A novel stemness classification in acute myeloid leukemia by the stemness index and the identification of cancer stem cell-related biomarkers
BackgroundStem cells play an important role in acute myeloid leukemia (AML). However, their precise effect on AML tumorigenesis and progression remains unclear.MethodsThe present study aimed to characterize stem cell-related gene expression and identify stemness biomarker genes in AML. We calculated...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-06-01
|
| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1202825/full |
| _version_ | 1797799994389954560 |
|---|---|
| author | Yue Huang Zhuo Zhang Zhuo Zhang Meijuan Sui Yang Li Yi Hu Haiyu Zhang Fan Zhang |
| author_facet | Yue Huang Zhuo Zhang Zhuo Zhang Meijuan Sui Yang Li Yi Hu Haiyu Zhang Fan Zhang |
| author_sort | Yue Huang |
| collection | DOAJ |
| description | BackgroundStem cells play an important role in acute myeloid leukemia (AML). However, their precise effect on AML tumorigenesis and progression remains unclear.MethodsThe present study aimed to characterize stem cell-related gene expression and identify stemness biomarker genes in AML. We calculated the stemness index (mRNAsi) based on transcription data using the one-class logistic regression (OCLR) algorithm for patients in the training set. According to the mRNAsi score, we performed consensus clustering and identified two stemness subgroups. Eight stemness-related genes were identified as stemness biomarkers through gene selection by three machine learning methods.ResultsWe found that patients in stemness subgroup I had a poor prognosis and benefited from nilotinib, MK-2206 and axitinib treatment. In addition, the mutation profiles of these two stemness subgroups were different, which suggested that patients in different subgroups had different biological processes. There was a strong significant negative correlation between mRNAsi and the immune score (r= -0.43, p<0.001). Furthermore, we identified eight stemness-related genes that have potential to be biomarkers, including SLC43A2, CYBB, CFP, GRN, CST3, TIMP1, CFD and IGLL1. These genes, except IGLL1, had a negative correlation with mRNAsi. SLC43A2 is expected to be a potential stemness-related biomarker in AML.ConclusionOverall, we established a novel stemness classification using the mRNAsi score and eight stemness-related genes that may be biomarkers. Clinical decision-making should be guided by this new signature in prospective studies. |
| first_indexed | 2024-03-13T04:28:20Z |
| format | Article |
| id | doaj.art-d9ac4c0e7ed54c788c45cce89a06e031 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-13T04:28:20Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-d9ac4c0e7ed54c788c45cce89a06e0312023-06-20T03:46:50ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-06-011410.3389/fimmu.2023.12028251202825A novel stemness classification in acute myeloid leukemia by the stemness index and the identification of cancer stem cell-related biomarkersYue Huang0Zhuo Zhang1Zhuo Zhang2Meijuan Sui3Yang Li4Yi Hu5Haiyu Zhang6Fan Zhang7Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, ChinaNational Health Commission (NHC) Key Laboratory of Cell Transplantation, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaDepartment of Hematology, Southern University of Science and Technology Hospital, Shenzhen, ChinaKey Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaMedical Insurance Office, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaCenter for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, ChinaKey Laboratory of Cardiovascular Disease Acousto-Optic Electromagnetic Diagnosis and Treatment in Heilongjiang Province, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaNational Health Commission (NHC) Key Laboratory of Cell Transplantation, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaBackgroundStem cells play an important role in acute myeloid leukemia (AML). However, their precise effect on AML tumorigenesis and progression remains unclear.MethodsThe present study aimed to characterize stem cell-related gene expression and identify stemness biomarker genes in AML. We calculated the stemness index (mRNAsi) based on transcription data using the one-class logistic regression (OCLR) algorithm for patients in the training set. According to the mRNAsi score, we performed consensus clustering and identified two stemness subgroups. Eight stemness-related genes were identified as stemness biomarkers through gene selection by three machine learning methods.ResultsWe found that patients in stemness subgroup I had a poor prognosis and benefited from nilotinib, MK-2206 and axitinib treatment. In addition, the mutation profiles of these two stemness subgroups were different, which suggested that patients in different subgroups had different biological processes. There was a strong significant negative correlation between mRNAsi and the immune score (r= -0.43, p<0.001). Furthermore, we identified eight stemness-related genes that have potential to be biomarkers, including SLC43A2, CYBB, CFP, GRN, CST3, TIMP1, CFD and IGLL1. These genes, except IGLL1, had a negative correlation with mRNAsi. SLC43A2 is expected to be a potential stemness-related biomarker in AML.ConclusionOverall, we established a novel stemness classification using the mRNAsi score and eight stemness-related genes that may be biomarkers. Clinical decision-making should be guided by this new signature in prospective studies.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1202825/fullAMLcancer stem cellmRNAsibiomarkersSLC43A2 |
| spellingShingle | Yue Huang Zhuo Zhang Zhuo Zhang Meijuan Sui Yang Li Yi Hu Haiyu Zhang Fan Zhang A novel stemness classification in acute myeloid leukemia by the stemness index and the identification of cancer stem cell-related biomarkers Frontiers in Immunology AML cancer stem cell mRNAsi biomarkers SLC43A2 |
| title | A novel stemness classification in acute myeloid leukemia by the stemness index and the identification of cancer stem cell-related biomarkers |
| title_full | A novel stemness classification in acute myeloid leukemia by the stemness index and the identification of cancer stem cell-related biomarkers |
| title_fullStr | A novel stemness classification in acute myeloid leukemia by the stemness index and the identification of cancer stem cell-related biomarkers |
| title_full_unstemmed | A novel stemness classification in acute myeloid leukemia by the stemness index and the identification of cancer stem cell-related biomarkers |
| title_short | A novel stemness classification in acute myeloid leukemia by the stemness index and the identification of cancer stem cell-related biomarkers |
| title_sort | novel stemness classification in acute myeloid leukemia by the stemness index and the identification of cancer stem cell related biomarkers |
| topic | AML cancer stem cell mRNAsi biomarkers SLC43A2 |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1202825/full |
| work_keys_str_mv | AT yuehuang anovelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers AT zhuozhang anovelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers AT zhuozhang anovelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers AT meijuansui anovelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers AT yangli anovelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers AT yihu anovelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers AT haiyuzhang anovelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers AT fanzhang anovelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers AT yuehuang novelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers AT zhuozhang novelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers AT zhuozhang novelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers AT meijuansui novelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers AT yangli novelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers AT yihu novelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers AT haiyuzhang novelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers AT fanzhang novelstemnessclassificationinacutemyeloidleukemiabythestemnessindexandtheidentificationofcancerstemcellrelatedbiomarkers |