Machine learning for identifying tumor stemness genes and developing prognostic model in gastric cancer

Area of Science:

• Oncology
• Molecular Biology
• Bioinformatics

Background:

• Gastric cancer poses significant challenges due to treatment resistance and recurrence.
• Tumor stem cells are implicated in gastric cancer progression and therapeutic failure.
• Identifying genes related to tumor stemness is crucial for improving patient outcomes.

Purpose of the Study:

• To identify key genes associated with tumor stemness in gastric cancer.
• To develop a prognostic model for gastric cancer patients based on tumor stemness genes.
• To investigate the role of identified genes in gastric cancer malignancy and immune infiltration.

Main Methods:

• Utilized ssGSEA, WGCNA, and machine learning to identify tumor stemness key genes (TSKGs).
• Constructed the Tumor Stemness Risk Genes Prognostic Model (TSRGPM) and assessed its prognostic value using PCA, Cox regression, and ROC analysis.
• Analyzed immune infiltration patterns with CIBERSORT and validated findings through in vitro experiments.

Main Results:

• Developed the TSRGPM, demonstrating its efficacy in risk stratification and as an independent prognostic indicator for gastric cancer.
• Found a significant correlation between Tumor Stemness Risk Scores (TSRS) and lymph node metastasis.
• Identified CDC25A as a key gene promoting malignant phenotype, cell proliferation, and DNA replication, validated by in vitro studies.

Conclusions:

• The TSRGPM provides a promising tool for gastric cancer prognostication.
• Identified CDC25A as a potential therapeutic target for gastric cancer.
• The study advances personalized management strategies for gastric cancer by elucidating tumor stemness mechanisms.