Discretization of Gene Expression Data Unmasks Molecular Subgroups Recurring in Different Human Cancer Types
Despite the individually different molecular alterations in tumors, the malignancy associated biological traits are strikingly similar. Results of a previous study using renal cell carcinoma (RCC) as a model pointed towards cancer-related features, which could be visualized as three groups by microa...
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| creator | Beleut, Manfred Soeldner, Robert Egorov, Mark Guenther, Rolf Dehler, Silvia Morys-Wortmann, Corinna Moch, Holger Henco, Karsten Schraml, Peter |
| description | Despite the individually different molecular alterations in tumors, the malignancy associated biological traits are strikingly similar. Results of a previous study using renal cell carcinoma (RCC) as a model pointed towards cancer-related features, which could be visualized as three groups by microarray based gene expression analysis. In this study, we used a mathematic model to verify the presence of these groups in RCC as well as in other cancer types. We developed an algorithm for gene-expression deviation profiling for analyzing gene expression data of a total of 8397 patients with 13 different cancer types and normal tissues. We revealed three common Cancer Transcriptomic Profiles (CTPs) which recurred in all investigated tumors. Additionally, CTPs remained robust regardless of the functions or numbers of genes analyzed. CTPs may represent common genetic fingerprints, which potentially reflect the closely related biological traits of human cancers. |
| doi_str_mv | 10.1371/journal.pone.0161514 |
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Results of a previous study using renal cell carcinoma (RCC) as a model pointed towards cancer-related features, which could be visualized as three groups by microarray based gene expression analysis. In this study, we used a mathematic model to verify the presence of these groups in RCC as well as in other cancer types. We developed an algorithm for gene-expression deviation profiling for analyzing gene expression data of a total of 8397 patients with 13 different cancer types and normal tissues. We revealed three common Cancer Transcriptomic Profiles (CTPs) which recurred in all investigated tumors. Additionally, CTPs remained robust regardless of the functions or numbers of genes analyzed. 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Results of a previous study using renal cell carcinoma (RCC) as a model pointed towards cancer-related features, which could be visualized as three groups by microarray based gene expression analysis. In this study, we used a mathematic model to verify the presence of these groups in RCC as well as in other cancer types. We developed an algorithm for gene-expression deviation profiling for analyzing gene expression data of a total of 8397 patients with 13 different cancer types and normal tissues. We revealed three common Cancer Transcriptomic Profiles (CTPs) which recurred in all investigated tumors. Additionally, CTPs remained robust regardless of the functions or numbers of genes analyzed. CTPs may represent common genetic fingerprints, which potentially reflect the closely related biological traits of human cancers.</description><subject>Algorithms</subject><subject>Biological effects</subject><subject>Biology and Life Sciences</subject><subject>Cancer</subject><subject>Cancer genetics</subject><subject>Carcinoma, Renal Cell - classification</subject><subject>Carcinoma, Renal Cell - genetics</subject><subject>Carcinoma, Renal Cell - metabolism</subject><subject>Classification</subject><subject>Data processing</subject><subject>DNA microarrays</subject><subject>Functions (mathematics)</subject><subject>Gene expression</subject><subject>Gene Expression Profiling</subject><subject>Gene Expression Regulation, Neoplastic - genetics</subject><subject>Genes, Neoplasm - genetics</subject><subject>Genetic variation</subject><subject>Genome-Wide Association Study</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Humans</subject><subject>Kidney cancer</subject><subject>Kidney Neoplasms - 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Results of a previous study using renal cell carcinoma (RCC) as a model pointed towards cancer-related features, which could be visualized as three groups by microarray based gene expression analysis. In this study, we used a mathematic model to verify the presence of these groups in RCC as well as in other cancer types. We developed an algorithm for gene-expression deviation profiling for analyzing gene expression data of a total of 8397 patients with 13 different cancer types and normal tissues. We revealed three common Cancer Transcriptomic Profiles (CTPs) which recurred in all investigated tumors. Additionally, CTPs remained robust regardless of the functions or numbers of genes analyzed. CTPs may represent common genetic fingerprints, which potentially reflect the closely related biological traits of human cancers.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27537329</pmid><doi>10.1371/journal.pone.0161514</doi><tpages>e0161514</tpages><orcidid>https://orcid.org/0000-0002-5610-156X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Biological effects Biology and Life Sciences Cancer Cancer genetics Carcinoma, Renal Cell - classification Carcinoma, Renal Cell - genetics Carcinoma, Renal Cell - metabolism Classification Data processing DNA microarrays Functions (mathematics) Gene expression Gene Expression Profiling Gene Expression Regulation, Neoplastic - genetics Genes, Neoplasm - genetics Genetic variation Genome-Wide Association Study Genomes Genomics Humans Kidney cancer Kidney Neoplasms - classification Kidney Neoplasms - genetics Kidney Neoplasms - metabolism Malignancy Medical prognosis Medicine and Health Sciences Models, Theoretical Mutation Neoplasms - classification Neoplasms - genetics Neoplasms - metabolism Oligonucleotide Array Sequence Analysis Pathology Physiological aspects Renal cell carcinoma Research and Analysis Methods Robustness (mathematics) Subgroups Tissues Tumors Values |
| title | Discretization of Gene Expression Data Unmasks Molecular Subgroups Recurring in Different Human Cancer Types |
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