| 1. |
- Jansson, Malin, et al.
(författare)
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MiR-155-mediated loss of C/EBP beta shifts the TGF-beta response from growth inhibition to epithelial-mesenchymal transition, invasion and metastasis in breast cancer
- 2013
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Ingår i: Oncogene. - : Nature Publishing Group. - 0950-9232 .- 1476-5594. ; 32:50, s. 5614-5624
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Tidskriftsartikel (refereegranskat)abstract
- During breast cancer progression, transforming growth factor-beta (TGF-beta) switches from acting as a growth inhibitor to become a major promoter of epithelial-mesenchymal transition (EMT), invasion and metastasis. However, the mechanisms involved in this switch are not clear. We found that loss of CCAAT-enhancer binding protein beta (C/EBP beta), a differentiation factor for the mammary epithelium, was associated with signs of EMT in triple-negative human breast cancer, and in invasive areas of mammary tumors in MMTV-PyMT mice. Using an established model of TGF-beta-induced EMT in mouse mammary gland epithelial cells, we discovered that C/EBP beta was repressed during EMT by miR-155, an oncomiR in breast cancer. Depletion of C/EBP beta potentiated the TGF-beta response towards EMT, and contributed to evasion of the growth inhibitory response to TGF-beta. Furthermore, loss of C/EBP beta enhanced invasion and metastatic dissemination of the mouse mammary tumor cells to the lungs after subcutaneous injection into mice. The mechanism by which loss of C/EBP beta promoted the TGF-beta response towards EMT, invasion and metastasis, was traced to a previously uncharacterized role of C/EBP beta as a transcriptional activator of genes encoding the epithelial junction proteins E-cadherin and coxsackie virus and adenovirus receptor. The results identify miR-155-mediated loss of C/EBP beta as a mechanism, which promotes breast cancer progression by shifting the TGF-beta response from growth inhibition to EMT, invasion and metastasis.
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| 2. |
- Borgmästars, Emmy, et al.
(författare)
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Multi-omics profiling to identify early plasma biomarkers in pre-diagnostic pancreatic ductal adenocarcinoma : a nested case-control study
- 2024
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Ingår i: Translational Oncology. - : Elsevier. - 1944-7124 .- 1936-5233. ; 48
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Tidskriftsartikel (refereegranskat)abstract
- Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor survival. Novel biomarkers are urgently needed to improve the outcome through early detection. Here, we aimed to discover novel biomarkers for early PDAC detection using multi-omics profiling in pre-diagnostic plasma samples biobanked after routine health examinations.A nested case-control study within the Northern Sweden Health and Disease Study was designed. Pre-diagnostic plasma samples from 37 future PDAC patients collected within 2.3 years before diagnosis and 37 matched healthy controls were included. We analyzed metabolites using liquid chromatography mass spectrometry and gas chromatography mass spectrometry, microRNAs by HTG edgeseq, proteins by multiplex proximity extension assays, as well as three clinical biomarkers using milliplex technology. Supervised and unsupervised multi-omics integration were performed as well as univariate analyses for the different omics types and clinical biomarkers. Multiple hypothesis testing was corrected using Benjamini-Hochberg's method and a false discovery rate (FDR) below 0.1 was considered statistically significant.Carbohydrate antigen (CA) 19-9 was associated with PDAC risk (OR [95 % CI] = 3.09 [1.31–7.29], FDR = 0.03) and increased closer to PDAC diagnosis. Supervised multi-omics models resulted in poor discrimination between future PDAC cases and healthy controls with obtained accuracies between 0.429–0.500. No single metabolite, microRNA, or protein was differentially altered (FDR < 0.1) between future PDAC cases and healthy controls.CA 19-9 levels increase up to two years prior to PDAC diagnosis but extensive multi-omics analysis including metabolomics, microRNAomics and proteomics in this cohort did not identify novel early biomarkers for PDAC.
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| 3. |
- Franklin, Oskar, et al.
(författare)
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Plasma micro-RNA alterations appear late in pancreatic cancer
- 2018
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Ingår i: Annals of Surgery. - 0003-4932 .- 1528-1140. ; 267:4, s. 775-781
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: The aim of this research was to study whether plasma microRNAs (miRNA) can be used for early detection of pancreatic cancer (PC) by analyzing prediagnostic plasma samples collected before a PC diagnosis. Background: PC has a poor prognosis due to late presenting symptoms and early metastasis. Circulating miRNAs are altered in PC at diagnosis but have not been evaluated in a prediagnostic setting. Methods: We first performed an initial screen using a panel of 372 miRNAs in a retrospective case-control cohort that included early-stage PC patients and healthy controls. Significantly altered miRNAs at diagnosis were then measured in an early detection case-control cohort wherein plasma samples in the cases are collected before a PC diagnosis. Carbohydrate antigen 19–9 (Ca 19–9) levels were measured in all samples for comparison. Results: Our initial screen, including 23 stage I-II PC cases and 22 controls, revealed 15 candidate miRNAs that were differentially expressed in plasma samples at PC diagnosis. We combined all 15 miRNAs into a multivariate statistical model, which outperformed Ca 19–9 in receiver-operating characteristics analysis. However, none of the candidate miRNAs, individually or in combination, were significantly altered in prediagnostic plasma samples from 67 future PC patients compared with 132 matched controls. In comparison, Ca 19–9 levels were significantly higher in the cases at <5 years before diagnosis. Conclusion: Plasma miRNAs are altered in PC patients at diagnosis, but the candidate miRNAs found in this study appear late in the course of the disease and cannot be used for early detection of the disease.
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| 4. |
- Borgmästars, Emmy, 1990-
(författare)
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In search of early biomarkers in pancreatic ductal adenocarcinoma using multi-omics and bioinformatics
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Background: Pancreatic ductal adenocarcinoma (PDAC) is a very aggressive malignancy with a 5-year survival of 10 %. Surgery is the only curative treatment. Unfortunately, few patients are eligible for surgery due to late detection. Thus, we need ways to detect the disease at an earlier stage and for that good screening biomarkers could be used. Previous studies have analyzed circulating analytes in prospective studies to identify early PDAC signals. One such class is microRNAs (miRNAs). MicroRNAs are non-coding RNAs of around 22 nucleotides that act as post- transcriptional regulators by interaction with messenger RNAs (mRNAs). The function of a miRNA can be elucidated by target prediction, to identify its potential targets, followed by enrichment analysis of the predicted targets. Challenges with this approach includes a lot of false positives being generated and that miRNAs can perform their role in a tissue- or disease-specific manner. Other classes of analytes that have previously been studied in prospective PDAC cohorts are metabolites and proteins. Aims: This thesis has three aims. First, to build a miRNA functional analysis pipeline with correlation support between miRNA and its predicted target genes. Second, to identify potential circulating biomarkers for early detection of PDAC using multi-omics. Third, to identify potential prognostic metabolites in a prospective PDAC cohort.Methods: We used publicly available data from the cancer genome atlas-pancreatic adenocarcinoma (TCGA-PAAD) and pre-diagnostic plasma samples from the Northern Sweden Health and Disease Study. We built a pipeline in R including miRNA, mRNA, and protein expression data from TCGA-PAAD for in silico miRNA functional analysis. Pre- diagnostic plasma samples from future PDAC patients as well as matched healthy controls were analyzed using multi- omics. Tissue polypeptide specific antigen (TPS) was analyzed by enzyme linked immunosorbent assay in 267 future PDAC samples and 320 healthy controls. Metabolomics and clinical biomarkers (carbohydrate antigen (CA) 19-9, carcinoembryonic antigen (CEA), and CA 15-3) were profiled in 100 future PDAC samples and 100 healthy controls using liquid chromatography-mass spectrometry (MS), gas chromatography-MS, and multi-plex technology. Of these, a subset of 39 future PDAC patients and 39 healthy controls were profiled for 2083 microRNAs using targeted sequencing and 644 proteins using proximity extension assays. Circulating levels of multi-omics analytes were analyzed using conditional or unconditional logistic regression. Least absolute shrinkage and selection operator (LASSO) in combination with 500 bootstrap iterations identified the most informative variables. The prognostic value of metabolites was assessed using cox regression. Multi-omics factor analysis (MOFA) and data integration analysis for biomarker discovery using latent components (DIABLO) were used for multi-omics integration analyses.Results: An automated pipeline was built consisting of 1) miRNA target prediction, 2) correlation analyses between miRNA and its targets on mRNA and protein expression levels, and 3) functional enrichment of correlated targets to identify enriched Kyoto encyclopedia of genes and genomes (KEGG) pathways and gene ontology (GO) terms for a specific miRNA. The pipeline was run for all microRNAs (~700) detected in the TCGA-PAAD cohort. These results can be downloaded from a shiny app (https://emmbor.shinyapps.io/mirfa/). TPS was not altered in pre-diagnostic PDAC patients up to 24 years prior to diagnosis, but increased at diagnosis (OR = 1.03, 95 % CI: 1.01-1.05). Internal area under curves of 0.74, 0.80, and 0.88 were achieved for five metabolites, two proteins, and two miRNAs that were selected by LASSO and bootstrap iterations, in combination with CA 19-9. Neither MOFA nor DIABLO separated well between future PDAC cases and healthy controls. Conclusions: Our bioinformatics pipeline for in silico functional analysis of microRNAs successfully identifies enriched KEGG pathways and GO terms for miRNA isoforms. The investigated plasma samples are heterogeneous, but among the analyzed variables, we identified five metabolites, two proteins, and two microRNAs with highest potential for early PDAC detection. CA 19-9 levels increased closer to diagnosis. We identified five fatty acids that could be studied in a diagnostic PDAC cohort as prognostic biomarkers.
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| 5. |
- Borgmästars, Emmy, et al.
(författare)
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miRFA : an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer
- 2019
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Ingår i: BMC Bioinformatics. - : BioMed Central. - 1471-2105. ; 20:1, s. 1-17
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: MicroRNAs (miRNAs) are small RNAs that regulate gene expression at a post-transcriptional level and are emerging as potentially important biomarkers for various disease states, including pancreatic cancer. In silico-based functional analysis of miRNAs usually consists of miRNA target prediction and functional enrichment analysis of miRNA targets. Since miRNA target prediction methods generate a large number of false positive target genes, further validation to narrow down interesting candidate miRNA targets is needed. One commonly used method correlates miRNA and mRNA expression to assess the regulatory effect of a particular miRNA. The aim of this study was to build a bioinformatics pipeline in R for miRNA functional analysis including correlation analyses between miRNA expression levels and its targets on mRNA and protein expression levels available from the cancer genome atlas (TCGA) and the cancer proteome atlas (TCPA). TCGA-derived expression data of specific mature miRNA isoforms from pancreatic cancer tissue was used.RESULTS: Fifteen circulating miRNAs with significantly altered expression levels detected in pancreatic cancer patients were queried separately in the pipeline. The pipeline generated predicted miRNA target genes, enriched gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathways. Predicted miRNA targets were evaluated by correlation analyses between each miRNA and its predicted targets. MiRNA functional analysis in combination with Kaplan-Meier survival analysis suggest that hsa-miR-885-5p could act as a tumor suppressor and should be validated as a potential prognostic biomarker in pancreatic cancer.CONCLUSIONS: Our miRNA functional analysis (miRFA) pipeline can serve as a valuable tool in biomarker discovery involving mature miRNAs associated with pancreatic cancer and could be developed to cover additional cancer types. Results for all mature miRNAs in TCGA pancreatic adenocarcinoma dataset can be studied and downloaded through a shiny web application at https://emmbor.shinyapps.io/mirfa/ .
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