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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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Prediction of Patients With High-Risk Osteosarcoma on the Basis of XGBoost Algorithm Using Transcriptome and

Weisong Zhao1,2, Huanliang Meng1,2, Zhenwu Dai3,4,5

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Summary

A new classifier accurately predicts high-risk osteosarcoma (OS) using gene expression and methylation data. This tool aids clinicians in identifying patients with the poorest prognosis for improved treatment decisions.

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Osteosarcoma (OS) is a primary bone cancer with high metastasis and mortality rates.
  • Previous multiomics analysis identified four OS subtypes, including a high-risk, MYC-driven subtype with poor prognosis.
  • A diagnostic tool is needed to identify high-risk OS pre-emptively.

Purpose of the Study:

  • To develop a classifier predicting the high-risk OS subtype.
  • Utilize transcriptome and methylation data for accurate prediction.
  • Aid clinicians in early identification of high-risk patients.

Main Methods:

  • Developed a classification model using eXtreme Gradient Boosting (XGBoost) with Bayesian optimization.
  • Integrated transcriptome and methylation data from the Shanghai General Hospital OS (SGH-OS) cohort.
  • Validated the model's performance on the external TARGET-OS cohort.

Main Results:

  • An XGBoost classifier incorporating nine genes (ARHGAP9, CADM1, CPE, DUSP3, FGFR1, GALNT3, IGF2BP3, KIF26A, ZFP3) was developed.
  • The classifier achieved excellent performance: AUC of 0.999 and accuracy of 0.989 in the internal cohort.
  • Successfully stratified survival outcomes in the external cohort; IGF2BP3 correlated with MYC signaling, suggesting a therapeutic target.

Conclusions:

  • The developed classifier shows excellent predictive performance for high-risk OS.
  • This tool can enhance clinical decision-making and patient management strategies.
  • Identifies IGF2BP3 as a potential therapeutic target in high-risk OS.