Multi-Omics Analysis of Acute Lymphoblastic Leukemia Identified the Methylation and Expression Differences Between BCP-ALL and T-ALL

Li, Jin-Fan; Ma, Xiaojing; Ying, Lin-Lin; Tong, Ying; Xiang, Xueping

doi:10.3389/fcell.2020.622393

Cited by 8 publications

(8 citation statements)

References 55 publications

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“…Increased gene expression of this gene can activate the pro-survival phosphatidylinositol-3-OH kinase pathway [100]. Recently, another study also analyzed molecular differences between B-ALL and T-ALL and found VPREB3 as a methylation and expression signature gene [101]. EBF1 is a transcription factor involved in B-cell lineage specification and commitment [102], which explains its increased expression in B-ALL compared to T-ALL ( Table 3 ).…”

Section: Resultsmentioning

confidence: 99%

“…A subset of these markers has also previously been implicated in differences between B- and T-ALL. For example, LINC00114 and CCN2 have previously been found to be upregulated in B-ALL compared to T-ALL while deletions of EBF1 have been associated with B-ALL [102,103] and VPREB3 has been found as a methylation and expression signature gene between B- and T-ALL [101]. Comparing the different biotypes of the predicted markers, the protein-coding genes are described the most in literature.…”

Section: Discussionmentioning

confidence: 99%

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Data-driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes

Nourbakhsh,

Tom,

Schrøder Lassen

et al. 2024

Preprint

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Acute lymphoblastic leukemia (ALL), the most common cancer in children, is overall divided into two subtypes, B-cell precursor ALL (B-ALL) and T-cell ALL (T-ALL), which have different molecular characteristics. Despite massive progress in understanding the disease trajectories of ALL, ALL remains a major cause of death in children. Thus, further research exploring the biological foundations of ALL is essential. Here, we examined the diagnostic, prognostic, and therapeutic potential of gene expression data in pediatric patients with ALL. We discovered a subset of expression markers differentiating B- and T-ALL:CCN2,VPREB3,NDST3,EBF1, RN7SKP185, RN7SKP291, SNORA73B, RN7SKP255, SNORA74A, RN7SKP48, RN7SKP80, LINC00114, a novel gene (ENSG00000227706), and 7SK. The expression level of these markers all demonstrated significant effects on survival of the patients, comparing the two subtypes. We also discovered four expression subgroups in the expression data with eight genes driving separation between two of these predicted subgroups. A subset of the 14 markers could separate B- and T-ALL in an independent cohort of patients with ALL. This study can enhance our knowledge of the transcriptomic profile of different ALL subtypes.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Data-driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes

Nourbakhsh,

Tom,

Schrøder Lassen

et al. 2024

Preprint

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“…HLA genes are involved in differentiating self- and non-self- cells in tumors and are candidate genetic susceptibility loci for childhood ALL. It has been reported that the HLA-DRA and HLA-DQB1 genes are associated with the progression of childhood ALL [ 41 , 42 ], and elevated HLA-DM expression contributes to childhood ALL [ 43 ]. CD molecules are commonly used as cell markers to define cells with certain functions or properties and to classify hematological malignancies.…”

Section: Discussionmentioning

confidence: 99%

LncRNAs serve as novel biomarkers for diagnosis and prognosis of childhood ALL

Huang

Xie

et al. 2021

Biomark Res

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Background Although some studies have demonstrated that lncRNAs are dysregulated in hematopoietic malignancies and may regulate the progression of leukemia, the detailed mechanism underlying tumorigenesis is still unclear. This study aimed to investigate lncRNAs that are differentially expressed in childhood B-cell acute lymphoblastic leukemia (B-ALL) and T-cell acute lymphoblastic leukemia (T-ALL) and their potential roles in the progression of childhood ALL. Methods Microarrays were used to detect differentially expressed lncRNAs and mRNAs. Several aberrantly expressed lncRNAs were validated by qRT-PCR. Leukemia-free survival was analyzed using the Kaplan–Meier method with a log-rank test. The co-expression correlations of lncRNAs and mRNAs were determined by Spearman’s correlation coefficient. CCK-8 assays and flow cytometry were performed to measure cell proliferation and apoptosis. Results We revealed that many lncRNAs were abnormally expressed in B-ALL and T-ALL. LncRNA/mRNA co-expression and the gene locus network showed that dysregulated lncRNAs are involved in diverse cellular processes. We also assessed the diagnostic value of the differentially expressed lncRNAs and confirmed the optimal combination of TCONS_00026679, uc002ubt.1, ENST00000411904, and ENST00000547644 with an area under the curve of 0.9686 [95 % CI: 0.9369–1.000, P < 0.001], with 90.7 % sensitivity and 92.19 % specificity, at a cut-off point of -0.5700 to distinguish childhood B-ALL patients from T-ALL patients, implying that these specific lncRNAs may have potential to detect subsets of childhood ALL. Notably, we found that the 8-year leukemia-free survival of patients with high TCONS_00026679 (p = 0.0081), ENST00000522339 (p = 0.0484), ENST00000499583 (p = 0.0381), ENST00000457217 (p = 0.0464), and ENST00000451368 (p = 0.0298) expression levels was significantly higher than that of patients with low expression levels of these lncRNAs, while patients with high uc002ubt.1 (p = 0.0499) and ENST00000547644 (p = 0.0451) expression levels exhibited markedly shorter 8-year leukemia-free survival. In addition, some lncRNAs were found to play different roles in cell proliferation and apoptosis in T-ALL and B-ALL. Conclusions Dysregulated lncRNAs involved in different regulatory mechanisms underlying the progression of childhood T-ALL and B-ALL might serve as novel biomarkers to distinguish ALL subsets and indicate poor outcomes.

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“…For example, as a result of these studies, 7 expression signature genes and 175 methylation signature genes were obtained, in which two genes, including CD3D and VPREB3 were common. In addition, it has been implied that CD3D gene has a major regulatory contribution in the cell and molecular process of this type of cancer (Li et al, 2020).…”

Section: In Multi-omics Data Analyses Of Cancermentioning

confidence: 99%

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

et al. 2022

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Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.

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Multi-Omics Analysis of Acute Lymphoblastic Leukemia Identified the Methylation and Expression Differences Between BCP-ALL and T-ALL

Cited by 8 publications

References 55 publications

Data-driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes

Data-driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes

LncRNAs serve as novel biomarkers for diagnosis and prognosis of childhood ALL

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

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