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Through an integrative analysis of 40 pyroptosis-related genes in newly diagnosed AML patients (TCGA, n\u2009=\u2009151) and healthy controls (GTEx, n\u2009=\u2009386), we identified 32 genes with aberrant expression. Among these, 9 genes were found to be significant prognostic markers, including ELANE (protective), and CASP1, CHMP4B, BAK1, and CHMP2A (risk), which retained their prognostic significance after adjusting for age and gender. Using unsupervised nonnegative matrix factorization (NMF) on TCGA data, we classified AML into two pyroptosis patterns: the ELANE<jats:sup>high<\/jats:sup> subtype, associated with favorable survival, and the ELANE<jats:sup>low<\/jats:sup> subtype, which was enriched in poor karyotypes and adverse outcomes. This classification was validated in an independent cohort (GSE10358, n\u2009=\u200991). Single-cell RNA sequencing data (GSE116256, n\u2009=\u200915) revealed that the ELANE<jats:sup>low<\/jats:sup> subtype is characterized by an immunologically active microenvironment, marked by an expansion of cytotoxic T cells and naive CD4\u2009+\u2009\/CD8\u2009+\u2009T cells. Factor analysis revealed associations between pyroptosis patterns and other forms of cell death, including ferroptosis, autophagy, and apoptosis, as well as with karyotype, leukemia stemness, and TP53\/FLT3-ITD mutations. Prognostic immune gene sets enriched in the ELANE<jats:sup>low<\/jats:sup> subtype were associated with interferon signaling and ubiquitin-mediated degradation pathways. Furthermore, protein\u2013protein interaction (PPI) network analysis identified three sub-networks and nine key hub genes. This study integrates gene expression data from newly diagnosed AML patients, revealing the heterogeneity of pyroptosis patterns within the population. It highlights the potential links between distinct pyroptosis patterns, the immune microenvironment, various cell death pathways, leukemia stemness, and genomic alterations, offering novel biomarkers and therapeutic targets for risk stratification and immunomodulatory interventions in AML.<\/jats:p>","DOI":"10.1007\/s12672-025-02298-5","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T12:47:24Z","timestamp":1744289244000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Identification and validation of pyroptosis patterns in AML via comprehensive bioinformatics analysis"],"prefix":"10.1007","volume":"16","author":[{"given":"Zeyu","family":"Deng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongkai","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruijuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongling","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,10]]},"reference":[{"issue":"9","key":"2298_CR1","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1182\/blood.2019001982","volume":"136","author":"S DeWolf","year":"2020","unstructured":"DeWolf S, Tallman MS. 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