Identification and validation of pyroptosis patterns in AML via comprehensive bioinformatics analysis
Abstract Pyroptosis, a lytic inflammatory cell death mechanism, plays dual roles in tumorigenesis, but its clinical relevance in acute myeloid leukemia (AML) remains poorly understood. Through an integrative analysis of 40 pyroptosis-related genes in newly diagnosed AML patients (TCGA, n = 151) and...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-04-01
|
| Series: | Discover Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12672-025-02298-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850183910431916032 |
|---|---|
| author | Zeyu Deng Hongkai Zhu Zhao Cheng Ruijuan Li Hongling Peng |
| author_facet | Zeyu Deng Hongkai Zhu Zhao Cheng Ruijuan Li Hongling Peng |
| author_sort | Zeyu Deng |
| collection | DOAJ |
| description | Abstract Pyroptosis, a lytic inflammatory cell death mechanism, plays dual roles in tumorigenesis, but its clinical relevance in acute myeloid leukemia (AML) remains poorly understood. Through an integrative analysis of 40 pyroptosis-related genes in newly diagnosed AML patients (TCGA, n = 151) and healthy controls (GTEx, n = 386), 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 ELANEhigh subtype, associated with favorable survival, and the ELANElow subtype, which was enriched in poor karyotypes and adverse outcomes. This classification was validated in an independent cohort (GSE10358, n = 91). Single-cell RNA sequencing data (GSE116256, n = 15) revealed that the ELANElow subtype is characterized by an immunologically active microenvironment, marked by an expansion of cytotoxic T cells and naive CD4 + /CD8 + T 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 ELANElow subtype were associated with interferon signaling and ubiquitin-mediated degradation pathways. Furthermore, protein–protein 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. |
| format | Article |
| id | doaj-art-5c00ce3eaefd4634adfea6e766938965 |
| institution | OA Journals |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-5c00ce3eaefd4634adfea6e7669389652025-08-20T02:17:10ZengSpringerDiscover Oncology2730-60112025-04-0116111510.1007/s12672-025-02298-5Identification and validation of pyroptosis patterns in AML via comprehensive bioinformatics analysisZeyu Deng0Hongkai Zhu1Zhao Cheng2Ruijuan Li3Hongling Peng4Department of Hematology, The Second Xiangya Hospital of Central South UniversityDepartment of Hematology, The Second Xiangya Hospital of Central South UniversityDepartment of Hematology, The Second Xiangya Hospital of Central South UniversityDepartment of Hematology, The Second Xiangya Hospital of Central South UniversityDepartment of Hematology, The Second Xiangya Hospital of Central South UniversityAbstract Pyroptosis, a lytic inflammatory cell death mechanism, plays dual roles in tumorigenesis, but its clinical relevance in acute myeloid leukemia (AML) remains poorly understood. Through an integrative analysis of 40 pyroptosis-related genes in newly diagnosed AML patients (TCGA, n = 151) and healthy controls (GTEx, n = 386), 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 ELANEhigh subtype, associated with favorable survival, and the ELANElow subtype, which was enriched in poor karyotypes and adverse outcomes. This classification was validated in an independent cohort (GSE10358, n = 91). Single-cell RNA sequencing data (GSE116256, n = 15) revealed that the ELANElow subtype is characterized by an immunologically active microenvironment, marked by an expansion of cytotoxic T cells and naive CD4 + /CD8 + T 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 ELANElow subtype were associated with interferon signaling and ubiquitin-mediated degradation pathways. Furthermore, protein–protein 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.https://doi.org/10.1007/s12672-025-02298-5Acute myeloid leukemiaClassificationPyroptosisImmunePrognosis |
| spellingShingle | Zeyu Deng Hongkai Zhu Zhao Cheng Ruijuan Li Hongling Peng Identification and validation of pyroptosis patterns in AML via comprehensive bioinformatics analysis Discover Oncology Acute myeloid leukemia Classification Pyroptosis Immune Prognosis |
| title | Identification and validation of pyroptosis patterns in AML via comprehensive bioinformatics analysis |
| title_full | Identification and validation of pyroptosis patterns in AML via comprehensive bioinformatics analysis |
| title_fullStr | Identification and validation of pyroptosis patterns in AML via comprehensive bioinformatics analysis |
| title_full_unstemmed | Identification and validation of pyroptosis patterns in AML via comprehensive bioinformatics analysis |
| title_short | Identification and validation of pyroptosis patterns in AML via comprehensive bioinformatics analysis |
| title_sort | identification and validation of pyroptosis patterns in aml via comprehensive bioinformatics analysis |
| topic | Acute myeloid leukemia Classification Pyroptosis Immune Prognosis |
| url | https://doi.org/10.1007/s12672-025-02298-5 |
| work_keys_str_mv | AT zeyudeng identificationandvalidationofpyroptosispatternsinamlviacomprehensivebioinformaticsanalysis AT hongkaizhu identificationandvalidationofpyroptosispatternsinamlviacomprehensivebioinformaticsanalysis AT zhaocheng identificationandvalidationofpyroptosispatternsinamlviacomprehensivebioinformaticsanalysis AT ruijuanli identificationandvalidationofpyroptosispatternsinamlviacomprehensivebioinformaticsanalysis AT honglingpeng identificationandvalidationofpyroptosispatternsinamlviacomprehensivebioinformaticsanalysis |