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Related Experiment Video

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Identification and Validation of a Prognostic Risk-Scoring Model Based on LATS2 Expression in Acute Myeloid Leukemia.

Bin Liu1, Jian Zhang2, Jing Wang2

  • 1Department of Center Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China.

Cancer Investigation
|December 26, 2025
PubMed
Summary

A new seven-gene prognostic model shows excellent potential for predicting outcomes in acute myeloid leukemia (AML) patients, improving upon current treatment limitations. This risk-scoring tool aids in evaluating overall survival (OS) for better AML management.

Keywords:
Acute myeloid leukemia (AML)LASSO regressionLarge tumor suppressor kinase 2 (LATS2)PrognosisThe cancer genome atlas program (TCGA)

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Area of Science:

  • Oncology
  • Genomics
  • Biostatistics

Background:

  • Treatment and remission rates for acute myeloid leukemia (AML) remain a significant clinical challenge.
  • Existing prognostic models for AML may not fully capture the complexity of patient outcomes.

Purpose of the Study:

  • To develop and validate a novel prognostic risk-scoring model for acute myeloid leukemia (AML).
  • To identify key gene signatures that predict overall survival (OS) in AML patients.

Main Methods:

  • Utilized LASSO-Cox regression analysis to identify seven signature genes (POU3F1, RPGR, PTP4A3, SOCS1, FAM83G, GREB1, COL2A1).
  • Developed a risk-scoring model based on these genes.
  • Validated the model's predictive efficacy using training and two external GEO datasets (GSE71014, GSE6891).

Main Results:

  • The seven-gene risk-scoring model demonstrated strong predictive performance in the training set (AUCs ranging from 0.876 to 0.976 for 1, 3, and 5 years).
  • The model also showed excellent validation in external datasets (AUCs ranging from 0.822 to 0.891 for 1, 3, and 5 years).
  • The model effectively evaluated the overall survival (OS) of AML patients.

Conclusions:

  • A seven-gene signature risk-scoring model offers a robust tool for predicting AML patient outcomes.
  • This model shows excellent performance in assessing overall survival (OS), potentially guiding clinical decision-making.
  • Further research can explore integrating this model into clinical practice for personalized AML treatment strategies.