ArticleBMC cancer2025
Machine learning-based bulk RNA analysis reveals a prognostic signature of 13 cell death patterns and potential therapeutic target of SMAD3 in acute myeloid leukemia.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial intelligence-based prognostic models in acute myeloid leukemia: systematic review and meta-analysis.Blood neoplasia · 2026Article
- Computer-aided drug design in acute myeloid leukemia: a comprehensive review of advances, challenges, and future prospect.Journal of computer-aided molecular design · 2026Review
- Development and validation of a leukemia prognostic model through single-cell RNA sequencing and machine learning approaches.Discover oncology · 2025Article
- SLC39A14 Is a Potential Therapy Target and Prognostic Biomarker for Acute Myeloid Leukemia.Genes · 2025Article
- Loss of RNF41 promotes bladder cancer metastasis through increasing NUDC stability to enhance tubulin polymerization.Cell death & disease · 2025Article
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11 authors.
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Abstract
backgroundDysregulation or abnormality of the programmed cell death (PCD) pathway is closely related to the occurrence and development of many tumors, including acute myeloid leukemia (AML). Studying the abnormal characteristics of PCD pathway-related molecular markers can provide a basis for prognosis prediction and targeted drug design in AML patients.
methodsA total of 1394 genes representing 13 different PCD pathways were examined in AML patients and healthy donors. The upregulated genes were analyzed for their ability to predict overall survival (OS) individually, and these prognostic genes were subsequently combined to construct a PCD-related prognostic signature via an integrated approach consisting of 101 models based on ten machine learning algorithms. RNA transcriptome and clinical data from multiple AML cohorts (TCGA-AML, GSE106291, GSE146173 and Beat AML) were obtained to develop and validate the AML prognostic model.
resultsA total of 214 upregulated PCD-related genes were identified in AML patients, 39 of which were proven to be prognostic genes in the training cohort. On the basis of the average C-index and number of model genes identified from the machine learning combinations, a PCD index was developed and validated for predicting AML OS. A prognostic nomogram was then generated and validated on the basis of the PCD index, age and ELN risk stratification in the Beat AML cohort and the GSE146173 cohort, revealing satisfactory predictive power (AUC values ≥ 0.7). With different mutation patterns, a higher PCD index was associated with a worse OS. The PCD index was significantly related to higher scores for immunosuppressive cells and mature leukemia cell subtypes. As the gene most closely related to the PCD index, the expression of SMAD3 was further validated in vitro. AML cells harboring KMT2A rearrangements were more sensitive to the SMAD3 inhibitor SIS3, and the expression of the autophagy-related molecular marker LC3 was increased in KMT2A-rearranged cell lines after SIS3 monotherapy and combined treatment.
conclusionThe PCD index and SMAD3 gene expression levels have potential prognostic value and can be used in targeted therapy for AML, and these findings can lead to the development of effective strategies for the combined treatment of high-risk AML patients.
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