Prognostic and immunotherapeutic efficacy predictive model for gastric cancer based on ferroptosis- and autophagy-related genes
Chuanyu Leng, Yuanyuan Fang, Wensheng Qiu, Xin Li, Xueying Sun, Yangyang Lu, Jing Lv, Weiwei QiABSTRACT
Purpose:
The clinical management of gastric cancer is challenged by profound tumor heterogeneity and the emergence of drug-resistant cell populations. Ferroptosis, a regulated form of iron-dependent cell death driven by lipid peroxidation, has exhibited limited clinical efficacy as a monotherapy. Emerging evidence shows that selective autophagy pathways can promote ferroptosis.
Methods:
In this study, integrated analysis of ferroptosis-associated autophagy genes (FARGs) was conducted to explore their application potential in improving therapeutic outcomes. By leveraging transcriptomic and clinical data from The Cancer Genome Atlas and Gene Expression Omnibus databases, a prognostic model was developed using univariate Cox regression followed by Least Absolute Shrinkage and Selection Operator (LASSO) regression, which identified 11 FARGs exhibiting robust prognostic performance across multiple cohorts.
Results:
Functional enrichment analysis revealed distinct pathway activations: extracellular matrix remodeling was enriched in high-risk patients, whereas interferon-mediated immune responses were heightened in low-risk patients. Further tumor microenvironment characterization revealed that high-risk patients displayed an immunosuppressive phenotype marked by M2 macrophage infiltration, upregulation of immune checkpoint molecules, and elevated stromal activity. By contrast, low-risk patients were associated with a higher prevalence of microsatellite instability-high status and increased tumor mutational burden, which are biomarkers predictive of favorable responses to immune checkpoint inhibitors. Investigation into autophagy–ferroptosis crosstalk highlighted the central role of lipid metabolism–related pathways. Aldehyde dehydrogenase 3A2 was also identified as a key biomarker, and functional validation confirmed its involvement in promoting cancer cell proliferation and migration.
Conclusion:
Collectively, the FARG-based model developed here shows great application potential in stratifying patient responses to immunotherapy and developing personalized treatment strategies for gastric cancer.