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Ran R, Brubaker DK. Enhanced annotation of CD45RA to distinguish T cell subsets in single-cell RNA-seq via machine learning. BIOINFORMATICS ADVANCES 2023; 3:vbad159. [PMID: 38023329 PMCID: PMC10676521 DOI: 10.1093/bioadv/vbad159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 10/20/2023] [Accepted: 11/03/2023] [Indexed: 12/01/2023]
Abstract
Motivation T cell heterogeneity presents a challenge for accurate cell identification, understanding their inherent plasticity, and characterizing their critical role in adaptive immunity. Immunologists have traditionally employed techniques such as flow cytometry to identify T cell subtypes based on a well-established set of surface protein markers. With the advent of single-cell RNA sequencing (scRNA-seq), researchers can now investigate the gene expression profiles of these surface proteins at the single-cell level. The insights gleaned from these profiles offer valuable clues and a deeper understanding of cell identity. However, CD45RA, the isoform of CD45 which distinguishes between naive/central memory T cells and effector memory/effector memory cells re-expressing CD45RA T cells, cannot be well profiled by scRNA-seq due to the difficulty in mapping short reads to genes. Results In order to facilitate cell-type annotation in T cell scRNA-seq analysis, we employed machine learning and trained a CD 45 RA + / - classifier on single-cell mRNA count data annotated with known CD45RA antibody levels provided by cellular indexing of transcriptomes and epitopes sequencing data. Among all the algorithms we tested, the trained support vector machine with a radial basis function kernel with optimized hyperparameters achieved a 99.96% accuracy on an unseen dataset. The multilayer perceptron classifier, the second most predictive method overall, also achieved a decent accuracy of 99.74%. Our simple yet robust machine learning approach provides a valid inference on the CD45RA level, assisting the cell identity annotation and further exploring the heterogeneity within human T cells. Based on the overall performance, we chose the support vector machine with a radial basis function kernel as the model implemented in our Python package scCD45RA. Availability and implementation The resultant package scCD45RA can be found at https://github.com/BrubakerLab/ScCD45RA and can be installed from the Python Package Index (PyPI) using the command "pip install sccd45ra."
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Affiliation(s)
- Ran Ran
- Department of Pathology, Center for Global Health and Diseases, Case Western Reserve University School of Medicine, Cleveland, OH 44106, United States
| | - Douglas K Brubaker
- Department of Pathology, Center for Global Health and Diseases, Case Western Reserve University School of Medicine, Cleveland, OH 44106, United States
- The Blood, Heart, Lung, and Immunology Research Center, Case Western Reserve University, University Hospitals of Cleveland, Cleveland, OH 44106, United States
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Gong A, Wang X, Wang X, Zhao Y, Cui Y. Twist1 Promoter Methylation Regulates the Proliferation and Apoptosis of Acute Myeloid Leukemia Cells via PI3K/AKT Pathway. Indian J Hematol Blood Transfus 2023; 39:25-32. [PMID: 36699440 PMCID: PMC9868029 DOI: 10.1007/s12288-022-01540-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 04/11/2022] [Indexed: 01/28/2023] Open
Abstract
Twist-related protein 1 (Twist1) is a widely recognized oncogene in acute myeloid leukemia (AML), and its promoter methylation is related with the progression of solid tumors. However, the association between Twist1 promoter methylation and AML has not been well studied. Twist1 mRNA expression was detected using quantitative real-time polymerase chain reaction (qRT-PCR). The protein levels of Twist1 and phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) signal were measured via western blotting. Methylation-specific PCR was performed to detect the methylation status of Twist1 promoter. CCK-8 assay and flow cytometry were used to reveal cellular biological effects. Twist1 expression and promoter methylation level were significantly upregulated in AML tissues and cell lines and were further downregulated in demethylating agent 5'-azacitidine (5-Aza)-treated cells. Ectopic expression of Twist1 increased AML cell viability, while reducing apoptosis, and attenuated the effects of 5-Aza on the proliferation and apoptosis. We also found that the PI3K/AKT signaling pathway was positively regulated by Twist1. Our findings revealed that Twist1 accelerates the tumorigenesis of AML cells by promoting its promoter methylation via the activation of PI3K/AKT signaling pathway.
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Affiliation(s)
- Aihong Gong
- Department of Medical Records Statistics Room, General Hospital of Ningxia Medical University, No. 692, Shengli South Street, Xingqing District, Yinchuan, 750004 Ningxia China
| | - Xiaojia Wang
- Department of Medical Records Statistics Room, General Hospital of Ningxia Medical University, No. 692, Shengli South Street, Xingqing District, Yinchuan, 750004 Ningxia China
| | - Xuewei Wang
- Department of Medical Records Statistics Room, General Hospital of Ningxia Medical University, No. 692, Shengli South Street, Xingqing District, Yinchuan, 750004 Ningxia China
| | - Ying Zhao
- Department of Hematology, General Hospital of Ningxia Medical University, Yinchuan, 750003 Ningxia China
| | - Yanan Cui
- Department of Medical Records Statistics Room, General Hospital of Ningxia Medical University, No. 692, Shengli South Street, Xingqing District, Yinchuan, 750004 Ningxia China
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A Novel Prognostic Model for Acute Myeloid Leukemia Based on Gene Set Variation Analysis. JOURNAL OF ONCOLOGY 2022; 2022:7727424. [DOI: 10.1155/2022/7727424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 10/24/2022] [Accepted: 10/26/2022] [Indexed: 11/22/2022]
Abstract
Acute myeloid leukemia (AML) is a malignant hematological malignancy with a poor prognosis. Risk stratification of patients with AML is mainly based on the characteristics of cytogenetics and molecular genetics; however, patients with favorable genetics may have a poor prognosis. Here, we focused on the activity changes of immunologic and hallmark gene sets in the AML population. Based on the enrichment score of gene sets by gene set variation analysis (GSVA), we identified three AML subtypes by the nonnegative matrix factorization (NMF) algorithm in the TCGA cohort. AML patients in subgroup 1 had worse overall survival (OS) than subgroups 2 and 3 (
). The median overall survival (mOS) of subgroups 1–3 was 0.4, 2.2, and 1.7 years, respectively. Clinical characteristics, including age and FAB classification, were significantly different among each subgroup. Using the least absolute shrinkage and selection operator (LASSO) regression method, we discovered three prognostic gene sets and established the final prognostic model based on them. Patients in the high-risk group had significantly shorter OS than those in the low-risk group in the TCGA cohort (
) with mOS of 2.2 and 0.7 years in the low- and high-risk groups, respectively. The results were further validated in the GSE146173 and GSE12417 cohorts. We further identified the key genes of prognostic gene sets using a protein-protein interaction network. In conclusion, the study established and validated a novel prognostic model for risk stratification in AML, which provides a new perspective for accurate prognosis assessment.
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Zhang S, Wang Q, Xia H, Liu H. A Novel Prognostic Model for Acute Myeloid Leukemia Based on Gene Set Variation Analysis. JOURNAL OF ONCOLOGY 2022; 2022:1-13. [DOI: g/10.1155/2022/7727424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Acute myeloid leukemia (AML) is a malignant hematological malignancy with a poor prognosis. Risk stratification of patients with AML is mainly based on the characteristics of cytogenetics and molecular genetics; however, patients with favorable genetics may have a poor prognosis. Here, we focused on the activity changes of immunologic and hallmark gene sets in the AML population. Based on the enrichment score of gene sets by gene set variation analysis (GSVA), we identified three AML subtypes by the nonnegative matrix factorization (NMF) algorithm in the TCGA cohort. AML patients in subgroup 1 had worse overall survival (OS) than subgroups 2 and 3 (
). The median overall survival (mOS) of subgroups 1–3 was 0.4, 2.2, and 1.7 years, respectively. Clinical characteristics, including age and FAB classification, were significantly different among each subgroup. Using the least absolute shrinkage and selection operator (LASSO) regression method, we discovered three prognostic gene sets and established the final prognostic model based on them. Patients in the high-risk group had significantly shorter OS than those in the low-risk group in the TCGA cohort (
) with mOS of 2.2 and 0.7 years in the low- and high-risk groups, respectively. The results were further validated in the GSE146173 and GSE12417 cohorts. We further identified the key genes of prognostic gene sets using a protein-protein interaction network. In conclusion, the study established and validated a novel prognostic model for risk stratification in AML, which provides a new perspective for accurate prognosis assessment.
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Affiliation(s)
- Shuai Zhang
- Department of Hematology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Bejing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Qianqian Wang
- Peking University China-Japan Friendship School of Clinical Medicine, Bejing, China
| | - Haoran Xia
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Bejing, China
| | - Hui Liu
- Department of Hematology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Bejing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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Zhong QH, Zha SW, Lau ATY, Xu YM. Recent knowledge of NFATc4 in oncogenesis and cancer prognosis. Cancer Cell Int 2022; 22:212. [PMID: 35698138 PMCID: PMC9190084 DOI: 10.1186/s12935-022-02619-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 05/20/2022] [Indexed: 02/05/2023] Open
Abstract
Nuclear factor of activated T-cells, cytoplasmic 4 (NFATc4), a transcription factor of NFAT family, which is activated by Ca2+/calcineurin signaling. Recently, it is reported that aberrantly activated NFATc4 participated and modulated in the initiation, proliferation, invasion, and metastasis of various cancers (including cancers of the lung, breast, ovary, cervix, skin, liver, pancreas, as well as glioma, primary myelofibrosis and acute myelocytic leukemia). In this review, we cover the latest knowledge on NFATc4 expression pattern, post-translational modification, epigenetic regulation, transcriptional activity regulation and its downstream targets. Furthermore, we perform database analysis to reveal the prognostic value of NFATc4 in various cancers and discuss the current unexplored areas of NFATc4 research. All in all, the result from these studies strongly suggest that NFATc4 has the potential as a molecular therapeutic target in multiple human cancer types.
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Affiliation(s)
- Qiu-Hua Zhong
- Laboratory of Cancer Biology and Epigenetics, Department of Cell Biology and Genetics, Shantou University Medical College, 22 Xinling Road, Shantou, Guangdong 515041 People’s Republic of China
| | - Si-Wei Zha
- Laboratory of Cancer Biology and Epigenetics, Department of Cell Biology and Genetics, Shantou University Medical College, 22 Xinling Road, Shantou, Guangdong 515041 People’s Republic of China
| | - Andy T. Y. Lau
- Laboratory of Cancer Biology and Epigenetics, Department of Cell Biology and Genetics, Shantou University Medical College, 22 Xinling Road, Shantou, Guangdong 515041 People’s Republic of China
| | - Yan-Ming Xu
- Laboratory of Cancer Biology and Epigenetics, Department of Cell Biology and Genetics, Shantou University Medical College, 22 Xinling Road, Shantou, Guangdong 515041 People’s Republic of China
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Zhang L, Ke W, Hu P, Li Z, Geng W, Guo Y, Song B, Jiang H, Zhang X, Wan C. N6-Methyladenosine-Related lncRNAs Are Novel Prognostic Markers and Predict the Immune Landscape in Acute Myeloid Leukemia. Front Genet 2022; 13:804614. [PMID: 35615374 PMCID: PMC9125310 DOI: 10.3389/fgene.2022.804614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 04/06/2022] [Indexed: 12/16/2022] Open
Abstract
Background: Acute myelocytic leukemia (AML) is one of the hematopoietic cancers with an unfavorable prognosis. However, the prognostic value of N 6-methyladenosine-associated long non-coding RNAs (lncRNAs) in AML remains elusive. Materials and Methods: The transcriptomic data of m6A-related lncRNAs were collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. AML samples were classified into various subgroups according to the expression of m6A-related lncRNAs. The differences in terms of biological function, tumor immune microenvironment, copy number variation (CNV), and drug sensitivity in AML between distinct subgroups were investigated. Moreover, an m6A-related lncRNA prognostic model was established to evaluate the prognosis of AML patients. Results: Nine prognosis-related m6A-associated lncRNAs were selected to construct a prognosis model. The accuracy of the model was further determined by the Kaplan–Meier analysis and time-dependent receiver operating characteristic (ROC) curve. Then, AML samples were classified into high- and low-risk groups according to the median value of risk scores. Gene set enrichment analysis (GSEA) demonstrated that samples with higher risks were featured with aberrant immune-related biological processes and signaling pathways. Notably, the high-risk group was significantly correlated with an increased ImmuneScore and StromalScore, and distinct immune cell infiltration. In addition, we discovered that the high-risk group harbored higher IC50 values of multiple chemotherapeutics and small-molecule anticancer drugs, especially TW.37 and MG.132. In addition, a nomogram was depicted to assess the overall survival (OS) of AML patients. The model based on the median value of risk scores revealed reliable accuracy in predicting the prognosis and survival status. Conclusion: The present research has originated a prognostic risk model for AML according to the expression of prognostic m6A-related lncRNAs. Notably, the signature might also serve as a novel biomarker that could guide clinical applications, for example, selecting AML patients who could benefit from immunotherapy.
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Affiliation(s)
- Lulu Zhang
- Department of Hematology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
- *Correspondence: Lulu Zhang,
| | - Wen Ke
- Department of Osteology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Pin Hu
- Department of Hematology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Zhangzhi Li
- Department of Hematology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Wei Geng
- Department of Hematology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Yigang Guo
- Department of Hematology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Bin Song
- Department of Hematology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Hua Jiang
- Department of Hematology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Xia Zhang
- Department of Hematology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Chucheng Wan
- Department of Hematology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
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