Cargando…

A Bioinformatics View on Acute Myeloid Leukemia Surface Molecules by Combined Bayesian and ABC Analysis

“Big omics data” provoke the challenge of extracting meaningful information with clinical benefit. Here, we propose a two-step approach, an initial unsupervised inspection of the structure of the high dimensional data followed by supervised analysis of gene expression levels, to reconstruct the surf...

Descripción completa

Detalles Bibliográficos
Autores principales: Thrun, Michael C., Mack, Elisabeth K. M., Neubauer, Andreas, Haferlach, Torsten, Frech, Miriam, Ultsch, Alfred, Brendel, Cornelia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687378/
https://www.ncbi.nlm.nih.gov/pubmed/36354555
http://dx.doi.org/10.3390/bioengineering9110642
_version_ 1784835990444048384
author Thrun, Michael C.
Mack, Elisabeth K. M.
Neubauer, Andreas
Haferlach, Torsten
Frech, Miriam
Ultsch, Alfred
Brendel, Cornelia
author_facet Thrun, Michael C.
Mack, Elisabeth K. M.
Neubauer, Andreas
Haferlach, Torsten
Frech, Miriam
Ultsch, Alfred
Brendel, Cornelia
author_sort Thrun, Michael C.
collection PubMed
description “Big omics data” provoke the challenge of extracting meaningful information with clinical benefit. Here, we propose a two-step approach, an initial unsupervised inspection of the structure of the high dimensional data followed by supervised analysis of gene expression levels, to reconstruct the surface patterns on different subtypes of acute myeloid leukemia (AML). First, Bayesian methodology was used, focusing on surface molecules encoded by cluster of differentiation (CD) genes to assess whether AML is a homogeneous group or segregates into clusters. Gene expressions of 390 patient samples measured using microarray technology and 150 samples measured via RNA-Seq were compared. Beyond acute promyelocytic leukemia (APL), a well-known AML subentity, the remaining AML samples were separated into two distinct subgroups. Next, we investigated which CD molecules would best distinguish each AML subgroup against APL, and validated discriminative molecules of both datasets by searching the scientific literature. Surprisingly, a comparison of both omics analyses revealed that CD339 was the only overlapping gene differentially regulated in APL and other AML subtypes. In summary, our two-step approach for gene expression analysis revealed two previously unknown subgroup distinctions in AML based on surface molecule expression, which may guide the differentiation of subentities in a given clinical–diagnostic context.
format Online
Article
Text
id pubmed-9687378
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-96873782022-11-25 A Bioinformatics View on Acute Myeloid Leukemia Surface Molecules by Combined Bayesian and ABC Analysis Thrun, Michael C. Mack, Elisabeth K. M. Neubauer, Andreas Haferlach, Torsten Frech, Miriam Ultsch, Alfred Brendel, Cornelia Bioengineering (Basel) Article “Big omics data” provoke the challenge of extracting meaningful information with clinical benefit. Here, we propose a two-step approach, an initial unsupervised inspection of the structure of the high dimensional data followed by supervised analysis of gene expression levels, to reconstruct the surface patterns on different subtypes of acute myeloid leukemia (AML). First, Bayesian methodology was used, focusing on surface molecules encoded by cluster of differentiation (CD) genes to assess whether AML is a homogeneous group or segregates into clusters. Gene expressions of 390 patient samples measured using microarray technology and 150 samples measured via RNA-Seq were compared. Beyond acute promyelocytic leukemia (APL), a well-known AML subentity, the remaining AML samples were separated into two distinct subgroups. Next, we investigated which CD molecules would best distinguish each AML subgroup against APL, and validated discriminative molecules of both datasets by searching the scientific literature. Surprisingly, a comparison of both omics analyses revealed that CD339 was the only overlapping gene differentially regulated in APL and other AML subtypes. In summary, our two-step approach for gene expression analysis revealed two previously unknown subgroup distinctions in AML based on surface molecule expression, which may guide the differentiation of subentities in a given clinical–diagnostic context. MDPI 2022-11-03 /pmc/articles/PMC9687378/ /pubmed/36354555 http://dx.doi.org/10.3390/bioengineering9110642 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Thrun, Michael C.
Mack, Elisabeth K. M.
Neubauer, Andreas
Haferlach, Torsten
Frech, Miriam
Ultsch, Alfred
Brendel, Cornelia
A Bioinformatics View on Acute Myeloid Leukemia Surface Molecules by Combined Bayesian and ABC Analysis
title A Bioinformatics View on Acute Myeloid Leukemia Surface Molecules by Combined Bayesian and ABC Analysis
title_full A Bioinformatics View on Acute Myeloid Leukemia Surface Molecules by Combined Bayesian and ABC Analysis
title_fullStr A Bioinformatics View on Acute Myeloid Leukemia Surface Molecules by Combined Bayesian and ABC Analysis
title_full_unstemmed A Bioinformatics View on Acute Myeloid Leukemia Surface Molecules by Combined Bayesian and ABC Analysis
title_short A Bioinformatics View on Acute Myeloid Leukemia Surface Molecules by Combined Bayesian and ABC Analysis
title_sort bioinformatics view on acute myeloid leukemia surface molecules by combined bayesian and abc analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687378/
https://www.ncbi.nlm.nih.gov/pubmed/36354555
http://dx.doi.org/10.3390/bioengineering9110642
work_keys_str_mv AT thrunmichaelc abioinformaticsviewonacutemyeloidleukemiasurfacemoleculesbycombinedbayesianandabcanalysis
AT mackelisabethkm abioinformaticsviewonacutemyeloidleukemiasurfacemoleculesbycombinedbayesianandabcanalysis
AT neubauerandreas abioinformaticsviewonacutemyeloidleukemiasurfacemoleculesbycombinedbayesianandabcanalysis
AT haferlachtorsten abioinformaticsviewonacutemyeloidleukemiasurfacemoleculesbycombinedbayesianandabcanalysis
AT frechmiriam abioinformaticsviewonacutemyeloidleukemiasurfacemoleculesbycombinedbayesianandabcanalysis
AT ultschalfred abioinformaticsviewonacutemyeloidleukemiasurfacemoleculesbycombinedbayesianandabcanalysis
AT brendelcornelia abioinformaticsviewonacutemyeloidleukemiasurfacemoleculesbycombinedbayesianandabcanalysis
AT thrunmichaelc bioinformaticsviewonacutemyeloidleukemiasurfacemoleculesbycombinedbayesianandabcanalysis
AT mackelisabethkm bioinformaticsviewonacutemyeloidleukemiasurfacemoleculesbycombinedbayesianandabcanalysis
AT neubauerandreas bioinformaticsviewonacutemyeloidleukemiasurfacemoleculesbycombinedbayesianandabcanalysis
AT haferlachtorsten bioinformaticsviewonacutemyeloidleukemiasurfacemoleculesbycombinedbayesianandabcanalysis
AT frechmiriam bioinformaticsviewonacutemyeloidleukemiasurfacemoleculesbycombinedbayesianandabcanalysis
AT ultschalfred bioinformaticsviewonacutemyeloidleukemiasurfacemoleculesbycombinedbayesianandabcanalysis
AT brendelcornelia bioinformaticsviewonacutemyeloidleukemiasurfacemoleculesbycombinedbayesianandabcanalysis