Vinsamlegast notið þetta auðkenni þegar þið vitnið til verksins eða tengið í það: http://hdl.handle.net/1946/35096
Background: Breast cancer is the most common cause of cancer for women in the world and represents a large portion of all cancer incidence and mortality each year. Desorption electrospray ionisation mass spectrometry imaging (DESI-MSI) is an emerging mass spectrometric technique that shows great promise in cancer prognostics and diagnostics, granting spatially resolved profiling information.
Objective: The objective of this project was to improve the diagnosis of breast cancer by analysing small molecule patterns in breast cancer tissue using DESI-MSI. With secondary objectives of distinguishing between breast cancer tissue and normal breast tissue based on their metabolic profile using DESI-MSI, and exploring a range of clinical factors and molecular subtypes of breast cancer for better understanding of the metabolic profile.
Methods: DESI-MSI analysis was performed on 26 fresh frozen breast tumour and 29 normal tissue samples from Icelandic patients. The two breast tissue types were compared using univariate and multivariate statistical analysis with Metaboanalyst. Within the breast tumour tissue samples, a range of clinical factors and molecular subtypes were compared with the same statistical methods.
Results: Multiple features were found, which could be significant in discriminating between the normal and tumour breast tissue samples. There was an indication of separation of the two breast tissue types with the multivariate statistical analysis methods PCA and PLS-DA. For the breast tumour tissue comparison, three subsets had any statistically significant features that showed some differentiation within each comparison group; BRCA2, progesterone receptor (PgR) and TNM staging.
Conclusions: The results of the project are preliminary and are dependent on further investigation as the project was cut short by COVID-19. The initial results of the project are promising but require further investigations, larger cohort, and collaboration with researchers in the field, particularly in terms of statistical analysis.
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