Background/Objectives: Acute lymphoblastic leukaemia (ALL) is a biologically heterogeneous disease in which transcriptional dysregulation contributes to disease onset and progression. Despite survival rates exceeding 90% in high-income countries, relapsed and high-risk cases remain a major clinical challenge, highlighting the need for improved molecular stratification, namely the classification of patients based on genetic and transcriptomic features associated with prognosis, therapeutic response, and disease biology, as well as for the identification of novel therapeutic targets. Methods: We performed an integrative cross-platform analysis to investigate the expression and potential relevance of three candidate genes: PXDN, TCF4, and TSPAN7 in ALL. Gene expression was interrogated across the MILE microarray cohort and the St. Jude Cloud PeCan paediatric RNA-sequencing dataset. Results: Differential expression analyses consistently showed significant upregulation of TCF4 and PXDN in B-cell ALL (B-ALL) across both platforms (adjusted p < 0.001), while TSPAN7 displayed higher expression in T-cell ALL (T-ALL) and variable upregulation in B-ALL. These findings were supported by preliminary validation using quantitative PCR in paediatric B-ALL samples. To explore potential functional associations, we performed gene regulatory network inference using scGraphVerse, identifying differentially expressed genes putatively linked to PXDN, TCF4, and TSPAN7. Structural modelling using AlphaFold suggested candidate protein–protein interaction interfaces for a subset of these genes, although these predictions require experimental validation. Functional enrichment analysis indicated an over-representation of developmental pathways associated with PXDN- and TCF4-related networks, whereas TSPAN7-associated genes were enriched in processes linked to neuronal lineage development. Conclusions: Collectively, our results identify, for the first time, PXDN, TCF4 and TSPAN7 as differentially expressed genes in ALL and highlight the usefulness of integrative transcriptomic analyses across independent datasets. While limited by small-scale experimental validation and reliance on computational predictions, this study provides a framework for prioritising candidate genes and generates testable hypotheses regarding their potential involvement in leukaemia-associated molecular pathways.
Primo et al. (Wed,) studied this question.