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Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning
BCR-ABL1–positive acute leukemia can be classified into three disease categories: B-lymphoblastic leukemia (B-ALL), acute myeloid leukemia (AML), and mixed-phenotype acute leukemia (MPAL). We conducted an integrative analysis of RNA sequencing (RNA-seq) data obtained from 12 BCR-ABL1–positive B-ALL,...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8419339/ https://www.ncbi.nlm.nih.gov/pubmed/34497767 http://dx.doi.org/10.3389/fonc.2021.717616 |
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author | Lee, Jaewoong Cho, Sungmin Hong, Seong-Eui Kang, Dain Choi, Hayoung Lee, Jong-Mi Yoon, Jae-Ho Cho, Byung-Sik Lee, Seok Kim, Hee-Je Kim, Myungshin Kim, Yonggoo |
author_facet | Lee, Jaewoong Cho, Sungmin Hong, Seong-Eui Kang, Dain Choi, Hayoung Lee, Jong-Mi Yoon, Jae-Ho Cho, Byung-Sik Lee, Seok Kim, Hee-Je Kim, Myungshin Kim, Yonggoo |
author_sort | Lee, Jaewoong |
collection | PubMed |
description | BCR-ABL1–positive acute leukemia can be classified into three disease categories: B-lymphoblastic leukemia (B-ALL), acute myeloid leukemia (AML), and mixed-phenotype acute leukemia (MPAL). We conducted an integrative analysis of RNA sequencing (RNA-seq) data obtained from 12 BCR-ABL1–positive B-ALL, AML, and MPAL samples to evaluate its diagnostic utility. RNA-seq facilitated the identification of all p190 BCR-ABL1 with accurate splicing sites and a new gene fusion involving MAP2K2. Most of the clinically significant mutations were also identified including single-nucleotide variations, insertions, and deletions. In addition, RNA-seq yielded differential gene expression profile according to the disease category. Therefore, we selected 368 genes differentially expressed between AML and B-ALL and developed two differential diagnosis models based on the gene expression data using 1) scoring algorithm and 2) machine learning. Both models showed an excellent diagnostic accuracy not only for our 12 BCR-ABL1–positive cases but also for 427 public gene expression datasets from acute leukemias regardless of specific genetic aberration. This is the first trial to develop models of differential diagnosis using RNA-seq, especially to evaluate the potential role of machine learning in identifying the disease category of acute leukemia. The integrative analysis of gene expression data by RNA-seq facilitates the accurate differential diagnosis of acute leukemia with successful detection of significant gene fusion and/or mutations, which warrants further investigation. |
format | Online Article Text |
id | pubmed-8419339 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84193392021-09-07 Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning Lee, Jaewoong Cho, Sungmin Hong, Seong-Eui Kang, Dain Choi, Hayoung Lee, Jong-Mi Yoon, Jae-Ho Cho, Byung-Sik Lee, Seok Kim, Hee-Je Kim, Myungshin Kim, Yonggoo Front Oncol Oncology BCR-ABL1–positive acute leukemia can be classified into three disease categories: B-lymphoblastic leukemia (B-ALL), acute myeloid leukemia (AML), and mixed-phenotype acute leukemia (MPAL). We conducted an integrative analysis of RNA sequencing (RNA-seq) data obtained from 12 BCR-ABL1–positive B-ALL, AML, and MPAL samples to evaluate its diagnostic utility. RNA-seq facilitated the identification of all p190 BCR-ABL1 with accurate splicing sites and a new gene fusion involving MAP2K2. Most of the clinically significant mutations were also identified including single-nucleotide variations, insertions, and deletions. In addition, RNA-seq yielded differential gene expression profile according to the disease category. Therefore, we selected 368 genes differentially expressed between AML and B-ALL and developed two differential diagnosis models based on the gene expression data using 1) scoring algorithm and 2) machine learning. Both models showed an excellent diagnostic accuracy not only for our 12 BCR-ABL1–positive cases but also for 427 public gene expression datasets from acute leukemias regardless of specific genetic aberration. This is the first trial to develop models of differential diagnosis using RNA-seq, especially to evaluate the potential role of machine learning in identifying the disease category of acute leukemia. The integrative analysis of gene expression data by RNA-seq facilitates the accurate differential diagnosis of acute leukemia with successful detection of significant gene fusion and/or mutations, which warrants further investigation. Frontiers Media S.A. 2021-08-23 /pmc/articles/PMC8419339/ /pubmed/34497767 http://dx.doi.org/10.3389/fonc.2021.717616 Text en Copyright © 2021 Lee, Cho, Hong, Kang, Choi, Lee, Yoon, Cho, Lee, Kim, Kim and Kim https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Lee, Jaewoong Cho, Sungmin Hong, Seong-Eui Kang, Dain Choi, Hayoung Lee, Jong-Mi Yoon, Jae-Ho Cho, Byung-Sik Lee, Seok Kim, Hee-Je Kim, Myungshin Kim, Yonggoo Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning |
title | Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning |
title_full | Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning |
title_fullStr | Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning |
title_full_unstemmed | Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning |
title_short | Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning |
title_sort | integrative analysis of gene expression data by rna sequencing for differential diagnosis of acute leukemia: potential application of machine learning |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8419339/ https://www.ncbi.nlm.nih.gov/pubmed/34497767 http://dx.doi.org/10.3389/fonc.2021.717616 |
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