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New genetic insights into immunotherapy outcomes in gastric cancer via single-cell RNA sequencing and random forest model. | LitMetric

AI Article Synopsis

  • * Researchers used single-cell RNA sequencing to identify key genes and created a random forest model that successfully predicts how well patients might respond to immunotherapy, showing promising results in terms of survival rates.
  • * The analysis revealed that high levels of METTL1 are linked to better survival outcomes and that METTL1 may improve gastric cancer cell activity by affecting immune responses, indicating its potential as a valuable biomarker for treatment optimization.

Article Abstract

Objective: The high mortality rate of gastric cancer, traditionally managed through surgery, underscores the urgent need for advanced therapeutic strategies. Despite advancements in treatment modalities, outcomes remain suboptimal, necessitating the identification of novel biomarkers to predict sensitivity to immunotherapy. This study focuses on utilizing single-cell sequencing for gene identification and developing a random forest model to predict immunotherapy sensitivity in gastric cancer patients.

Methods: Differentially expressed genes were identified using single-cell RNA sequencing (scRNA-seq) and gene set enrichment analysis (GESA). A random forest model was constructed based on these genes, and its effectiveness was validated through prognostic analysis. Further, analyses of immune cell infiltration, immune checkpoints, and the random forest model provided deeper insights.

Results: High METTL1 expression was found to correlate with improved survival rates in gastric cancer patients (P = 0.042), and the random forest model, based on METTL1 and associated prognostic genes, achieved a significant predictive performance (AUC = 0.863). It showed associations with various immune cell types and negative correlations with CTLA4 and PDCD1 immune checkpoints. Experiments in vitro and in vivo demonstrated that METTL1 enhances gastric cancer cell activity by suppressing T cell proliferation and upregulating CTLA4 and PDCD1.

Conclusion: The random forest model, based on scRNA-seq, shows high predictive value for survival and immunotherapy sensitivity in gastric cancer patients. This study underscores the potential of METTL1 as a biomarker in enhancing the efficacy of gastric cancer immunotherapy.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11063021PMC
http://dx.doi.org/10.1007/s00262-024-03684-8DOI Listing

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