Development and validation of a prognostic prediction model based on coagulation-related genes and clinical factors in acute leukemia.
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BACKGROUND: Acute leukemia (AL) is one of the most prevalent pediatric malignancies with highly heterogeneous clinical outcomes. Coagulation-related genes (CRGs) play a crucial role in tumours, but their value in combination with clinical factors for prognostic prediction in AL is unclear. This study aims to develop a prognostic model based on the CRGs signature, with the goal of improving prognostic monitoring and identifying potential therapeutic targets for pediatric AL. METHODS: We collected transcriptomic and clinical data of pediatric AL patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and The Cancer Genome Atlas (TCGA) databases, including age, gender, and white blood cell count (WBC). Molecular subtypes related to CRGs were identified via non-negative matrix factorization (NMF). A CRGs-based gene signature was developed using the least absolute shrinkage and selection operator (LASSO) and regression analyses. The model was built on a training set and validated independently. Time-dependent receiver operating characteristic (ROC) was used to assess the predictive accuracy of the model for 1-, 3-, and 5-year overall survival (OS). Nomograms were constructed combining CRGs characteristics and clinical factors, and their clinical utility was assessed using calibration curves and decision curve analysis (DCA). Immune infiltration was quantified using the single-sample gene set enrichment analysis (ssGSEA) and the microenvironment cell populations-counter (MCPcounter) algorithm. Kaplan-Meier (K-M) survival analysis was performed to assess the correlation between signature gene expression and OS. Moreover, molecular docking was utilized to investigate the potential interactions between signature genes and small-molecule drugs. Expression of key genes was confirmed by quantitative reverse transcription polymerase chain reaction (qRT-PCR). RESULTS: A total of 103 AL patients were included as a training set. Risk stratification based on the median risk score of CRGs showed a significant difference in OS between the two groups (P<0.001), with the low-risk group having a better prognosis. The area under the curves (AUCs) of the model for 1-, 3-, and 5-year survival prediction in the training set were 0.711, 0.762, and 0.718, respectively, and the AUC values in the independent validation set also showed good agreement. Analysis integrating risk scores with clinical data indicated that the CRGs signature could serve as an independent prognostic factor. The nomogram constructed based on CRGs features and key clinical variables showed good fit and potential clinical net effect. Molecular docking analysis revealed stable binding interactions between PROS1 and the small-molecule drugs, avatrombopag and lusutrombopag. CONCLUSIONS: In this study, a robust prognostic model incorporating CRGs was constructed to effectively predict survival outcomes in paediatric AL patients. The model helps to enable individualised risk stratification and guide targeted therapy. In addition, avatrombopag and lusutrombopag as potential therapeutic agents provide new ideas for precision medicine in paediatric AL.