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A Novel Six-Gene-Based Prognostic Model Predicts Survival and Clinical Risk Score for Gastric Cancer.

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This study identifies six autophagy-related genes (ARGs) as key biomarkers for predicting overall survival in gastric cancer (GC). The developed risk-score model effectively distinguishes high-risk from low-risk patients, aiding in personalized treatment strategies.

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autophagy-related genesbiomarkersgastric canceroverall survival raterisk-score model

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Area of Science:

  • Oncology
  • Molecular Biology
  • Bioinformatics

Background:

  • Autophagy is crucial in cancer development and treatment resistance.
  • Autophagy-related genes (ARGs) roles in gastric cancer (GC) remain underexplored.
  • Identifying novel biomarkers for GC prognosis is essential.

Purpose of the Study:

  • To conduct a bioinformatic analysis of ARGs in GC.
  • To identify novel biomarkers for predicting overall survival (OS) in GC patients.
  • To develop a prognostic model for GC.

Main Methods:

  • Utilized TCGA and GEO datasets for gene expression and clinical data.
  • Performed Lasso, univariate, and multivariate Cox regression analyses to identify OS-related ARGs.
  • Developed a six-ARG risk-score model and assessed its prognostic performance using Kaplan-Meier and ROC curve analyses.
  • Analyzed immune status and functional enrichment (GO, KEGG) of model-related genes.

Main Results:

  • Identified six ARGs (DYNLL1, PGK2, HPR, PLOD2, PHYHIP, CXCR4) as prognostic indicators.
  • A high-risk score was significantly associated with lower OS in GC patients (p < 0.05).
  • The risk-score model demonstrated robust prognostic performance and independence from most clinical traits.
  • Model-related genes were linked to immune suppression and pathways like PI3K-Akt and MAPK signaling.

Conclusions:

  • The identified six-ARGs serve as potential prognostic biomarkers for GC.
  • The developed risk-score model can aid in predicting patient outcomes.
  • This research supports the development of personalized treatment strategies for GC patients.