GEO microarray chip information.

Treatment-related myeloid neoplasms (t-MN) represent a severe complication of cancer therapy, characterized by poor prognosis and limited treatment options. This study presents a preliminary, exploratory bioinformatic analysis aimed at characterizing the expression landscape and potential regulatory roles of pyroptosis-related genes (PRGs) in a murine model of t-MN. Utilizing RNA-seq data (GEO: GSE135866), differential expression analysis identified 1286 DEGs. Cross-referencing 367 curated mouse PRGs revealed 46 pyroptosis-related DEGs (PRDEGs). Functional enrichment analysis (GO, KEGG) showed these PRDEGs are significantly involved in autophagy, inflammatory regulation, apoptosis, NOD-like receptor signaling, and the AMPK pathway. GSEA associated the broader gene set with PI3K-Akt and Notch signaling. Protein-protein interaction network analysis identified five critical hub genes: Trp53, Mtor, Gpx3, Foxo3, and Cybb. ROC curve analysis confirmed these hub genes exhibit significant differential expression and high diagnostic accuracy (AUC > 0.9) in distinguishing t-MN from controls. Furthermore, immunoinfiltration analysis (CIBERSORT) revealed significant differences in immune cell composition between t-MN and control samples and identified notable correlations between hub gene expression and specific immune cell abundances. Importantly, given the limited sample size and the use of murine bone marrow data, the statistical findings should be interpreted strictly at the exploratory and hypothesis-generating level. This study does not support definitive biological conclusions or causal inferences but rather aims to delineate the pyroptosis-related molecular profile in a preclinical t-MN model. The results are intended to inform and guide future investigations—including validation in larger cohorts, independent experimental models, and human clinical samples—to assess the translational potential of these candidate biomarkers and therapeutic targets.

治疗相关髓系肿瘤（treatment-related myeloid neoplasms, t-MN）是癌症治疗相关的严重并发症，以预后不良、治疗选择匮乏为特征。本研究开展了一项探索性的初步生物信息学分析，旨在刻画t-MN小鼠模型中细胞焦亡相关基因（pyroptosis-related genes, PRGs）的表达谱及其潜在调控功能。本研究依托RNA测序（RNA-seq）数据（基因表达综合数据库：GSE135866）开展差异表达分析，共筛选得到1286个差异表达基因（differentially expressed genes, DEGs）。通过与367个经整理的小鼠细胞焦亡相关基因交叉比对，最终得到46个细胞焦亡相关差异表达基因（pyroptosis-related DEGs, PRDEGs）。功能富集分析（基因本体论GO、京都基因与基因组百科全书KEGG）结果显示，这些PRDEGs显著富集于自噬、炎症调控、细胞凋亡、NOD样受体信号通路以及AMPK信号通路。基因集富集分析（GSEA）则将整体基因集关联至PI3K-Akt及Notch信号通路。蛋白质相互作用网络分析筛选得到5个关键核心基因：Trp53、Mtor、Gpx3、Foxo3及Cybb。受试者工作特征（ROC）曲线分析证实，这些核心基因在区分t-MN样本与对照样本时呈现显著差异表达，且诊断效能优异（曲线下面积AUC>0.9）。此外，免疫浸润分析（CIBERSORT）显示，t-MN样本与对照样本的免疫细胞组成存在显著差异，同时发现核心基因表达与特定免疫细胞丰度间存在显著相关性。值得注意的是，鉴于本研究样本量有限且仅使用小鼠骨髓数据，所有统计学结果仅可作为探索性及假说生成层面的解读依据。本研究并不支持明确的生物学结论或因果推断，仅旨在刻画临床前t-MN模型中与细胞焦亡相关的分子特征。本研究结果可为未来相关研究提供参考与指引，包括在更大样本队列、独立实验模型及人类临床样本中开展验证工作，以评估这些候选生物标志物与治疗靶点的转化应用潜力。