ArticleFrontiers in cell and developmental biology2020
Multi-Omics Analysis of Acute Lymphoblastic Leukemia Identified the Methylation and Expression Differences Between BCP-ALL and T-ALL.
Article in Frontiers in cell and developmental biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Article
- Data-driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes.Molecular oncology · 2025Article
- Centromeres in cancer: Unraveling the link between chromosomal instability and tumorigenesis.Medical oncology (Northwood, London, England) · 2024Review
- A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction.Heliyon · 2024Article
- Pan-Cancer Analysis of GALNT6 with Potential Implications for Prognosis and Tumor Microenvironment in Human Cancer Based on Bioinformatics and qPCR Verification.International journal of general medicine · 2024Article
- Increasing Complexity of Molecular Landscapes in Human Hematopoietic Stem and Progenitor Cells during Development and Aging.International journal of molecular sciences · 2022Review
- Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer.Frontiers in genetics · 2022Review
- LncRNAs serve as novel biomarkers for diagnosis and prognosis of childhood ALL.Biomarker research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute lymphoblastic leukemia (ALL) as a common cancer is a heterogeneous disease which is mainly divided into BCP-ALL and T-ALL, accounting for 80-85% and 15-20%, respectively. There are many differences between BCP-ALL and T-ALL, including prognosis, treatment, drug screening, gene research and so on. In this study, starting with methylation and gene expression data, we analyzed the molecular differences between BCP-ALL and T-ALL and identified the multi-omics signatures using Boruta and Monte Carlo feature selection methods. There were 7 expression signature genes (CD3D, VPREB3, HLA-DRA, PAX5, BLNK, GALNT6, SLC4A8) and 168 methylation sites corresponding to 175 methylation signature genes. The overall accuracy, accuracy of BCP-ALL, accuracy of T-ALL of the RIPPER (Repeated Incremental Pruning to Produce Error Reduction) classifier using these signatures evaluated with 10-fold cross validation repeated 3 times were 0.973, 0.990, and 0.933, respectively. Two overlapped genes between 175 methylation signature genes and 7 expression signature genes were CD3D and VPREB3. The network analysis of the methylation and expression signature genes suggested that their common gene, CD3D, was not only different on both methylation and expression levels, but also played a key regulatory role as hub on the network. Our results provided insights of understanding the underlying molecular mechanisms of ALL and facilitated more precision diagnosis and treatment of ALL.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.