Scholarlink

<p>Osteosarcoma, an aggressive bone malignancy predominantly affecting children and adolescents, is characterized by a poor prognosis and high mortality rates. The development of reliable prognostic tools is critical for advancing personalized treatment strategies. However, identifying robust gene signatures to predict osteosarcoma outcomes remains a significant challenge. In this study, we analyzed gene expression data from 138 osteosarcoma samples across two multicenter cohorts and identified 14 consensus prognosis-associated genes via univariate Cox regression analysis. Using 66 combinations of 10 machine learning (ML) algorithms, we developed a machine learning-derived prognostic signature (MLDPS) optimized by the average C-index across TARGET, GSE21257, and merged cohorts. The MLDPS effectively stratified osteosarcoma patients into high- and low-risk score groups, achieving strong predictive performance for 1-, 3-, and 5-year overall survival (AUC range: 0.852 — 0.963). The MLDPS, comprising seven genes (CTNNBIP1, CORT, DLX2, TERT, BBS4, SLC7A1, NKX2-3), exhibited superior predictive accuracy compared to 10 established gene signatures. The findings of the MLDPS carry significant clinical implications for osteosarcoma treatment. Patients with a high-risk score demonstrated worse prognosis, increased metastasis risk, reduced immune infiltrations, and greater sensitivity to immunotherapy. Conversely, low-risk patients exhibited prolonged survival and distinct drug sensitivities. These findings underscore the potential of MLDPS to guide risk stratification, inform personalized therapeutic strategies, and improve clinical management in osteosarcoma.</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

Feasibility of machine learning–based modeling and prediction to assess osteosarcoma outcomes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月19日

查看全文
  • Qinfei Zhao,
  • Weiquan Hu,
  • Yu Xia,
  • Shengyun Dai,
  • Xiangsheng Wu,
  • Jing Chen,
  • Xiaoying Yuan,
  • Tianyu Zhong,
  • Xuxiang Xi,
  • Qi Wang

摘要

Osteosarcoma, an aggressive bone malignancy predominantly affecting children and adolescents, is characterized by a poor prognosis and high mortality rates. The development of reliable prognostic tools is critical for advancing personalized treatment strategies. However, identifying robust gene signatures to predict osteosarcoma outcomes remains a significant challenge. In this study, we analyzed gene expression data from 138 osteosarcoma samples across two multicenter cohorts and identified 14 consensus prognosis-associated genes via univariate Cox regression analysis. Using 66 combinations of 10 machine learning (ML) algorithms, we developed a machine learning-derived prognostic signature (MLDPS) optimized by the average C-index across TARGET, GSE21257, and merged cohorts. The MLDPS effectively stratified osteosarcoma patients into high- and low-risk score groups, achieving strong predictive performance for 1-, 3-, and 5-year overall survival (AUC range: 0.852 — 0.963). The MLDPS, comprising seven genes (CTNNBIP1, CORT, DLX2, TERT, BBS4, SLC7A1, NKX2-3), exhibited superior predictive accuracy compared to 10 established gene signatures. The findings of the MLDPS carry significant clinical implications for osteosarcoma treatment. Patients with a high-risk score demonstrated worse prognosis, increased metastasis risk, reduced immune infiltrations, and greater sensitivity to immunotherapy. Conversely, low-risk patients exhibited prolonged survival and distinct drug sensitivities. These findings underscore the potential of MLDPS to guide risk stratification, inform personalized therapeutic strategies, and improve clinical management in osteosarcoma.