The authors have declared that no competing interests exist.
Conceived and designed the experiments: BS GS PMS NI. Performed the experiments: BS MS OKO. Analyzed the data: BS OKO MDV. Contributed reagents/materials/analysis tools: NI OKO MDV. Wrote the paper: BS. Edited the paper: GS PMS NI. Designed the research: NI.
Emerging evidence indicate a new role of TFPI in cancer biology. We recently reported that both isoforms of TFPI induced apoptosis and inhibited proliferation of cancer cells. The signaling pathway(s) mediating the effects of TFPI is, however, presently still unclear. Our goal was to further investigate the cellular processes affected by TFPI and to get insight into the molecular mechanisms involved in the effects of TFPI, using a global gene expression study approach. TFPIα or TFPIβ cDNA were transfected into SK-BR-3 breast cancer cells for stable overexpression. Global mRNA and microRNA (miRNA) expressions were measured and functional annotation of the differentially expressed genes and miRNAs according to gene ontology terms was conducted. Selected results were validated using qRT-PCR and Western blot. A total of 242 and 801 mRNA transcripts and 120 and 46 miRNAs were differentially expressed in cells overexpressing TFPIα or TFPIβ, respectively. Overexpression of either isoform significantly affected the expression of genes involved in cell development (apoptosis, cell movement, migration, invasion, colony formation, growth, and adhesion) and immune response. Network analyses revealed biological interactions between these genes and implied that several of the genes may be involved in both processes. The expression profiles also correlated significantly with clinical phenotype and outcome. Functional cluster analyses indicated altered activity of the epidermal growth factor receptor, small GTPases, and the NF-κB and JAK/STAT cascades when TFPI was overexpressed, and increased activity of the transcription factors NF-κB and Elk-1 and phospho-Akt levels was observed. Integrated mRNA-miRNA analyses showed that 19% and 32% of the differentially expressed genes in cells overexpressing TFPIα or TFPIβ, respectively, may have been regulated by miRNAs. Overexpression of TFPI in breast cancer cells affected the expression of mRNAs and miRNAs involved in processes facilitating cancer cell growth and immunologic response, possibly by signal transduction involving the EGFR pathway.
Tissue factor (TF) pathway inhibitor-1 (TFPI) is a serine protease inhibitor encoded on chromosome 2. Alternative splicing of the TFPI gene results in two main isoforms, TFPIα and TFPIβ. The 276 amino acid TFPIα contains three Kunitz protease inhibitor domains and a basic C-terminal end
Microarrays are widely used for the simultaneous screening of whole genome mRNA expression, giving extensive information about the transcriptome. Functional analysis of expression signatures elucidates ongoing cellular and molecular processes. Expression profiles derived from clinically assessed breast tumors also aid in tumor classification and prognostic assessment
In general, TFPI is known for its important role in the regulation of TF induced blood coagulation. However, more recent evidence indicates an additional role of TFPI in cancer. Several cancer tissues and cell lines have been shown to express TFPI
In the present study, the global mRNA expression profiles of SK-BR-3 breast cancer cells stably overexpressing TFPIα or TFPIβ were investigated to further understand the cellular processes affected beyond apoptosis and proliferation, and the molecular mechanisms behind these effects. The clinical relevance of the differentially expressed genes was assessed using publicly available, clinically annotated breast cancer expression data. To our knowledge, no reports have described the relationship between TFPI and miRNA expressions. A miRNA screening was therefore conducted to elucidate the possible involvement of these mRNA regulators in mediating the cellular effects of TFPI.
The human mammary adenocarcinoma SK-BR-3 cells (ATCC HTB-30, Manassas, VA, USA) were grown in RPMI1640 containing phenol red and 2 mM L-glutamine (Lonza, Viviere, Belgium), supplemented with 10% heat inactivated FBS (Lonza). Cells were cultured at 37°C in an incubator with a humidified atmosphere and 5% CO2.
Stable cell lines with TFPI upregulated were established as previously described
Total RNA was isolated from the cells using the mirVANA RNA isolation kit (Ambion Life Technologies) according to the manufacturer's instruction. The quantity and quality of the isolated RNA were measured using the NanoDrop® ND-1000 UV-Vis Spectrophotometer (NanoDrop Technologies, Wilmington, DE) and Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA), respectively.
Microarray analyses were performed using the Affymetrix GeneChip Human Gene 1.0 ST Arrays (Affymetrix, Santa Clara, CA) which contains approximately 28,000 gene transcripts. 150 ng of total RNA was subjected to GeneChip HT One-Cycle cDNA Synthesis Kit and GeneChip HT IVT Labeling Kit, following the manufacturer's protocol for whole genome gene expression analysis (Affymetrix). Biotinylated and fragmented single stranded cDNAs were hybridized to the GeneChips. The arrays were washed and stained using FS-450 fluidics station (Affymetrix). Signal intensities were detected by Hewlett Packard Gene Array Scanner 3000 7G (Hewlett Packard, Palo Alto, CA, USA). Three chips with samples from three independent RNA isolations were run for each cell line.
The scanned images were processed using the AGCC (Affymetrix GeneChip Command Console) software and the CEL files were imported into the Partek Genomics Suite software (Partek, Inc. MO, USA). The Robust Multichip Analysis (RMA) algorithm was applied for generation of signal values and normalization. Probe sets with maximal signal values of less than 5 across all arrays were removed to filter for low and non-expressed genes, reducing the number of mRNA transcripts to 25,492. For expression comparisons of different groups, profiles were compared using a 1-way ANOVA model. The results were expressed as fold changes (FC), i.e., ratios of mean signal values from cells with TFPI upregulated and empty vector control cells. Gene lists were generated with the criteria of false discovery rate (FDR) 10% and a FC of ≥|2|. The data has been deposited in the NCBI Gene Expression Omnibus (GEO) database with accession number GSE30037 in compliance to MIAME guidelines (
The publicly available breast cancer datasets GSE6532, GSE4922, and GSE7390 were downloaded from GEO at NCBI and merged. Expression data generated from untreated patients using the Affymetrix HG-U133A arrays were selected, and profiles from samples not containing the clinical variables age, size, grade, estrogen receptor status, relapse-free survival time, and relapse-free survival were excluded, resulting in 545 samples. The datasets were RMA normalized individually using the R/Bioconductor package
The correlation between the expression of TFPI isoforms in the normalized datasets and the clinical variables were investigated using a non-parametric Spearman's rho correlation test in R.
miRNA quantification was conducted using the Taqman Human MicroRNA Array Card A v2.0 (Applied Biosystems Life Technologies) following the manufacturer's instructions. The array measures the expression of 377 highly characterized miRNAs and 4 controls simultaneously. In short, 350 ng total RNA was reversely transcribed into cDNA using the Megaplex Human RT primers pool A and TaqMan MicroRNA RT kit (Applied Biosystems Life Technologies). After addition of TaqMan Gene Expression Master mix (Applied Biosystems Life Technologies), samples were loaded on the arrays and run on the ABI PRISM 7900 HT Sequence Detection System (Applied Biosystems Life Technologies) using the low density array format. Results were normalized against the endogenous control U6 snRNA and changes in relative miRNA expression were calculated using the comparative Ct method and expressed as FC of empty vector pTOPO control. miRNAs with a FC of ≥|2| were considered differentially expressed.
For the integrated miRNA and mRNA analysis, the lists of differentially expressed miRNAs were loaded into the Partek Genomics Suite software (Partek) already containing the analyzed microarray data. The software connects to the targetscan 5.1 database (
Quantitative real-time PCR (qRT-PCR) was performed on selected mRNAs and miRNAs using Taqman single assays (Applied Biosystems Life Technology) to verify the array expression results. For validation of the mRNA data, samples were prepared as previously reported
For Western blot analysis, cells were harvested and lysed as described previously
Pathway analysis was performed using Cignal FinderTM 10-Pathway Reporter Arrays (SuperArray Biosciences, Fredrick, MD) according to the manufacturer's instruction. In short, SK-BR-3 cells (3×104) transiently overexpressing TFPIα or TFPIβ or the empty vector pTOPO as a control were seeded into 96-well arrays containing luciferase reporters to common signal transduction pathways and transfection agent. After 48 h, cells were lysed and luciferase activity measured using a Wallac Victor 1420 plate-reader (Perkin Elmer, Waltham, MA). The intensity of the firefly luciferase was normalized to renilla luciferase, and the firefly/renilla ratios of cells overexpressing TFPI and empty vector control cells were divided to determine the relative luciferase activity.
The stable overexpression of the two isoforms of TFPI in SK-BR-3 breast cancer cells have previously been reported
TFPI mRNA FC of control | Total TFPI ag FC of control | ||
SK-BR-3 | pTOPO-TFPIα | 34±2.1 | 6.4±0.1 |
pTOPO-TFPIβ | 55±6.4 | 17.3±0.1* | |
pTOPO (control) | 1±0.1 | 1.0±0.1 |
Overexpression of TFPIα or TFPIβ in SK-BR-3 breast cancer cells, displayed as a summary of previously reported data
The results generated from the microarray experiments were loaded into the Ingenuity Pathway Analysis software (Ingenuity Systems,
TFPIα vs.pTOPO Gene ontology category | Significance | # Genes | TFPIβ vs. pTOPO Gene ontology category | Significance | # Genes |
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Gene Expression | 1.20E-07-9.31E-03 | 37 | Cellular Growth and Proliferation | 9.92E-08-1.92E-02 | 109 |
Cell Death | 1.20E-06-1.07E-02 | 64 | Cellular Movement | 1.14E-07-1.93E-02 | 97 |
Cellular Development | 3.41E-06-1.09E-02 | 47 | Carbohydrate Metabolism | 2.16E-05-1.83E-02 | 21 |
Cellular Movement | 1.05E-05-7.82E-03 | 38 | Cell Death | 2.72E-05-1.86E-02 | 133 |
Cellular Growth and Proliferation | 1.13E-05-1.07E-02 | 52 | Cell-To-Cell Signaling and Interaction | 6.18E-05-1.61E-02 | 81 |
Cell-To-Cell Signaling and Interaction | 3.40E-05-1.09E-02 | 45 | Cell Morphology | 6.38E-05-1.92E-02 | 79 |
Cellular Function and Maintenance | 1.49E-04-1.09E-02 | 26 | Cellular Assembly and Organization | 6.38E-05-1.92E-02 | 72 |
Cell Morphology | 1.53E-04-1.09E-02 | 17 | Cellular Function and Maintenance | 1.23E-04-1.97E-02 | 45 |
Cellular Assembly and Organization | 1.53E-04-4.41E-03 | 12 | Protein Synthesis | 1.23E-04-1.23E-04 | 9 |
Post-Translational Modification | 1.53E-04-8.71E-04 | 6 | Cellular Development | 1.46E-04-1.92E-02 | 95 |
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Infection Mechanism | 3.56E-16-9.31E-03 | 44 | Genetic Disorder | 1.35E-07-1.02E-02 | 361 |
Antimicrobial Response | 2.00E-14-3.09E-03 | 17 | Cancer | 1.40E-05-1.92E-02 | 183 |
Inflammatory Response | 2.00E-14-8.98E-03 | 56 | Reproductive System Disease | 1.40E-05-1.53E-02 | 92 |
Organismal Injury and Abnormalities | 9.91E-14-5.96E-03 | 39 | Cardiovascular Disease | 2.19E-05-1.87E-02 | 143 |
Infectious Disease | 3.36E-07-1.04E-02 | 43 | Endocrine System Disorders | 2.54E-04-1.70E-03 | 144 |
Immunological Disease | 6.84E-07-9.31E-03 | 55 | Metabolic Disease | 2.54E-04-1.70E-03 | 156 |
Cancer | 1.22E-06-1.06E-02 | 70 | Renal and Urological Disease | 3.79E-04-4.58E-03 | 7 |
Inflammatory Disease | 2.05E-06-7.71E-03 | 67 | Gastrointestinal Disease | 3.84E-04-1.92E-02 | 90 |
Reproductive System Disease | 3.17E-06-5.96E-03 | 19 | Inflammatory Disease | 3.84E-04-1.64E-02 | 168 |
Hepatic System Disease | 7.15E-05-7.82E-03 | 29 | Infectious Disease | 6.89E-04-9.96E-03 | 6 |
The ten most significant categories involved in molecular and cellular function and diseases and disorders, as annotated by the Ingenuity Pathway Analysis software, are listed.
To investigate the biological interaction between the differentially expressed genes we generated networks using the Ingenuity Pathway Analysis software. Of the 183 and 669 transcript IDs mapped in the ingenuity analyses, 161 and 560 were network eligible. To express the probability that the genes in a network are actually connected, a
To explore the candidate signal transduction mechanisms possibly mediating the effects of TFPI, we investigated the functional clusters among the differentially expressed genes using eGOn v2.0 (
GO number | Name | Whole array | TFPIα/β vs. pTOPO | |
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GO:0007243 | protein kinase cascade | 321 | 11 | 0.003 |
GO:0007249 | I-kappaB kinase/NF-kappaB cascade | 107 | 6 | 0.003 |
GO:0007253 | cytoplasmic sequestering of NF-kappaB | 3 | 1 | 0.039 |
GO:0007259 | JAK-STAT cascade | 31 | 4 | 0.001 |
GO:0008593 | regulation of Notch signaling pathway | 3 | 1 | 0.039 |
GO:0007186 | G-protein coupled receptor protein signaling pathway | 605 | 1 | 0.005 |
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11326 | 140 | |
GO:0005057 | receptor signaling protein activity | 119 | 6 | 0.004 |
GO:0005006 | epidermal growth factor receptor activity | 2 | 1 | 0.025 |
GO:0004694 | eukaryotic translation initiation factor 2α kinase activity | 3 | 1 | 0.037 |
GO:0004710 | MAP/ERK kinase kinase activity | 1 | 1 | 0.012 |
GO:0017112 | Rab guanyl-nucleotide exchange factor activity | 3 | 1 | 0.037 |
GO:0005093 | Rab GDP-dissociation inhibitor activity | 2 | 1 | 0.025 |
GO:0003924 | GTPase activity | 144 | 5 | 0.033 |
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GO:0007242 | intracellular signaling cascade | 1142 | 63 | 0.018 |
GO:0042504 | tyrosine phosphorylation of Stat4 protein | 1 | 1 | 0.042 |
GO:0007173 | epidermal growth factor receptor signaling pathway | 25 | 5 | 0.003 |
GO:0016601 | Rac protein signal transduction | 9 | 3 | 0.005 |
GO:0034097 | response to cytokine stimulus | 3 | 2 | 0.005 |
GO:0030522 | intracellular receptor-mediated signaling pathway | 48 | 5 | 0.048 |
GO:0030518 | steroid hormone receptor signaling pathway | 42 | 5 | 0.029 |
GO:0030521 | androgen receptor signaling pathway | 28 | 4 | 0.027 |
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11326 | 482 | |
GO:0004714 | transmembrane receptor protein tyrosine kinase activity | 55 | 7 | 0.008 |
GO:0004710 | MAP/ERK kinase kinase activity | 1 | 1 | 0.043 |
GO:0008427 | calcium-dependent protein kinase inhibitor activity | 1 | 1 | 0.043 |
GO:0008083 | growth factor activity | 123 | 10 | 0.041 |
GO:0005006 | epidermal growth factor receptor activity | 2 | 2 | 0.002 |
The differentially expressed mRNAs were loaded into eGOn for functional annotation, and the Master-Target test was used to identify significantly over-represented gene ontology categories (by comparing the number of genes associated with a gene ontology category to the total number of genes in the array associated with that category).
The luciferase reporter system cignal finder and western blotting were used to further identify the signaling molecules that were affected in cells overexpressing TFPI. Results showed that overexpression of either isoform of TFPI led to increased activity of the NF-κB and Elk-1 transcription factors, but not AP1 (
(A) Transcription factor activity was measured using the cignal finder luciferase reporter system. SK-BR-3 cells (3×104) transiently transfected with vectors overexpressing TFPIα (dark gray) or TFPIβ (gray), or with empty vector pTOPO (light gray) as controls were seeded in 96-well arrays 24 hours after transfection. After 48 hours, cells were lysed and the firefly and renilla luciferase intensity determined. The results are presented as mean (n≥8) relative luciferase activity + SEM of three independent experiments. Statistical differences between cells overexpressing TFPI and empty vector control cells were determined using the student's t test (*
To further evaluate the involvement of TFPI in cancer progression, the clinical importance of the differentially expressed genes was assessed. Our data were compared to a merged dataset of publicly available, clinically annotated breast cancer expression profiles, comprised of 545 untreated patients. Results showed that the differentially expressed genes were significantly associated with tumor grade and ER status when either isoform of TFPI was overexpressed. Furthermore, overexpression of TFPIβ resulted in an expression signature that significantly correlated with relapse-free survival time and occurrence (
Clinical Variable | TFPIα | TFPIβ |
Merged Breast cancer datasets (n = 545) | ||
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ns | 4.96×10−5 |
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ns | Ns |
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4.87×10−4 | 2.41×10−14 |
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1.09×10−8 | 2.04×10−60 |
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ns | 8.33×10−4 |
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ns | 4.09×10−4 |
Three publicly available, clinically annotated breast cancer datasets (GSE6532, GSE4922, and GSE7390) were downloaded from the Gene Expression Omnibus (GEO) database at NCBI and merged. Associations between the differentially expressed genes and clinical variables were evaluated using the
We also investigated the correlation between the expression of TFPI isoforms and clinical variables in the merged datasets. The results showed that the expression of two probes identifying TFPIα (209676_at and 213258_at) correlated significantly with tumor size (
We next investigated if overexpression of TFPI affected the expression of any miRNAs in the two cell lines. Overexpression of TFPIα resulted in differential expression of 120 different miRNAs, of which all were downregulated (top ten: miR-15a, miR-98, miR-628-5p, miR-128, miR-95, miR-340, miR-660, miR-9, miR-501-5p, miR-96;
miRNAs associated with molecular and cellular function (A) and cancer disease (B), as annotated by the Ingenuity Pathway Analysis software.
We investigated the possible interactions between the miRNA and mRNA expression results. Using the Partek software, the miRNA and mRNA data were compared to identify whether any of the predicted miRNA gene targets were actually differentially expressed at the mRNA level. The results showed that predicted targets of 83 miRNAs were oppositely expressed at the mRNA level in cells overexpressing TFPIα, possibly regulating 46 different mRNAs (
miRNA |
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Target modulation (FC |
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Downregulated microRNAs | TFPIα | TFPIβ | ||
hsa-miR-15a |
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neuron navigator 1 | 2.1 | 3.2 |
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ring finger protein 213 | 2.2 | 2.3 | |
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SWAP switching B-cell complex 70kDa subunit | 2.2 | 2.9 | |
hsa-miR-93 |
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netrin 4 | 2.6 | 3.8 |
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signal transducer and activator of transcription 3 | 2.1 | 2.6 | |
hsa-miR-101 |
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2.2 | 2.3 | |
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SWAP switching B-cell complex 70kDa subunit | 2.2 | 2.9 | |
hsa-miR-135a |
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fermitin family member 2 | 2.4 | 2.6 |
hsa-miR-135b |
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fermitin family member 2 | 2.4 | 2.6 |
hsa-miR-148b |
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2.2 | 2.3 | |
hsa-miR-193b |
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5'-nucleotidase, ecto (CD73) | 2.6 | 2.3 |
hsa-miR-200a |
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wingless-type MMTV integration site family, member 5A | 3.2 | 7.7 |
hsa-miR-200b |
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keratin 80 | 2.2 | 2.0 |
hsa-miR-203 |
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acyl-CoA synthetase long-chain family member 1 | 3.1 | 3.3 |
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platelet derived growth factor D | 2.1 | 3.2 | |
hsa-miR-218 |
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acyl-CoA synthetase long-chain family member 1 | 3.1 | 3.3 |
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tumor protein D52 | 2.0 | 2.5 | |
hsa-miR-301a |
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acyl-CoA synthetase long-chain family member 1 | 3.1 | 3.3 |
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fermitin family member 2 | 2.4 | 2.6 | |
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2.2 | 2.3 | ||
hsa-miR-365 |
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wingless-type MMTV integration site family, member 5A | 3.2 | 7.7 |
hsa-miR-429 |
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keratin 80 | 2.2 | 2.0 |
hsa-miR-454 |
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acyl-CoA synthetase long-chain family member 1 | 3.1 | 3.3 |
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fermitin family member 2 | 2.4 | 2.6 | |
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2.2 | 2.3 | ||
hsa-miR-636 |
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acyl-CoA synthetase long-chain family member 1 | 3.1 | 3.3 |
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tumor protein D52 | 2.0 | 2.5 |
Fold change.
To validate the results obtained from the microarray and miRNA array analyses, qRT-PCR was performed on selected genes using Taqman single assays. 16 mRNAs, which were either among the most differentially expressed genes or identified as a key gene in the functional and/or cluster analyses, and 13 miRNAs, which were identified in the functional analysis or had predicted mRNA targets differentially expressed, were selected. All of the 16 mRNAs and 11 of the 13 miRNAs selected for verification were validated (
Selected mRNAs (A and B) and miRNAs (C and D) differentially expressed in SK-BR-3 cells overexpressing TFPIα (A and C) or TFPIβ (B and D) were validated by Taqman single assays and qRT-PCR. Results were normalized against the endogenous controls PMM1 and U6 snRNA and the relative expression calculated using the comparative Ct method. Values are presented as mean (n = 3) fold change (FC) of empty vector pTOPO control + SEM of three biological replicates. White bars indicate array expression values, black bars represent Taqman verification values. Dotted lines indicate FC = |2|.
Proteins were separated on a SDS-polyacrylamide gel, transferred to a nitrocellulose membrane and detected using an anti-EGFR antibody. Anti-actin was used as a protein loading control. (A) Western blot of one representative experiment. (B) Quantification of three independent experiments using ImageJ (n = 3+ SEM).
We recently reported that stable overexpression of TFPIα or TFPIβ induced death receptor activated apoptosis and inhibited proliferation of SK-BR-3 breast cancer cells
In line with our previous findings
Overexpression of either isoform of TFPI resulted in expression profiles that associated significantly with tumor grade and ER status in a breast cancer patient material. This indicates a possibly therapeutic potential of TFPI that could affect tumor grade and perhaps patient outcome. However, TFPIα and TFPIβ expression in the patient material was not associated with tumor grade or ER status illustrating TFPI as a poor predictive marker for these variables. The association between TFPI and ER status is also intriguing as anti-estrogen therapy is important in breast cancer treatment
In a previous study, 16 genes were reported to be differentially expressed in HUVECs after treatment with rTFPIα
miRNAs play an important role in cancer development as they can alter the expression of many tumor suppressor- and oncogenes
Interestingly, only a portion of the differentially expressed mRNAs and miRNAs were identical after overexpression of TFPIα or TFPIβ. It therefore seems likely that these genes were responsible for the effects observed in both cell lines related to cancer cell growth. However, additional mechanisms and processes were also affected, which were unique for each of the isoforms of TFPI. Compared to cells overexpressing TFPIα, more genes were differentially expressed in cells overexpressing TFPIβ, and processes such as Carbohydrate metabolism, Free Radical Scavenging, Lipid Metabolism, Cellular Response to Therapeutics, Drug Metabolism, Vitamin and Mineral Metabolism, and Amino Acid Metabolism were significantly affected (
In summary, we here report the mRNA and miRNA expression profiles of SK-BR-3 breast cancer cells overexpressing TFPIα or TFPIβ. The differentially expressed genes provide a genetic insight into the pro-apoptotic and anti-proliferative effects previously observed in these cells
(TIF)
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Differentially expressed mRNAs following increased expression of TFPIα in SK-BR-3 cells.
(XLS)
Differentially expressed mRNAs following increased expression of TFPIβ in SK-BR-3 cells.
(XLS)
Functional annotation of differentially expressed mRNAs following overexpression of TFPIα or TFPIβ. All the significant categories involved in molecular and cellular function and diseases and disorders as annotated by the Ingenuity Pathway Analysis software are listed.
(XLS)
Network analysis of genes differentially expressed in SK-BR-3 cells overexpressing TFPIα or TFPIβ compared to empty vector control cells (pTOPO). Genes in bold are members of the network which are differentially expressed.
(XLS)
Differentially expressed miRNAs following overexpression of TFPIα or TFPIβ in SK-BR-3 cells.
(XLS)
Functional annotation of differentially expressed miRNAs following overexpression of TFPIα or TFPIβ. The ten most the significant categories involved in molecular and cellular function and diseases and disorders as annotated by the Ingenuity Pathway Analysis software are listed.
(XLS)
Differentially expressed miRNAs with predicted gene targets oppositely expressed at the mRNA level in SK-BR-3 cells overexpressing TFPIα. Predicted mRNA targets in bold were also identified as possible targets in cells overexpressing TFPIβ.
(XLS)
Differentially expressed miRNAs with predicted gene targets oppositely expressed at the mRNA level in SK-BR-3 cells overexpressing TFPIβ. Predicted mRNA targets in bold were also identified as possible targets in cells overexpressing TFPIα.
(XLS)