Individualized pseudogenes networks for survival prognosis in B-cell acute lymphoblastic leukemia

B-cell acute lymphoblastic leukemia (B-ALL) is the most common pediatric cancer, with significant advancements in risk stratification driven by next-generation sequencing (NGS). While genomic alterations have been extensively studied in B-ALL, the role of pseudogenes remains largely unexplored due to their historical classification as non-functional sequences. However, recent evidence suggests that pseudogenes may play regulatory roles in cancer. Our previous work identified changes in pseudogene connectivity in B-ALL bone marrow samples compared to normal samples, with specific pseudogene clusters being overexpressed in the malignant phenotype. These findings suggest that pseudogene co-expression patterns may contain biologically and clinically relevant information. To evaluate the role of the coexpression between pseudogenes in B-ALL and its impact on patient outcomes, we constructed single-sample co-expression networks (SSNs) using RNA-seq data from two independent B-ALL cohorts (n = 1,416). Unsupervised clustering of these networks revealed patient subgroups with distinct overall survival (OS) profiles. Differential co-expression and network topology analyses identified EEF1A1P12 as a central hub, with its coordination state potentially influencing OS. Specifically, distinct co-expression patterns between EEF1A1P12 and EEF1A1P4 were associated with significant survival differences. To translate these findings into a predictive framework, we developed a novel pipeline leveraging co-expression biomarkers for survival risk stratification. This approach identified the interaction between RPL7P10 and RPS3AP36 as a robust classifier for B-ALL patient survival. Our results establish pseudogene co-expression as a key molecular feature in B-ALL, with implications for patient stratification and prognostic modeling. This study underscores the importance of integrating pseudogene interactions into leukemia research and network medicine to improve precision oncology approaches.