Scholarlink

<p>Current biomarkers and prognostic systems lack sufficient evaluation of their predictive power on HMA efficacy for myeloid neoplasms.&#xa0;Weighted Gene Co-expression Network Analysis (WGCNA) was employed to uncover the target module correlated with HMA response. Based on this module, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to establish a predictive model with non-zero coefficients, termed the HMA-29 model.&#xa0;The magenta module was identified as the target module correlating with HMA response (R² = 0.37, <i>p</i> = 2 × 10⁻⁴). LASSO regression established the HMA-29 model, comprising the expression signatures of 29 genes, which stratified patients into responsive-like and resistant-like groups. HMA-29 risk scores significantly differed between HMA responders and non-responders and demonstrated superior predictive performance (AUC 0.9982, <i>p</i> &lt; 0.0001). The predictive capability was validated across independent cohorts/disease subtypes/HMA drugs/sampling conditions. Notably, HMA-29 risk scores did not significantly differ between pre- and post-treatment samples. Moreover, Kaplan-Meier analysis demonstrated significant differences of disease-free survival (DFS) and overall survival (OS) between the HMA-29-defined groups. GSEA indicated that the G2M checkpoint, MYC targets, and E2F targets were positively associated with HMA-29 risk.&#xa0;We developed a novel predictive model for HMA response in myeloid neoplasms, demonstrating superior predictive and prognostic value compared to existing systems. Our findings provide insights into potential therapeutic strategies, including the integration of targeted therapies with HMA treatment.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Predicting response to hypomethylating agents in myeloid neoplasms: a robust model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月24日

查看全文
  • Chao Guo,
  • Ya-yue Gao,
  • Qian-qian Ju,
  • Zhen-ling Li

摘要

Current biomarkers and prognostic systems lack sufficient evaluation of their predictive power on HMA efficacy for myeloid neoplasms. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to uncover the target module correlated with HMA response. Based on this module, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to establish a predictive model with non-zero coefficients, termed the HMA-29 model. The magenta module was identified as the target module correlating with HMA response (R² = 0.37, p = 2 × 10⁻⁴). LASSO regression established the HMA-29 model, comprising the expression signatures of 29 genes, which stratified patients into responsive-like and resistant-like groups. HMA-29 risk scores significantly differed between HMA responders and non-responders and demonstrated superior predictive performance (AUC 0.9982, p < 0.0001). The predictive capability was validated across independent cohorts/disease subtypes/HMA drugs/sampling conditions. Notably, HMA-29 risk scores did not significantly differ between pre- and post-treatment samples. Moreover, Kaplan-Meier analysis demonstrated significant differences of disease-free survival (DFS) and overall survival (OS) between the HMA-29-defined groups. GSEA indicated that the G2M checkpoint, MYC targets, and E2F targets were positively associated with HMA-29 risk. We developed a novel predictive model for HMA response in myeloid neoplasms, demonstrating superior predictive and prognostic value compared to existing systems. Our findings provide insights into potential therapeutic strategies, including the integration of targeted therapies with HMA treatment.