Pan-Cancer and Single-Cell Modeling of Genomic Alterations Through Gene Expression
Autor: | Forest Ray, Daniele Mercatelli, Federico M. Giorgi |
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Přispěvatelé: | Mercatelli D., Ray F., Giorgi F.M. |
Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
0301 basic medicine
lcsh:QH426-470 Gene regulatory network Genomics Computational biology Biology single-cell sequencing medicine.disease_cause 03 medical and health sciences 0302 clinical medicine Genetic Gene expression Noise - reduction medicine Genetics genomics Coexpression cancer Copy-number variation Transcriptomics Gene Genetics (clinical) Original Research Transcriptional network Gene network TCGA lcsh:Genetics 030104 developmental biology Single cell sequencing Tumor progression 030220 oncology & carcinogenesis NGS (next generation sequencing) Genomic Molecular Medicine Carcinogenesis |
Zdroj: | Frontiers in Genetics Frontiers in Genetics, Vol 10 (2019) |
ISSN: | 1664-8021 |
Popis: | Cancer is a disease often characterized by the presence of multiple genomic alterations, which trigger altered transcriptional patterns and gene expression, which in turn sustain the processes of tumorigenesis, tumor progression, and tumor maintenance. The links between genomic alterations and gene expression profiles can be utilized as the basis to build specific molecular tumorigenic relationships. In this study, we perform pan-cancer predictions of the presence of single somatic mutations and copy number variations using machine learning approaches on gene expression profiles. We show that gene expression can be used to predict genomic alterations in every tumor type, where some alterations are more predictable than others. We propose gene aggregation as a tool to improve the accuracy of alteration prediction models from gene expression profiles. Ultimately, we show how this principle can be beneficial in intrinsically noisy datasets, such as those based on single-cell sequencing. |
Databáze: | OpenAIRE |
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