Skip to main content
NCBI home page
3 Biotech logoLink to 3 Biotech
. 2021 Sep 23;11(10):446. doi: 10.1007/s13205-021-02994-3

Identification of cancer stemness related miRNA(s) using integrated bioinformatics analysis and in vitro validation

Kumari Sunita Prajapati 1, Mohd Shuaib 1, Prem Prakash Kushwaha 1, Atul Kumar Singh 1, Shsahank Kumar 1,✉
PMCID: PMC8460704  PMID: 34631347

Abstract

The stemness property of cells allows them to sustain their lineage, differentiation, proliferation, and regeneration. MicroRNAs are small non-coding RNAs known to regulate the stemness property of cells by regulating the expression of stem cell signaling pathway proteins at mRNA level. Dysregulated miRNA expression and associated stem cell signaling pathways in normal stem cells give rise to cancer stem cells. Thus, the present study was aimed to identify the miRNAs involved in the regulation of major stem cell signaling pathways. The proteins (n = 36) involved in the signaling pathways viz., Notch, Wnt, JAK-STAT, and Hedgehog which is associated with the stemness property was taken into the consideration. The miRNAs, having binding sites for the targeted protein-encoding gene were predicted using an online tool (TargetScan) and the common miRNA among the test pathways were identified using Venn diagram analysis. A total of 22 common miRNAs (including 8 non-studied miRNAs) were identified which were subjected to target predictions, KEGG pathway, and gene ontology (GO) analysis to study their potential involvement in the stemness process. Further, we studied the clinical relevance of the non-studied miRNAs by performing the survival analysis and their expression levels in clinical breast cancer patients using the TCGA database. The identified miRNAs showed overall poor survival in breast cancer patients. The miR-6844 showed significantly high expression in various clinical subgroups of invasive breast cancer patients compared with the normal samples. The expression levels of identified miRNA(s) were validated in breast normal, luminal A, triple-negative, and stem cells in vitro models using qRT-PCR analysis. Further treatment with the phytochemical showed excellent down regulation of the lead miRNA. Overall the study first time reports the association of four miRNAs (miR-6791, miR-4419a, miR-4251 and miR-6844) with breast cancer stemness.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-021-02994-3.

Keywords: Cancer stemness, miRNA, Breast cancer, Mammosphere, Phytochemical

Introduction

Breast cancer is the most common death-causing disease worldwide. It is reported as the highest occurred cancer in females of all ages in comparison to other cancer. Globally, it is diagnosed 1 in 4 cancers among women (Sung et al. 2021). In India itself, 1.38 million new cases of breast cancer appear each year. About 458,000 deaths occur from breast cancer every year. The breast cancer mortality rate in India is 1.7 times higher than the maternal mortality rate. About 1 in 28 Indian women are expected to develop breast cancer during their lifetime. Different studies showed that radiotherapy, chemotherapy, and surgery have provided some relaxation in breast cancer patients. These breast cancer therapy modalities showed drug resistance, disease relapse, tumor migration/invasion, and poor overall disease-free survival which results to poor therapeutic outcomes in patients. It has been observed that cancer relapse and drug resistance is associated with the occurrence of a small and slow-growing population of cancer cells (known as cancer stem cells) in the given tumor (Kushwaha et al. 2020a, b; Verma et al. 2020). Due to the repeated chemotherapy, most of the cancer cells used to die but the cancer stem cells (a small population) survive and their population increases with the time duration of the treatment (Zhao, 2016). The cancer stem cells acclimatize themselves to the radio/chemotherapy and continue to grow which shows therapy resistance cancer cell population in the tumor. The stemness property in breast cancer stem cells is mainly regulated by its micro-environment which ultimately alters the signaling pathways (Notch, Hedgehog (Hh), Wnt/β-catenin, and JAK/STAT, etc.) associated with cancer stemness (Yang et al. 2020). The stem cell-associated signaling is modulated by the up/down expression of the stemness-related markers such as ALDH1, CD44, SOX2, Nanog, EpCAM and EMT, etc. MicroRNAs are the small non-coding RNA that negatively regulates the gene expression at mRNA level by binding to its complementary sequences at 3’ untranslated regions (3’UTRs). It has been reported that the abrupt miRNA expression is associated with cancer stemness and related effects in breast cancer patients (Asadzadeh et al. 2019). The miRNAs are known to modulate the cancer stem cell proliferation and differentiation and thereby involve in the production of cancer and/or cancer stem cells continuously in the tumor. Different studies reported that the altered expression of miRNAs (miR-206, miR-210, miR-21, miR-221, miR-222, miR-140-5p, etc.) is related to breast cancer stemness and self-renewal efficacy mediated cellular proliferation, metastasis/invasion, and drug resistance. The study reports that these cancer-associated hallmarks were altered by the miRNAs via the modulation of various cancer stem cells-associated signaling pathways (Han et al. 2012; Samaeekia et al. 2017; Tang et al. 2018; Wu et al. 2019). Reduction of stemness and self-renewal properties in pre-clinical breast cancer stem experimental models showed decreased cellular proliferation, induced apoptosis, and reduced metastasis/invasion/drug resistance, and cancer aggressiveness (Sridharan et al. 2019). Clinically it has been observed that miRNA(s)-mediated therapeutics target the stemness property of cancer and thereby reduces drug resistance (Bonneau et al. 2019). Literature shows huge lacuna about the clinically approved therapeutic miRNAs Therefore, the identification of miRNAs involved in the pathophysiology of breast cancer stemness is the need of time. Plant-based compounds serve as a good source of anti-cancer and anti-drug resistance agents due to their large variety of molecular structures (Mishra et al. 2013; Kumar et al. 2013; Kumar et al. 2014a, b; Kushwaha et al. 2019, 2021; Kushwaha et al. 2020a, b). Thus, the present study aimed to identify the miRNAs having the potential to target the cancer stem cell signaling pathway protein-encoding genes. Further, an attempt was made to validate the identified miRNAs using in vitro model of breast cancer stem cells. Moreover, the effect of anticancer phytochemical on the miRNA(s) expression pattern was also aimed.

Material and methods

Literature-based identification of major cancer stem cell signaling pathways and associated proteins

An exhaustive literature was searched to obtain major cancer stem cell signaling pathways and associated proteins using different search engine platforms including PubMed (Gupta et al. 2021). “Cancer stem cell signaling” keyword was used to search the literature in various databases. The signaling pathways associated with stemness were considered based on the expert recommendation available in the literature. Further we selected only those signaling proteins which have been experimentally proved to be associated with the respective pathway and cancer stemness. The proteins which were reported for their direct involvement in the respective signaling pathways were only considered for the study.

Prediction of miRNAs having binding potential to identified protein encoding gene

In the present study, we predicted the miRNAs having potential to bind with the test protein encoding genes using TargetScan online tool (http://www.targetscan.org/vert_72/). The tool allows us to predict the miRNAs which targets a given protein encoding gene. The tool utilizes the related algorithm and statistics to produce the result (Agarwal et al. 2015).

Identification of common miRNAs

Venny is an interactive web tool which compares the lists of input information and provides results in the form of Venn’s diagram (Oliveros 2007). Venny 2.1 allows finding common elements/entry among four list uploaded with the respective columns available on the webpage of the program. We submitted the lists of identified miRNAs involve in stem cell signaling pathways into Venny list of column to get the common miRNAs. Due to limitations in the online Venny Diagram tools (at a time only four groups are allowed), we prepared different sets of signaling pathway-associated miRNAs (Hedgehog-JAK/STAT; Hedgehog-NOTCH; Hedgehog-Wnt; JAK/STAT-NOTCH; JAK/STAT-Wnt; NOTCH-Wnt; Hedgehog-JAK/STAT-NOTCH; Hedgehog-JAK/STAT-Wnt; Hedgehog-NOTCH-Wnt; JAK/STAT-NOTCH-Wnt) to finally achieve the common miRNAs among the test signaling pathway.

MicroRNAs target gene prediction and miRNA-mRNA hybridization energy calculation

The target genes of common miRNAs were identified using online software miRSystem (Lu et al. 2012). MiRSystem is a useful database which allows us to predict the target genes for a given set of miRNAs. It is a complete package of seven known miRNA target gene prediction programs including miRanda, DIANA, TargetScan, PicTar, miRBridge, rna22, and PITA. The target genes with cut off value Hit > 1 conditions were considered for the further analysis. The target binding site and hybridization energy (∆G) of miRNA-mRNA interaction was calculated using the STarMirDB online tool available in the Sfold server (Shuaib et al. 2021).

Functional annotation and pathway enrichment analysis

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene ontology (GO) enrichment analysis of predicted targets of identified common miRNAs was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/tools.jsp). DAVID is a free online bioinformatics program developed by Immunopathogenesis and Bioinformatics. It provides the systematic and integrative analysis of large data of genes (Huang et al. 2008, 2009). KEGG is an integrated database contains information on the allocation of the genome sequence to specific pathways (Kanehisa 2002). Functional annotation tool available in DAVID program was used to identify the functional enrichment of the target genes in terms of KEGG pathway and Gene Ontology (genes involved in Biological function, cellular components and molecular function).

Survival analysis of miRNAs

Kaplan–Meier Plotter Database (KMPD) is an online survival analysis tool capable to validate the association of test miRNAs with the overall survival (OS) in cancer patients. The clinical breast cancer patient samples present in the TCGA database were used to draw the survival plot. The hazard ratio (HR) and the significant p value was calculated by the plotter. The tool utilizes the Kaplan–Meier method to plot the survival graphs (Lánczky et al. 2016).

miRNA expression in clinical samples of breast cancer patients

UALCAN is a comprehensive and interactive web tool utilized for the analysis of cancer OMICS data including TCGA datasets. The tool provides us the validation of candidate gene and identification of biomarkers in cancer patient samples (Chandrashekar et al. 2017). Thus, the UALCAN database (http://ualcan.path.uab.edu/analysis.html) was utilized to retrieve the expression level of lead miRNA(s) in different clinic-pathological groups of invasive breast carcinoma patient samples available in the Cancer Genome Atlas.

Breast cancer cell line and monolayer cell culture

Breast normal (MCF-10A) and cancer cells (MCF-7 and MDA-MB-231) were procured from ATCC, USA and NCCS, Pune respectively. MCF-10A cells were cultured in DMEM/Ham's F-12 (GIBCO-Invitrogen, Carlsbad, CA) supplemented with 5% horse serum, 20 ng/ml epidermal growth factor (EGF), 0.5 µg/ml hydrocortisone, 100 ng/ml cholera toxin, 10 µg/ml insulin, and 100 U/ml of penicillin, and 100 μg/ml of streptomycin. All the growth factors were purchased from Sigma (St. Louis, MO, USA). MCF-7 and MDA-MB-231 cell lines were cultured in DMEM supplemented with 10% (v/v) heat inactivated fetal bovine serum (FBS), 2 mM l-glutamine, 100 U/ml of penicillin, and 100 μg/ml of streptomycin. Cells were maintained at 37 °C in an incubator having 5% CO2 under a humid environment (Nishi et al. 2014; Kumar et al. 2014a, b).

Mammosphere formation and phytochemical treatment

Mammosphere formation potential of breast cancer cells in the presence of specific media and growth factors is a well known in vitro model to assess the stemness and self-renewal initiation potential of therapeutic molecules. Adherent MCF-7 cells were detached using tyrpsin and the cells (density of 1 × 104 cells/well) were seeded in a six-well ultra-low-attachment plates (Corning, Tewksbury, MA, US) utilizing the serum-free DMEM Ham’s F12 nutrient mixture (1:1 v/v). The cell growth medium was supplemented with the B-27 supplement (2%), fibroblast growth factor (10 ng/ml), and epidermal growth factor (20 ng/ml). Further, the cells were incubated at 37 °C under a humid environment in an incubator for 5 days at 5% CO2 to allow the formation of mammosphere (Kushwaha et al. 2020a, b). To assess the efficacy of phytochemical (Withaferin A) on lead miRNA expression pattern in mammosphere the MCF 7 cells were pretreated (2 µM Withaferin A) and allowed to form mammosphere for 5 days. Withaferin-A (WA) was procured from (Cat# 89910-10MG, Sigma-Aldrich Switzerland) and dissolved in DMSO as per specifications. The non-treated MCF-7 cells were allowed to form mammosphere, considered as control group.

Total RNA Isolation and cDNA synthesis

Total RNA including small RNAs (miRNAs) from cultured MCF-10A, MCF-7, MDA-MB-231 (non-treated and WA-treated) and MCF-7 derived mammosphere (non-treated and WA-treated) were isolated using mirVana™ miRNA isolation kit (Catalog# P/N 15604, Invitrogen Thermo Fisher Scientific) according to manufacturer’s instructions with slight modifications. Briefly, the test cells (2D and 3D) were centrifuged at 200 RPM up to 5 min at 4 °C to get the pellets. The pellets were lysed with 300 µl of lysis/binding buffer and the miRNA was isolated as per manufacture instructions. The total RNA Quality was analyzed using NanoDrop (ND)-2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA). Samples with absorption ranges from 1.90 to 2.0 at 260/280 nm ratio were considered for reverse-transcribed to cDNA. Total RNA of 1 μg containing miRNA fractions were polyadenylated by poly (A) polymerase enzyme and were reverse-transcribed into cDNA by reverse transcriptase enzyme using miRNA first strand cDNA synthesis kit (Cat: 683183, Takara) at 37 °C for 1 h in the PCR machine followed by enzymes inactivation at 85 °C. The cDNA synthesis for WA treated MDA-MB-231 and MCF-7-derived mammosphere groups was performed as per given methodology.

MiRNA expression analysis using qRT-PCR

Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to study the differential expression of lead miRNA(s) in MCF-10A (normal breast cell) vs MCF-7 (luminal breast cancer cell); MCF-10A vs MDA-MB-231 (TNBC cell); MCF-7 vs MCF-7-derived mammosphere study groups. The miRNA(s) expression was also studied in phytochemical treated MDA-MB-231 and MCF-7-derived mammosphere compared with the respective non-treated cells. The miRNA(s) expression pattern was studied using universal SYBR green master mix 2 × with forward and reverse specific primers using a method of comparative CT (∆∆CT) value. Final volume of each reaction was maintained at 12.5 μL containing 1 µl of cDNA. 6.25 µL of 2 × SYBR green PCR master mixes, 0.25 µL of each primer, and 4.75 µl of nuclease free RT-PCR grade water was added. CT value of miRNA(s) were determined by following 95 °C for 3 min (initializing step 1), denaturation at 95 °C for 10 s, annealing 60 °C for 30 s. and extension 72 °C for 30 s. A total of 45 cycles in QuantStudio(R) 3 Real-Time PCR machine (Applied Biosystems, Thermo Fisher Scientific USA) was kept. CT values of miRNAs were normalized using RNAU6 to confirm the accuracy of results. The cycle of threshold (Ct) 2-dd CT method was used for fold change expression of miRNAs. The list of primers used and their sequence are provided in supplementary table 1.

Statistical analysis

All the experiments were performed in triplicates and the data were analyzed and expressed as the mean ± SD. Statistically significant p value < 0.05 was used for all the data.

Result and discussion

Identification of cancer stem cell signaling-associated proteins

After literature review the four signaling pathways viz., Notch, Wnt, Hedgehog, and JAK-STAT were considered for further study to identify the associated proteins of the signaling. An exhaustive literature review revealed the 35 proteins having direct involvement in the cancer stemness. A total of 09 (ADAM11, ADAM12, ADAM15, ADAM17, γ-secretase, NOTCH1, NOTCH2, NOTCH3, NOTCH4), 11 (APC, AXIN1, β-catenin, CK1, DBL1, DBL2, FRIZZLED2, FRIZZLED7, GSK3β, LRP5, and LRP6), 05 (GLI1, GLI2, GLI3, PTCH1, and SMO), and 11 (JAK1, JAK2, JAK3, SOCS, STAT1, STAT2, STAT3, STAT4, STAT5A, STAT5B, and STAT6) proteins were identified to be involved in the Notch, Wnt, Hedgehog, and JAK-STAT signaling pathways, respectively.

Identification of miRNAs having potential to bind identified protein encoding genes

The online tool (TargetScan)-mediated identification of the miRNAs having the potential to bind identified protein-encoding genes (n = 36) resulted in the overall 2087 miRNAs. A total of 564 miRNAs were found to bind with ADAM11, ADAM12, ADAM15, ADAM17, γ-secretase, NOTCH1, NOTCH2, NOTCH3, and NOTCH4 mRNAs, involved in the notch signaling pathway (supplementary table 2). A total of 707 miRNAs bound with different target genes (APC, AXIN1, β-catenin, CK1, DBL1, DBL2, FZD2, FZD7, GSK3β, LRP5 and LRP6) involved in the Wnt signaling pathway (supplementary table 2). Similarly, a total of 539 and 214 miRNAs showed binding with the target genes involved in JAK-STAT (JAK1/2/3, SOCS, STAT1/2/3/4, STAT5A/5B, and 6) and Hedgehog GLI1/2/3, PTCH1, and SMO) signaling pathway, respectively (supplementary table 2). The overall miRNAs identified for the respective proteins associated with the test signaling pathways are depicted in (Fig. 1A). The overall strategy followed to narrow down the number of miRNAs is depicted in (Fig. 1B).

Fig. 1.

Fig. 1

Open in a new tab

miRNA filtering criteria and distribution among test signaling pathways. A Work flow of the filtration of miRNAs to get the lead. Numbers in the parenthesis showed total number of miRNAs predicted for a given signaling pathway test proteins. B Distribution of predicted miRNAs percentage targeting the four stem cell signaling pathway proteins (n = 35) mRNA. HH Hedgehog signaling; JK JAK-STAT signaling, NT Notch signaling, WNT Wnt signaling

Identification of common miRNAs

Venn diagram analysis was used to find the common miRNAs among the identified miRNAs in “Identification of miRNAs having potential to bind identified protein encoding genes” for the test signaling pathways (NOTCH, Hedgehog, Wnt, and JAK-STAT). The different sets as mentioned in the methodology section were utilized to get the common miRNAs in the test signaling pathway. The set Hedgehog-JAK/STAT, Hedgehog-NOTCH, Hedgehog-Wnt, JAK/STAT-NOTCH, JAK/STAT-Wnt, NOTCH-Wnt, Hedgehog-JAK/STAT-NOTCH, Hedgehog-JAK/STAT-Wnt, Hedgehog-NOTCH-Wnt, and JAK/STAT-NOTCH-Wnt showed 20, 20, 35, 78, 83, 158, 16, 19, 17, and 76 miRNAs, respectively (supplementary table 2). The Venn diagram analysis of the final set, i.e., Hedgehog-JAK/STAT-NOTCH-Wnt showed a total of 22 miRNAs common among the test signaling pathway. The names of identified miRNAs are depicted in (Fig. 2). The target binding site and hybridization energy (∆G) of the common miRNAs (n = 22) for different test signaling proteins (n = 36) were identified as per methodology given in “MicroRNAs target gene prediction and miRNA-mRNA hybridization energy calculation”. The name of miRNA(s) having the potential to bind the target gene(s) and their respective hybridization energies are tabulated in Table 1. The miRNA-mRNA pair showing the highest hybridization energy was visualized for each test target in Table 1. The results indicate that has-miR-6791-5p showed the highest binding with Hedgehog and JAK-STAT signaling genes (Gli-1 and STAT2, respectively) (Table 1). Similarly, has-miR-4728-5p and has-miR-1227-5p showed potential binding with the Notch and Wnt signaling pathway genes (ADAM1 and Frizzled 7, respectively) (Table 1). The result indicates that has-miR-6844 showed potential binding (≈ − 20 kcal/mole) PITCH1, JAK2, ADAM12, and LRP5 genes are involved in Hedgehog, JAK-STA, Notch, and Wnt signaling pathways, respectively (Table 1).

Fig. 2.

Fig. 2

Open in a new tab

Venn diagram analysis mediated identification of common miRNAs showing binding with the targeted protein mRNAs. Blue-Hedgehog; Yellow-JAK-STAT; Green-Notch and Pink-Wnt signaling pathway columns

Table 1.

Target sites and hybridization energies of common miRNA(s) and test gene(s) interaction

graphic file with name 13205_2021_2994_Tab1a_HTML.jpg

graphic file with name 13205_2021_2994_Tab1b_HTML.jpg

Open in a new tab

MicroRNA(s) target prediction and KEGG pathway analysis

A total of 2402 target genes of the common miRNAs (n = 22) with a cut-off value of Hit > 1 were identified using the methodology provided in “MicroRNAs target gene prediction and miRNA-mRNA hybridization energy calculation”. The list of genes is given in supplementary table 3. The KEGG pathway analysis of the identified target genes was predicted using the online tool mentioned in the methodology “Functional annotation and pathway enrichment analysis”. More than 100 pathways were identified which showed enrichment of the common miRNAs target genes. A total of 73 statistically significant (p < 0.05) pathways are tabulated in (Table 2). The percentage count and number of target genes enriched in different pathways were in the range of 0.1–4.04 and 8–90, respectively (Table 2). The result showed that the target genes were enriched in cancer stem cell-associated pathways. Besides, some molecular events such as ubiquitin-mediated proteolysis, axon guidance, endocytosis, transcriptional misregulation in cancer, molecular regulation of stem cells pluripotency were also found to be associated with the enrichment of miRNAs target genes (Table 2). It should be noted that all the major signaling pathways considered in the initial stage of the present study (Hedgehog, Notch, and Wnt) have been found to be associated with the common miRNAs target genes enrichment. The JAK-STAT pathway was also the target of common miRNAs but the enrichment of the target genes in the pathway was slightly non-significant (not mentioned in the table). In separate studies, it has been reported that axon guidance and endocytosis are involved in breast cancer stemness and associated drug resistance (Chédota et al. 2005; Chen et al. 2014; Plalaniyandi et al. 2012). Recently, it has been reported that the signaling pathways such as Wnt, Notch, JAK-STAT, Hedgehog, MAPK, AMPK-PI3-Akt, Estrogen, and Hippo signaling pathways are involved in the maintenance and stemness property of the breast cancer stem cells (Kushwaha et al. 2020a, b; Song and Farzaneh, 2021). These signaling pathways have been reported to modulate by miRNAs (Kushwaha et al. 2020a, b).

Table 2.

KEGG pathway analysis of all the lead miRNAs

Pathway Count % Count p value
hsa04120:Ubiquitin mediated proteolysis 46 2.07 4.04E − 10
hsa04360:Axon guidance 42 1.89 4.40E − 09
hsa05200:Pathways in cancer 90 4.04 9.47E − 09
hsa04144:Endocytosis 60 2.70 2.13E − 07
hsa04068:FoxO signaling pathway 38 1.71 2.04E − 06
hsa05202:Transcriptional misregulation in cancer 43 1.93 5.97E − 06
hsa04024:cAMP signaling pathway 47 2.11 2.07E − 05
hsa04723:Retrograde endocannabinoid signaling 28 1.26 8.99E − 05
hsa04152:AMPK signaling pathway 32 1.44 9.38E − 05
hsa04550:Signaling pathways regulating pluripotency of stem cells 35 1.57 9.73E − 05
hsa04022:cGMP-PKG signaling pathway 38 1.71 1.10E − 04
hsa04720:Long-term potentiation 21 0.94 1.14E − 04
hsa04724:Glutamatergic synapse 30 1.35 1.31E − 04
hsa04010:MAPK signaling pathway 53 2.38 1.97E − 04
hsa05205:Proteoglycans in cancer 44 1.98 2.52E − 04
hsa04917:Prolactin signaling pathway 21 0.94 3.40E − 04
hsa04919:Thyroid hormone signaling pathway 29 1.30 3.75E − 04
hsa04015:Rap1 signaling pathway 45 2.02 3.90E − 04
hsa04725:Cholinergic synapse 28 1.26 4.82E − 04
hsa04931:Insulin resistance 27 1.21 7.21E − 04
hsa05212:Pancreatic cancer 19 0.85 8.30E − 04
hsa04921:Oxytocin signaling pathway 34 1.53 8.43E − 04
hsa04520:Adherens junction 20 0.90 9.51E − 04
hsa04014:Ras signaling pathway 46 2.07 0.00106
hsa04151:PI3K-Akt signaling pathway 64 2.88 0.00128
hsa04713:Circadian entrainment 24 1.08 0.00132
hsa05166:HTLV-I infection 50 2.25 0.00133
hsa04666:Fc gamma R-mediated phagocytosis 22 0.99 0.00137
hsa05231:Choline metabolism in cancer 25 1.12 0.00138
hsa04722:Neurotrophin signaling pathway 28 1.26 0.00171
hsa04924:Renin secretion 18 0.81 0.00189
hsa04012:ErbB signaling pathway 22 0.99 0.00219
hsa04960:Aldosterone-regulated sodium reabsorption 13 0.58 0.00227
hsa04916:Melanogenesis 24 1.08 0.00271
hsa05014:Amyotrophic lateral sclerosis (ALS) 15 0.67 0.00273
hsa05210:Colorectal cancer 17 0.76 0.00350
hsa04340:Hedgehog signaling pathway 10 0.45 0.00438
hsa04728:Dopaminergic synapse 28 1.26 0.00449
hsa04114:Oocyte meiosis 25 1.12 0.00518
hsa05214:Glioma 17 0.76 0.00576
hsa05211:Renal cell carcinoma 17 0.76 0.00674
hsa05031:Amphetamine addiction 17 0.76 0.00674
hsa04910:Insulin signaling pathway 29 1.30 0.00676
hsa04390:Hippo signaling pathway 31 1.39 0.00705
hsa05100:Bacterial invasion of epithelial cells 19 0.85 0.00725
hsa04350:TGF-beta signaling pathway 20 0.90 0.00739
hsa04810:Regulation of actin cytoskeleton 40 1.80 0.00770
hsa05223:Non-small cell lung cancer 15 0.67 0.00823
hsa05161:Hepatitis B 29 1.30 0.01324
hsa04930:Type II diabetes mellitus 13 0.58 0.01392
hsa05220:Chronic myeloid leukemia 17 0.76 0.01575
hsa05410:Hypertrophic cardiomyopathy (HCM) 18 0.81 0.01577
hsa04070:Phosphatidylinositol signaling system 21 0.94 0.01892
hsa04726:Serotonergic synapse 23 1.03 0.01976
hsa04914:Progesterone-mediated oocyte maturation 19 0.85 0.02207
hsa04261:Adrenergic signaling in cardiomyocytes 27 1.21 0.02226
hsa04310:Wnt signaling pathway 27 1.21 0.02226
hsa05215:Prostate cancer 19 0.85 0.02460
hsa04920:Adipocytokine signaling pathway 16 0.72 0.02599
hsa04150:mTOR signaling pathway 14 0.63 0.02611
hsa05218:Melanoma 16 0.72 0.02926
hsa05414:Dilated cardiomyopathy 18 0.81 0.03122
hsa05206:MicroRNAs in cancer 48 2.16 0.03236
hsa05032:Morphine addiction 19 0.85 0.03349
hsa04330:Notch signaling pathway 12 0.54 0.03356
hsa04710:Circadian rhythm 9 0.40 0.03449
hsa04380:Osteoclast differentiation 25 1.12 0.03685
hsa05412:Arrhythmogenic right ventricular cardiomyopathy (ARVC) 15 0.67 0.03751
hsa04915:Estrogen signaling pathway 20 0.90 0.03899
hsa04972:Pancreatic secretion 19 0.85 0.04061
hsa05221:Acute myeloid leukemia 13 0.58 0.04364
hsa04320:Dorso-ventral axis formation 8 0.36 0.04559
hsa04141:Protein processing in endoplasmic reticulum 30 1.35 0.05048
Open in a new tab

Gene ontology analysis

A total of 2402 predicted target genes of the common miRNAs (n = 22, depicted in Fig. 2) were subjected to gene ontology using DAVID online tool as per methodology given in “Functional annotation and pathway enrichment analysis”. The GO analysis revealed the molecular function (MF), biological processes (BC), and cellular components (CC) in which the target genes were enriched. Initially, lots of BC, CC, and MF terms were obtained, which were filtered using p < 0.05 criteria. The significant GO terms were further arranged according to the number of genes enriched and the data were manually refined as per the present study design. Results showed that test miRNAs targeting gene were enriched in response to the drug, cellular adhesion/division, apoptosis, protein ubiquitination/phosphorylation, transcription regulation and signal transduction for biological process term of GO; protein Ser/Thr kinase activity, transcription/protein kinase/ RNA/DNA/ and metal-binding activity for molecular function term of GO; cell–cell adherence, cell junction, cytoskeleton, cell-surface for cellular component term of GO (Fig. 3A–C).

Fig. 3.

Fig. 3

Open in a new tab

Gene Ontology analysis of the identified common miRNAs target genes. GO term analysis was performed using online DAVID tool. A Biological function term, B cellular component term, C molecular function term. GO gene ontology, BP biological processes, CC cellular components, MF molecular functions

p value < 0.05 was taken as a threshold value to obtain the statistically significant GO and KEGG pathway enrichment results. GO analysis showed that the targeted genes of the common expressed miRNAs were enriched in the positive regulation of transcription from RNA polymerase II promoter, negative regulation of transcription from RNA polymerase II promoter, DNA-template, signal transduction represent BP. The molecular functions (MF) of these genes included protein/sequence-specific DNA/zinc ion/ ATP and RNA binding, transcription factor activity. The CC of the target genes involved in nucleus, nucleoplasm, cytoplasm, cytosol, membrane, golgi apparatus, perinuclear region of cytoplasm, golgi membrane, plasma membrane, endoplasmic reticulum, and intracellular.

Selection of lead miRNA(s) for in vitro validation

A literature survey was performed to identify the miRNAs which are not yet studied in breast cancer as per methodology provided in the “Breast cancer cell line and monolayer cell culture” and “Mammosphere formation and phytochemical treatment”. The literature survey resulted in the identification of eight miRNAs (hsa-miR-4251, hsa-miR-4679, hsa-miR-6077, hsa-miR-6791, hsa-miR-6844, hsa-miR-4419a, hsa-miR-4531, and hsa-miR-4292) which are not yet been reported for their association with breast cancer. It should be noted that the literature on other cancer was only found for the miR-6077. Overexpression of miR-6077 was associated with the chemotherapy sensitization efficacy in patient-derived lung adenocarcinoma cells by reducing the GLUT1 transporter expression (glucose transporter 1) (Ma et al. 2019). Thus the eight non-studied miRNAs were selected for further study.

Survival analysis of the identified lead DEMs

Further, we narrowed down the list of miRNAs on the basis of their clinical association with breast cancer. For this, we performed the survival analysis of breast cancer patients correlated with the expression profile of the miRNAs (hsa-miR-4251, hsa-miR-4679, hsa-miR-6077, hsa-miR-6791, hsa-miR-6844, hsa-miR-4419a, hsa-miR-4531, and hsa-miR-4292). The survival analysis curve for these miRNAs was plotted by the Kaplan–Meier plotter utilizing the breast cancer patient (n = 1062) data available in the TCGA database. The results showed that the hazard ratio (HR) of the test miRNAs was in the range of 1.94–2.23. Moreover, the survival analysis was highly statistically significant with a p value < 0.0001 (Fig. 4). It should be noted that the survival analysis for the three miRNAs viz., hsa-miR-4419a, hsa-miR-4531, and hsa-miR-4292 were not found due to a lack of information in the breast cancer patient data available in the TCGA database. Different studies showed the association of abrupt expression of the miRNAs with the overall and disease-free survival in breast cancer patients. The up-expression of miR-9, miR-221, miR-21, and miR-142 in breast cancer patients and pre-clinical experimental model showed their association with the lower overall survival and disease-free survival in breast cancer patients. These miRNAs also showed their association with the development of stemness property-mediated increased metastatic potential and EMT activation. It has been reported that the high expression of miR-27a positively regulates breast cancer stem cell formation in in vitro and in vivo experimental models (Qian et al. 2009; Isobe et al. 2014; Cheng et al. 2018; Tang et al. 2014). Further, the studies correlated the findings with the previously published report showing the association of the miRNA with poor survival in breast cancer patients. In the present study, the survival analysis of the test miRNAs showed that their up-expression is associated with poor survival in breast cancer patients. Based on these facts we selected the five clinically relevant miRNAs viz., hsa-miR-4251, hsa-miR-4679, hsa-miR-6077, hsa-miR-6791, and hsa-miR-6844 for further study.

Fig. 4.

Fig. 4

Open in a new tab

Survival analysis of the lead non-studied miRNAs in breast cancer patient’s data available in TCGA database. A Survival analysis of hsa-miR-4251, B survival analysis of hsa-miR-4679, C survival analysis of hsa-miR-6077, D survival analysis of hsa-miR-6791 and E survival analysis of hsa-miR-6844. The survival analysis for the hsa-miR-4419a, hsa-miR-4531, and hsa-miR-4292 were not found in the database

miRNA expression profile in breast cancer patients

Further to explore the clinical relevance of the identified miRNAs (hsa-miR-4251, hsa-miR-4679, hsa-miR-6077, hsa-miR-6791, and hsa-miR-6844) we performed their expression analysis in breast cancer patient data available in the TCGA database as per the methodology provided in “miRNA expression in clinical samples of breast cancer patients”. The expression profile of the test miRNAs was studied in breast invasive carcinoma (n = 749) and normal tissue (n = 76) samples. Out of 5 miRNAs, only hsa-miR-6844 showed the expression in the database while the expression pattern of the other four miRNAs was not found. The expression pattern of the lead miRNA hsa-miR-6844 was studied in different clinical groups of breast cancer patients such as normal vs primary tumor/nodal metastasis/ cancer stages/race/tumor histology grade/cancer sub-classes/ menopause stage and TP53 mutation status. Results showed that miR-6844 is highly expressed in primary breast tumors (Fig. 5A). The miRNA was associated with the nodal metastasis in the sequence of N2 > N0 > N1 (Fig. 5B). miR-6844 expression was significantly reduced from lower to higher (S1 to S4) cancer stage (Fig. 5C). Comparatively higher expression of miR-6844 was found in the Asian breast cancer patient population (Fig. 5D). The miRNA expression among the various histological grades of breast cancer showed a significantly higher association with the MP, MD, and IDC stages (Fig. 5E). A significant difference in the expression pattern of miRNA among sub-type of breast cancer was observed (Fig. 5F). The expression pattern of miR-6844 was comparatively higher in peri-menopausal breast cancer patients in comparison to pre and post-menopausal individuals (Fig. 5G). This information indicates the possible prognostic value of the miR-6844 in menopausal women for the occurrence of breast cancer. Further, the miRNA expression pattern study in breast cancer patients provided important information that miR-6844 miRNA is associated with the patients having TP53 mutations (Fig. 5H). It should be noted that to date there is no study reported on mir-6844 either in breast and or other disease models.

Fig. 5.

Fig. 5

Open in a new tab

Expression pattern of hsa-miR-6844 in different type of clinical groups of the breast invasive carcinoma. A Normal tissue vs breast primary tumor, B normal tissue vs nodal metastasis status, C normal tissue vs individual cancer stages, D normal tissue vs patients’s race, E normal tissue vs breast cancer tumor histology grade, F normal tissue vs breast cancer sub-classes, G normal tissue vs menopause stage, H normal tissue vs TP53 mutation status. The data are retrieved from TCGA database

In vitro validation of the miRNA expression profile in breast cancer stem like cells (mammosphere)

Based on the data obtained in the survival analysis and expression pattern in breast cancer patient samples, we considered miR-6844 as the lead miRNA. We studied the expression pattern of the lead miRNA and other non-studied miRNAs (hsa-miR-6791, hsa-miR-4679, hsa-miR-4419a, hsa-miR-4251, and hsa-miR-4531) in in vitro model of breast cancer stem cells (also known as mammosphere) compared with the MCF-7 monolayer cells. As in the cancer-subtype miRNA expression analysis (Fig. 5F) the result showed there was no significant difference in the expression pattern of miR-6844 among normal and luminal sub-type samples. Thus we utilized the luminal breast cancer in vitro model (MCF-7 cell line) and MCF-7-derived mammosphere (breast cancer stem-like cells) to study the differential expression pattern of the lead miRNA. The expression pattern of miR-6844 showed it is down expression in in vitro luminal breast cancer model (MCF-7 cell line) (Fig. 6A). Our result was in line with the expression pattern of the miRNA in breast cancer luminal patients (Fig. 5F). The hsa-miR-6791, hsa-miR-4419a, hsa-and miR-4251 showed significantly higher expression in the mammosphere while hsa-miR-4679 and hsa-miR-4531 did not show expression difference among the test groups (Supplementary Fig. 1). The lead miRNA-6844 showed significantly very high expression in MCF-derived mammosphere in comparison to MCF-7 cells (Fig. 6B). Literature showed that the abrupt expression of miRNAs was found in breast cancer stem cells in comparison to normal breast cells (Isobe et al. 2014). Literature is silent about the role of identified miRNAs (miR-4251, miR-6791, and miR-4419a) in breast cancer stem cell formation. The present study indicates the potential involvement of miRNA-6844 in the breast cancer stemness process. The mechanistic role of the miRNA should be studied in in vitro and in vivo breast cancer stemness models.

Fig. 6.

Fig. 6

Open in a new tab

Expression pattern of miR-6488 in different experimental groups and effect of phytochemical on mammosphere. A miRNA-6844 expression in breast normal cells compared with the luminal and triple negative breast cancer cells (MDA-MB-231). B miRNA-6844 expression in luminal breast cancer cell and MCF-7 cells derived breast cancer stem like cells (mammosphere), C effect of Withaferin A on miR-6844 expression in TNBC cells and MCF-7 mammospheres compared with the respective non-treated cells. D-I MCF-7 cells derived mammosphere. D-II Stem cell formation reduction potential of Withaferin-A treatment. 2D monolayer culture, 3D sphere culture, T treated

Effect of Withaferin A on the expression profile of lead miRNA

It has been reported that miRNA plays a critical role in the maintenance of cancer stem cell stemness and self-renewal properties. Different studies showed that phytochemicals have the potential to inhibit the stemness and self-renewal property of cancer stem cells by modulating miRNA expression (Naujokat and McKee 2021). Withania somnifera (Solanaceae) is an Indian medicinal plant possesses Withaferin A (steroidal lactone) as a pharmacologically active phytocompound. Withaferin A (WA) showed anticancer activity in various in vitro and in vivo breast cancer experimental models. The preclinical studies have reported that the WA inhibits breast cancer stemness, self-renewal, and associated drug-resistance via targeting different signaling pathways (Nishi et al. 2014; Kim et al. 2021). Phytochemicals have potent anticancer activity and exert lesser side effects (Kumar et al. 2013). Thus we considered WA (as potential antitumor and breast cancer stem cell targeting phytochemical) to study its miRNA modulation potential in breast cancer stem-like cells. The MCF-7 mammospheres cultured maintained and treated as per methodology provided in “Mammosphere formation and phytochemical treatment”. The qRT-PCR analysis of the WA-treated mammosphere showed a significantly altered expression profile of miR-6844 (Fig. 6C). The WA treatment showed the reduction of mammosphere formation in MCF-7-derived mammosphere (Fig. 6D-I, D-II). The result showed that WA not even decreases the miR-6844 expression but down-regulated it in comparison to its expression in MCF-7 non-stem like cells (2D culture). The results corroborate with the previous findings which reported the miRNA modulation and cancer stem cell inhibition potential of Withaferin-A (Shuaib et al. 2021). Still, in-depth studies are required to find the miRNA-based therapeutic potential of WA in breast cancer stem-like cells. It has been reported that miRNA-mediated stem cell signaling pathways modulation play important role in cancer stemness. In the present study, WA treated showed the down-regulation of miR-6844 and mammosphere formation inhibition potential. The study indicates that WA has the potential to target breast cancer stemness by modulation of miRNA expression.

Conclusion

In the present study, we identify the common miRNAs having binding potential with the major stem cell signaling pathway (Notch, Wnt, JAK-STAT, and Hedgehog) protein-encoding genes. (n = 36). The non-studied miRNAs subjected to the KEGG pathway and Gene Ontology analysis revealed their probable association with the cancer stem cell signaling pathways (Notch, Wnt, Hedgehog, PI3K-Akt, MAPK, AMPK, etc.) and GO terms (cell surface, plasma membrane, protein Ser/Thr, nuclear and cytoplasmic event, etc.). The miR-6791, miR-4419a, miR-4251 and miR-6844 showed clinical relevance in terms of poor survival and increased expression in invasive breast carcinoma patient samples available in the TCGA database. These miRNAs showed significant up-expression in the in vitro validation experiment performed in the breast cancer stem cell model (mammosphere). Further, the phytochemical mediated alteration of the lead miRNA-6844 in the mammosphere indicates the miRNA could be targeted by the therapeutic agents and could act as a future therapeutic target for cancer stemness. Based on these results we highly recommend that the identified miRNAs should be studied in in vitro and in vivo breast cancer stem cell models.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 57 kb) (57.7KB, docx)
Supplementary file2 (XLSX 152 kb) (152.2KB, xlsx)

Acknowledgements

KSP acknowledge Department of Biotechnology, India for providing DBT-Senior Research Fellowship. MS acknowledges Indian Council of Medical Research, India for providing ICMR-Senior Research fellowship [File No. 5/3/8/80/ITR-F/2020-ITR]. AKS acknowledges CSIR, India for providing CSIR-Senior Research Fellowship. SK acknowledges Department of Science and Technology, India for providing financial support in the form DST-SERB Grant [EEQ/2016/000350]. SK also acknowledges DST-India for providing Departmental grant to the Department of Biochemistry, Central University of Punjab, Bathinda, India in the form of DST-FIST grant.

Author contribution

SK conceptualized, supervised, the study and wrote the original draft of the manuscript; KSP, MS, PPK and AKS were curated the data and analyze them.

Declarations

Conflict of interest

The authors declare no conflict of interest.

Ethics approval and consent to participate

Not applicable.

References

  1. Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. Elife. 2015;12(4):e05005. doi: 10.7554/eLife.05005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Asadzadeh Z, Mansoori B, Mohammadi A, Aghajani M, Haji-Asgarzadeh K, Safarzadeh E, Mokhtarzadeh A, Duijf PHG, Baradaran B. microRNAs in cancer stem cells: biology, pathways, and therapeutic opportunities. J Cell Physiol. 2019;234:10002–10017. doi: 10.1002/jcp.27885. [DOI] [PubMed] [Google Scholar]
  3. Bonneau E, Neveu B, Kostantin E, Tsongalis GJ, De Guire V. How close are miRNAs from clinical practice? A perspective on the diagnostic and therapeutic market. EJIFCC. 2019;30:114–127. [PMC free article] [PubMed] [Google Scholar]
  4. Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK, Varambally S. UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 2017;19:649–658. doi: 10.1016/j.neo.2017.05.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Chédotal A, Kerjan G, Moreau-Fauvarque C. The brain within the tumor: new roles for axon guidance molecules in cancers. Cell Death Differ. 2005;12:1044–1056. doi: 10.1038/sj.cdd.4401707. [DOI] [PubMed] [Google Scholar]
  6. Chen WX, Liu XM, Lv MM, Chen L, Zhao JH, Zhong SL, Ji MH, Hu Q, Luo Z, Wu JZ, Tang JH. Exosomes from drug-resistant breast cancer cells transmit chemoresistance by a horizontal transfer of microRNAs. PLoS ONE. 2014;9:e95240. doi: 10.1371/journal.pone.0095240. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cheng CW, Yu JC, Hsieh YH, Liao WL, Shieh JC, Yao CC, Lee HJ, Chen PM, Wu PE, Shen CY. Increased cellular levels of MicroRNA-9 and MicroRNA-221 correlate with cancer stemness and predict poor outcome in human breast cancer. Cell Physiol Biochem. 2018;48:2205–2218. doi: 10.1159/000492561. [DOI] [PubMed] [Google Scholar]
  8. da Huang W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2008;37:1–13. doi: 10.1093/nar/gkn923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. da Huang W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57. doi: 10.1038/nprot.2008.211. [DOI] [PubMed] [Google Scholar]
  10. Gupta S, Singh AK, Kushwaha PP, Prajapati KS, Shuaib M, Senapati S, Kumar S. Identification of potential natural inhibitors of SARS-CoV2 main protease by molecular docking and simulation studies. J Biomol Struct Dyn. 2021;39:4334–4345. doi: 10.1080/07391102.2020.1776157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Han M, Liu M, Wang Y, Chen X, Xu J, Sun Y, Zhao L, Qu H, Fan Y, Wu C. Antagonism of miR-21 reverses epithelial-mesenchymal transition and cancer stem cell phenotype through AKT/ERK1/2 inactivation by targeting PTEN. PLoS ONE. 2012;7:e39520. doi: 10.1371/journal.pone.0039520. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Isobe T, Hisamori S, Hogan DJ, et al. miR-142 regulates the tumorigenicity of human breast cancer stem cells through the canonical WNT signaling pathway. Elife. 2014;18(3):e01977. doi: 10.7554/eLife.01977. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Kanehisa M. The KEGG database. Novartis Found Symp. 2002;247:91–101. doi: 10.1002/0470857897.ch8. [DOI] [PubMed] [Google Scholar]
  14. Kim SH, Singh KB, Hahm ER, Singh SV. The Role of Forkhead Box Q1 transcription factor in anticancer effects of Withaferin A in breast cancer. Cancer Prev Res (Phila) 2021;14:421–432. doi: 10.1158/1940-6207.CAPR-20-0590. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Kumar S, Chashoo G, Saxena AK, Pandey AK. Parthenium hysterophorus: a probable source of anticancer, antioxidant and anti-HIV agents. Biomed Res Int. 2013;2013:810734. doi: 10.1155/2013/810734. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Kumar S, Kumar R, Dwivedi A, Pandey AK. In vitro antioxidant, antibacterial, and cytotoxic activity and in vivo effect of Syngoniumpodophyllum and Eichhorniacrassipes leaf extracts on isoniazid induced oxidative stress and hepatic markers. Biomed Res Int. 2014;2014:459452. doi: 10.1155/2014/459452. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Kumar S, Pandey S, Pandey AK. In vitro antibacterial, antioxidant, and cytotoxic activities of Parthenium hysterophorus and characterization of extracts by LC-MS analysis. Biomed Res Int. 2014;2014:495154. doi: 10.1155/2014/495154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kushwaha PP, Vardhan PS, Kapewangolo P, Shuaib M, Prajapati SK, Singh AK, Kumar S. Bulbine frutescens phytochemical inhibits notch signaling pathway and induces apoptosis in triple negative and luminal breast cancer cells. Life Sci. 2019;234:116783. doi: 10.1016/j.lfs.2019.116783. [DOI] [PubMed] [Google Scholar]
  19. Kushwaha PP, Singh AK, Prajapati KS, Shuaib M, Fayez S, Bringmann G, Kumar S. Induction of apoptosis in breast cancer cells by naphthylisoquinoline alkaloids. Toxicol Appl Pharmacol. 2020;15(409):115297. doi: 10.1016/j.taap.2020.115297. [DOI] [PubMed] [Google Scholar]
  20. Kushwaha PP, Singh AK, Shuaib M, Prajapati KS, Vardhan PS, Gupta S, Kumar S. 3-O-(E)-p-Coumaroyl betulinic acid possess anticancer activity and inhibit Notch signaling pathway in breast cancer cells and mammosphere. Chem Biol Interact. 2020;1(328):109200. doi: 10.1016/j.cbi.2020.109200. [DOI] [PubMed] [Google Scholar]
  21. Kushwaha PP, Kumar R, Neog PR, et al. Characterization of phytochemicals and validation of antioxidant and anticancer activity in some Indian polyherbal ayurvedic products. Vegetos. 2021;34:286–299. doi: 10.1007/s42535-021-00205-1. [DOI] [Google Scholar]
  22. Lánczky A, Nagy Á, Bottai G, Munkácsy G, Szabó A, Santarpia L, Győrffy B. miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients. Breast Cancer Res Treat. 2016;160:439–446. doi: 10.1007/s10549-016-4013-7. [DOI] [PubMed] [Google Scholar]
  23. Lu Tzu-Pin, Lee Chien-Yueh, Tsai Mong-Hsun, Chiu Yu-Chiao, Hsiao Chuhsing Kate, Lai Liang-Chuan, Chuang Eric Y., Watson Michael. miRSystem: An Integrated System for Characterizing Enriched Functions and Pathways of MicroRNA Targets. PLoS ONE. 2012;7(8):e42390. doi: 10.1371/journal.pone.0042390. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Ma De-bin, Qin Meng-meng, Shi Liang, Ding Xin-min. MicroRNA-6077 enhances the sensitivity of patients-derived lung adenocarcinoma cells to anlotinib by repressing the activation of glucose transporter 1 pathway. Cellular Signalling. 2019;64:109391. doi: 10.1016/j.cellsig.2019.109391. [DOI] [PubMed] [Google Scholar]
  25. Mishra A, Sharma AK, Kumar S, Saxena AK, Pandey AK. Bauhinia variegata leaf extracts exhibit considerable antibacterial, antioxidant, and anticancer activities. Biomed Res Int. 2013;2013:915436. doi: 10.1155/2013/915436. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Naujokat C, McKee DL. The “Big Five“ phytochemicals Targeting cancer stem cells: curcumin, EGCG, sulforaphane. Resveratrol Genistein Curr Med Chem. 2021;28(22):4321–4342. doi: 10.2174/0929867327666200228110738. [DOI] [PubMed] [Google Scholar]
  27. Nishi M, Akutsu H, Kudoh A, Kimura H, Yamamoto N, Umezawa A, Lee SW, Ryo A. Induced cancer stem-like cells as a model for biological screening and discovery of agents targeting phenotypic traits of cancer stem cell. Oncotarget. 2014;30(5):8665–8680. doi: 10.18632/oncotarget.2356. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Oliveros JC (2007) Venny. An interactive tool for comparing lists with Venn’s diagram, 2007-2015. https://bioinfogp.cnb.csic.es/tools/venny/
  29. Palaniyandi K, Pockaj BA, Gendler SJ, Chang XB. Human breast cancer stem cells have significantly higher rate of Clathrin-independent and Caveolin-independent endocytosis than the differentiated breast cancer cells. J Cancer Sci Ther. 2012;27:214–222. [PMC free article] [PubMed] [Google Scholar]
  30. Qian B, Katsaros D, Lu L, Preti M, Durando A, Arisio R, Mu L, Yu H. High miR-21 expression in breast cancer associated with poor disease-free survival in early stage disease and high TGF-beta1. Breast Cancer Res Treat. 2009;117:131–140. doi: 10.1007/s10549-008-0219-7. [DOI] [PubMed] [Google Scholar]
  31. Samaeekia R, Adorno-Cruz V, Bockhorn J, Chang YF, Huang S, Prat A, Ha N, Kibria G, Huo D, Zheng H, Dalton R, Wang Y, Moskalenko GY, Liu H. miR-206 inhibits stemness and metastasis of breast cancer by targeting MKL1/IL11 Pathway. Clin Cancer Res. 2017;23:1091–1103. doi: 10.1158/1078-0432.CCR-16-0943. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Shuaib M, Prajapati KS, Singh AK, Kushwaha PP, Waseem M, Kumar S. Identification of miRNAs and related hub genes associated with the triple negative breast cancer using integrated bioinformatics analysis and in vitro approach. J Biomol Struct Dyn. 2021;13:1–15. doi: 10.1080/07391102.2021.1961869. [DOI] [PubMed] [Google Scholar]
  33. Song K, Farzaneh M. Signaling pathways governing breast cancer stem cells behavior. Stem Cell Res Ther. 2021;12:245. doi: 10.1186/s13287-021-02321-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Sridharan S, Howard CM, Tilley AMC, Subramaniyan B, Tiwari AK, Ruch RJ, Raman D. Novel and alternative targets against breast cancer stemness to combat chemoresistance. Front Oncol. 2019;16(9):1003. doi: 10.3389/fonc.2019.01003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71:209–249. doi: 10.3322/caac.21660. [DOI] [PubMed] [Google Scholar]
  36. Tang W, Yu F, Yao H, Cui X, Jiao Y, Lin L, Chen J, Yin D, Song E, Liu Q. miR-27a regulates endothelial differentiation of breast cancer stem like cells. Oncogene. 2014;33:2629–2638. doi: 10.1038/onc.2013.214. [DOI] [PubMed] [Google Scholar]
  37. Tang T, Yang Z, Zhu Q, Wu Y, Sun K, Alahdal M, Zhang Y, Xing Y, Shen Y, Xia T, Xi T, Pan Y, Jin L. Up-regulation of miR-210 induced by a hypoxic microenvironment promotes breast cancer stem cells metastasis, proliferation, and self-renewal by targeting E-cadherin. FASEB J. 2018 doi: 10.1096/fj.201801013R. [DOI] [PubMed] [Google Scholar]
  38. Verma S, Prajapati KS, Kushwaha PP, et al. Resistance to second generation antiandrogens in prostate cancer: pathways and mechanisms. Cancer Drug Res. 2020;3:742–761. doi: 10.20517/cdr.2020.45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Wu D, Zhang J, Lu Y, Bo S, Li L, Wang L, Zhang Q, Mao J. miR-140-5p inhibits the proliferation and enhances the efficacy of doxorubicin to breast cancer stem cells by targeting Wnt1. Cancer Gene Ther. 2019;26:74–82. doi: 10.1038/s41417-018-0035-0. [DOI] [PubMed] [Google Scholar]
  40. Yang L, Shi P, Zhao G, Xu J, Peng W, Zhang J, Zhang G, Wang X, Dong Z, Chen F, Cui H. Targeting cancer stem cell pathways for cancer therapy. Signal Transduct Target Ther. 2020;5:8. doi: 10.1038/s41392-020-0110-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Zhao J. Cancer stem cells and chemoresistance: the smartest survives the raid. Pharmacol Ther. 2016;160:145–158. doi: 10.1016/j.pharmthera.2016.02.008. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOCX 57 kb) (57.7KB, docx)
Supplementary file2 (XLSX 152 kb) (152.2KB, xlsx)

Articles from 3 Biotech are provided here courtesy of Springer

RESOURCES