Identifying Candidate Gene Drivers Associated with Relapse in Pediatric T-Cell Acute Lymphoblastic Leukemia Using a Gene Co-Expression Network Approach.
Anthony KypraiosJuba BennourVéronique ImbertLéa DavidJulien CalvoFrançoise PflumioRaphaël BonnetMarie CouraletVirginie MagnoneKevin LebrigandPascal BarbryPierre S RohrlichJean-François PeyronPublished in: Cancers (2024)
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.
Keyphrases
- genome wide
- genome wide identification
- acute lymphoblastic leukemia
- bioinformatics analysis
- transcription factor
- genome wide analysis
- dna methylation
- network analysis
- single cell
- copy number
- free survival
- end stage renal disease
- immune response
- poor prognosis
- gene expression
- prognostic factors
- chronic kidney disease
- young adults
- magnetic resonance imaging
- computed tomography
- deep learning
- binding protein
- high throughput
- cell proliferation
- inflammatory response
- heat stress
- heat shock