Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach
| Autor: | Marco Caprini, Andrew N Holding, Francesco Formaggio, Daniele Mercatelli, Federico M. Giorgi |
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| Přispěvatelé: | Mercatelli, Daniele, Formaggio, Francesco, Caprini, Marco, Holding, Andrew, Giorgi, Federico M |
| Rok vydání: | 2021 |
| Předmět: |
Bioinformatics
Biophysics Breast Neoplasms Computational biology Biology Biochemistry Molecular Bases of Health & Disease Machine Learning Transcriptome transcriptomics breast cancer Breast cancer Predictive Value of Tests Databases Genetic Biomarkers Tumor medicine Humans Gene Regulatory Networks Systems Biology & Networks Protein Interaction Maps skin and connective tissue diseases Molecular Biology Genotyping Research Articles Survival analysis Cancer bioinformatic Gene Expression & Regulation Gene Expression Profiling master regulator biomarkers surfaceome Genomics Cell Biology Prognosis medicine.disease Gene Expression Regulation Neoplastic Statistical classification Increased risk Case-Control Studies biomarker Female Surface protein master regulators Normal breast Signal Transduction |
| Zdroj: | Bioscience Reports |
| ISSN: | 1573-4935 0144-8463 |
| Popis: | Background: Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. So far, very few attempts have been made to characterize the surfaceome of patients with breast cancer, particularly in relation with the current molecular breast cancer (BRCA) classification. In this view, we developed a new computational method to infer cell-surface protein activities from transcriptomics data, termed ‘SURFACER’. Methods: Gene expression data from GTEx were used to build a normal breast network model as input to infer differential cell-surface proteins activity in BRCA tissue samples retrieved from TCGA versus normal samples. Data were stratified according to the PAM50 transcriptional subtypes (Luminal A, Luminal B, HER2 and Basal), while unsupervised clustering techniques were applied to define BRCA subtypes according to cell-surface proteins activity. Results: Our approach led to the identification of 213 PAM50 subtypes-specific deregulated surface genes and the definition of five BRCA subtypes, whose prognostic value was assessed by survival analysis, identifying a cell-surface activity configuration at increased risk. The value of the SURFACER method in BRCA genotyping was tested by evaluating the performance of 11 different machine learning classification algorithms. Conclusions: BRCA patients can be stratified into five surface activity-specific groups having the potential to identify subtype-specific actionable targets to design tailored targeted therapies or for diagnostic purposes. SURFACER-defined subtypes show also a prognostic value, identifying surface-activity profiles at higher risk. |
| Databáze: | OpenAIRE |
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