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- Apleberger, Lennart, et al.
(författare)
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Byggandets industrialisering : nulägesbeskrivning
- 2007
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Rapporten belyser definitionsfrågor samt olika förutsättningar och utgångspunkter som behöver beaktas då man diskuterar industrialisering av byggandet. En genomgång av utvecklingsläget i byggbranschen redovisas inklusive en internationell utblick samt forskningsinsatser och utbildning. Industrialiseringsgraden och implementeringsnivån har bedömts för tio olika svenska koncept. I rapporten föreslås också prioriterade områden för fortsatta utvecklingssatsningar.
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- Björkblom, Benny, et al.
(författare)
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Pre-diagnostic plasma metabolites linked to future brain tumor development
- 2018
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Ingår i: Neuro-Oncology. - : Oxford University Press. - 1522-8517 .- 1523-5866. ; 20, s. 288-289
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- BACKGROUND: The Northern Sweden Health and Disease Study is a unique population-based biobank linked to the clinical data registries. The samples originate from over 133 000 individuals living in the northern part of Sweden, and primarily collected during health checkups from the age of 40 years. Our project aims to investigate alterations in metabolite signatures in blood plasma of healthy blood donors that later in life developed a tumor. Brain tumors, especially glioblastoma is associated with poor prognosis. To explore early events of metabolic reprograming linked to future diagnosis, we investigated alterations in metabolite concentrations in plasma collected several years before diagnosis with matched healthy controls.MATERIALS AND METHODS: In total 392 analytical samples (256 repeated timepoint and 136 single timepoint, case-control samples) were analyzed using GCTOFMS. Constrained randomization of run order was utilized to maximize information output and minimize the false discovery rate. By use of reference databases, we could with high confidence quantify and identify 150 plasma metabolites. We detected metabolites with significant alterations in concertation between pre-clinical glioma cases and healthy controls by the effect projection approach based on orthogonal partial least squares (OPLSEP).RESULTS AND CONCLUSIONS: For the repeated blood samples, we designed and applied a novel multivariate strategy for high resolution biomarker pattern discovery. We utilize the fact that we have available samples from two repeated time points prior to diagnosis for each future glioma case and their matched controls to construct a small design of experiment (DoE) of four samples for each match pair. The data for each individual DoE was evaluated by OPLS-EP to determine the effect of each individual metabolite in relation to control-case, time and their interaction. Finally, latent significance calculations by means of OPLS were used to extract and evaluate the correct latent biomarker and highlight true significance of individual metabolites. Our study presents an approach to minimize confounding effects due to systematic noise from sampling, the analytical method, as well as take into account personalized metabolic levels over time, enabling biomarker detection within a smaller sample group. We will present and discuss the latest results and biomarkers from this exploratory metabolomics study at the meeting
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- 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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