targets for hsa-miR-5683, providing insights into the regulatory network in TNBC progression. Those findings were further extended to HR + breast cancer. Overall, the study underscores the significance of hsa-miR-5683 in breast cancer and offers potential avenues for targeted therapeutic strategies in TNBC and HR + breast cancer.

Data availability

PRJNA953015.

References

  1. Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. Ca Cancer J Clin. 2023;73(1):17–48.

    Article  PubMed  Google Scholar 

  2. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin. 2021;71(3):209–49.

    Article  Google Scholar 

  3. Alteri R, Barnes C, Burke A, Gansler T, Gapstur S, Gaudet M, Kramer J, Newman L, Niemeyer D, Richards C. Breast cancer facts & figs. Atlanta: American Cancer Society; 2013. pp. 2013–4.

    Google Scholar 

  4. Bener A, Ayub H, Kakil R, Ibrahim W. Patterns of cancer incidence among the population of Qatar: a worldwide comparative study. Asian Pac J Cancer Prev. 2008;9(1):19–24.

    PubMed  Google Scholar 

  5. Boyle P, Levin B. World cancer report 2008. IARC Press, International Agency for Research on Cancer; 2008.

  6. Galizia D, Milani A, Geuna E, Martinello R, Cagnazzo C, Foresto M, Longo V, Berchialla P, Solinas G, Calori A. Self-evaluation of duration of adjuvant chemotherapy side effects in breast cancer patients: a prospective study. Cancer Med. 2018;7(9):4339–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Relative survival by. stage at diagnosis (female breast cancer) [https://ncci.canceraustralia.gov.au/relative-survival-stage-diagnosis-female-breast-cancer]

  8. Zielonke N, Gini A, Jansen EEL, Anttila A, Segnan N, Ponti A, Veerus P, de Koning HJ, van Ravesteyn NT, Heijnsdijk EAM, et al. Evidence for reducing cancer-specific mortality due to screening for breast cancer in Europe: a systematic review. Eur J Cancer. 2020;127:191–206.

    Article  PubMed  Google Scholar 

  9. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817–26.

    Article  CAS  PubMed  Google Scholar 

  10. van ‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530–6.

    Article  PubMed  Google Scholar 

  11. Vuong D, Simpson PT, Green B, Cummings MC, Lakhani SR. Molecular classification of breast cancer. Virchows Arch. 2014;465:1–14.

    Article  CAS  PubMed  Google Scholar 

  12. Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, Van De Rijn M, Jeffrey SS. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proceedings of the National Academy of Sciences 2001, 98(19):10869–10874.

  13. Nielsen TO, Parker JS, Leung S, Voduc D, Ebbert M, Vickery T, Davies SR, Snider J, Stijleman IJ, Reed J. A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor–positive breast cancer. Clin Cancer Res. 2010;16(21):5222–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chia SK, Bramwell VH, Tu D, Shepherd LE, Jiang S, Vickery T, Mardis E, Leung S, Ung K, Pritchard KI. A 50-gene intrinsic subtype classifier for prognosis and prediction of benefit from adjuvant tamoxifen. Clin Cancer Res. 2012;18(16):4465–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Pu M, Messer K, Davies SR, Vickery TL, Pittman E, Parker BA, Ellis MJ, Flatt SW, Marinac CR, Nelson SH. Based PAM50 signature and long-term breast cancer survival. Breast Cancer Res Treat. 2020;179:197–206.

    Article  CAS  PubMed  Google Scholar 

  16. Ambros V. microRNAs: tiny regulators with great potential. Cell. 2001;107(7):823–6.

    Article  CAS  PubMed  Google Scholar 

  17. Calin GA, Croce CM. MicroRNA signatures in human cancers. Nat Rev Cancer. 2006;6(11):857–66.

    Article  CAS  PubMed  Google Scholar 

  18. VishnubalajiR, Shaath H, Al-Alwan M, Abdelalim EM, Alajez NM: Reciprocal interplays between MicroRNAs and pluripotency transcriptionfactors in dictating stemness features in human cancers. Semin Cancer Biol 2022, 87:1-16.

  19. Elango R, Rashid S, Vishnubalaji R, Al-Sarraf R, Akhtar M, Ouararhni K, Decock J, Albagha OM, Alajez NM. Transcriptome profiling and network enrichment analyses identify subtype-specific therapeutic gene targets for breast cancer and their microRNA regulatory networks. Cell Death Dis. 2023;14(7):415.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Chen X, Wu X, Ge F, Cheng J, Cheng Z, Qi Z. Aloin induces gastric Cancer Cell apoptosis via the miR-5683/HMGB1 Signal Axis. Nat Prod Commun. 2023;18(5):1934578X231168759.

    CAS  Google Scholar 

  21. Elango R, Vishnubalaji R, Manikandan M, Binhamdan SI, Siyal AA, Alshawakir YA, Alfayez M, Aldahmash A, Alajez NM. Concurrent targeting of BMI1 and CDK4/6 abrogates tumor growth in vitro and in vivo. Sci Rep. 2019;9(1):13696.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Shaath H, Vishnubalaji R, Elango R, Velayutham D, Jithesh PV, Alajez NM. Therapeutic targeting of the TPX2/TTK network in colorectal cancer. Cell Commun Signal. 2023;21(1):265.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Bertucci F, Finetti P, Cervera N, Charafe-Jauffret E, Buttarelli M, Jacquemier J, Chaffanet M, Maraninchi D, Viens P, Birnbaum D. How different are luminal A and basal breast cancers? Int J Cancer. 2009;124(6):1338–48.

    Article  CAS  PubMed  Google Scholar 

  24. Kattan Z, Minig V, Leroy P, Dauça M, Becuwe P. Role of manganese superoxide dismutase on growth and invasive properties of human estrogen-independent breast cancer cells. Breast Cancer Res Treat. 2008;108:203–15.

    Article  CAS  PubMed  Google Scholar 

  25. Roubinet C, White IJ, Baum B. Asymmetric nuclear division in neural stem cells generates sibling nuclei that differ in size, envelope composition, and chromatin organization. Curr Biol. 2021;31(18):3973–83. e3974.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Papa L, Hahn M, Marsh EL, Evans BS, Germain D. SOD2 to SOD1 switch in breast cancer. J Biol Chem. 2014;289(9):5412–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Chen Y, Li K, Gong D, Zhang J, Li Q, Zhao G, Lin P. ACLY: a biomarker of recurrence in breast cancer. Pathol - Res Pract. 2020;216(9):153076.

    Article  CAS  PubMed  Google Scholar 

  28. Lawson CD, Der CJ. Filling GAPs in our knowledge: ARHGAP11A and RACGAP1 act as oncogenes in basal-like breast cancers. Small GTPases. 2018;9(4):290–6.

    Article  CAS  PubMed  Google Scholar 

  29. Zhang J, Yin Y-T, Wu C-H, Qiu R-L, Jiang W-J, Deng X-G, Li Z-X. AK4 promotes the progression of HER2-positive breast cancer by facilitating cell proliferation and invasion. Disease Markers 2019, 2019.

  30. Davis RT, Blake K, Ma D, Gabra MBI, Hernandez GA, Phung AT, Yang Y, Maurer D, Lefebvre AE, Alshetaiwi H. Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing. Nat Cell Biol. 2020;22(3):310–20.

    Article  CAS  PubMed  Google Scholar 

  31. Liu M, Li R, Wang M, Liu T, Zhou Q, Zhang D, Wang J, Shen M, Ren X, Sun Q. PGAM1 regulation of ASS1 contributes to the progression of breast cancer through the cAMP/AMPK/CEBPB pathway. Mol Oncol. 2022;16(15):2843–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Qatar National Research Fund (grant no. NPRP12S-0221-190124) and Qatar Biomedical Research institute (QBRI) (grant no. IGP5-2022-006) for funding this study.

Funding

This work was supported by Qatar National Research Fund (grant no. NPRP12S-0221-190124) and intramural grant program (IGP5-2022-006) from Qatar Biomedical Research institute (QBRI) for Dr. Nehad Alajez.

Author information

Authors and Affiliations

  1. College of Health & Life Sciences, Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha, Qatar

    Bushra Yasin Abohalawa & Nehad M. Alajez

  2. Translational Oncology Research Center (TORC), Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), PO Box 34110, Doha, 00000, Qatar

    Hibah Shaath, Ramesh Elango, Radhakrishnan Vishnubalaji & Nehad M. Alajez

  3. Department of Laboratory Medicine and Pathology (DLMP), Hamad Medical Corporation (HMC), Doha, Qatar

    Sameera Rashid, Reem Al-Sarraf & Mohammed Akhtar

  4. The Christie NHS Foundation Trust, Manchester, UK

    Sameera Rashid

Authors
  1. Bushra Yasin Abohalawa
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Hibah Shaath
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ramesh Elango
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Radhakrishnan Vishnubalaji
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Sameera Rashid
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Reem Al-Sarraf
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Mohammed Akhtar
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Nehad M. Alajez
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

B. Y. A performed experiments and manuscript writing; H. S., RE., and V. B performed experiments; SR, RA, and MA, pathological assessment and provided clinical specimens and clinical data; NMA conceived study, obtained funding, data analysis, and finalized manuscript.

Corresponding author

Correspondence to Nehad M. Alajez.

Ethics declarations

Ethics approval and consent to participate

The study was performed under ethical approval from HMC (MRC-01-19-142) and from Qatar Biomedical Research Institute (QBRI-IRB 2020-09-035). Consent was not required for this study since the study was conducted on archived FFPE samples.

Consent for publication

All authors have consented to the publication of this manuscript.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abohalawa, B.Y., Shaath, H., Elango, R. et al. MicroRNAome profiling of breast cancer unveils hsa-miR-5683 as a tumor suppressor microRNA predicting favorable clinical outcome. Cancer Cell Int 24, 377 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-024-03550-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-024-03550-8

Keywords

Cancer Cell International

ISSN: 1475-2867

Contact us