Identifying Candidate Gene Drivers Associated with Relapse in Pediatric T-Cell Acute Lymphoblastic Leukemia Using a Gene Co-Expression Network Approach

Pediatric T-cell Acute Lymphoblastic Leukemia (T-ALL) relapses are still associated with a dismal outcome, justifying the search for new therapeutic targets and relapse biomarkers. Using single-cell RNA sequencing (scRNAseq) data from three paired samples of pediatric T-ALL at diagnosis and relapse,...

Full description

Saved in:
Bibliographic Details
Published in:Cancers 2024-05, Vol.16 (9), p.1667
Main Authors: Kypraios, Anthony, Bennour, Juba, Imbert, Véronique, David, Léa, Calvo, Julien, Pflumio, Françoise, Bonnet, Raphaël, Couralet, Marie, Magnone, Virginie, Lebrigand, Kevin, Barbry, Pascal, Rohrlich, Pierre S, Peyron, Jean-François
Format: Article
Language:English
Subjects:
Acute lymphoblastic leukemia
Acute lymphocytic leukemia
Antigen presentation
Bone marrow
Cancer
Care and treatment
Cells
Cytoskeleton
Datasets
Development and progression
Diagnosis
Gene expression
Genes
Genetic aspects
Genetic transcription
Genomics
Health aspects
Immune response
Krueppel-like factor
Leukemia
Lymphocytes T
Medical prognosis
Molecular modelling
NF-κB protein
Oncology, Experimental
Patients
Pediatrics
Physiological aspects
Ribosomal proteins
RNA
RNA sequencing
Sequence analysis
Statistics
Stress response
T cells
Therapeutic targets
Transcriptomics
Variables
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Pediatric T-cell Acute Lymphoblastic Leukemia (T-ALL) relapses are still associated with a dismal outcome, justifying the search for new therapeutic targets and relapse biomarkers. Using single-cell RNA sequencing (scRNAseq) data from three paired samples of pediatric T-ALL at diagnosis and relapse, we first conducted a high-dimensional weighted gene co-expression network analysis (hdWGCNA). This analysis highlighted several gene co-expression networks (GCNs) and identified relapse-associated hub genes, which are considered potential driver genes. Shared relapse-expressed genes were found to be related to antigen presentation (HLA, B2M), cytoskeleton remodeling (TUBB, TUBA1B), translation (ribosomal proteins, EIF1, EEF1B2), immune responses (MIF, EMP3), stress responses (UBC, HSP90AB1/AA1), metabolism (FTH1, NME1/2, ARCL4C), and transcriptional remodeling (NF-κB family genes, FOS-JUN, KLF2, or KLF6). We then utilized sparse partial least squares discriminant analysis to select from a pool of 481 unique leukemic hub genes, which are the genes most discriminant between diagnosis and relapse states (comprising 44, 35, and 31 genes, respectively, for each patient). Applying a Cox regression method to these patient-specific genes, along with transcriptomic and clinical data from the TARGET-ALL AALL0434 cohort, we generated three model gene signatures that efficiently identified relapsed patients within the cohort. Overall, our approach identified new potential relapse-associated genes and proposed three model gene signatures associated with lower survival rates for high-score patients.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers16091667