Computational biology and chemistry · 2026
Unveiling a novel disulfide-mediated prognostic classification and immune landscape in lung adenocarcinoma: Implications for therapeutic interventions.
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Zhang Ye, Li Chunling
Аннотация
BACKGROUND: This study investigated the influence of disulfidptosis-related lung cancer (DRLC) genes on lung adenocarcinoma (LUAD) development, along with their associated mechanisms, immune infiltration, and potential drug combinations for disulfidptosis-based strategies.
METHODS: We identified key genes affected by glucose starvation and analyzed their correlation with LUAD patient survival using TCGA data. Enrichment analysis and protein-protein interaction networks were used for DRLC genes. We explored downstream disulfide proteins and developed Cox regression models for survival prediction. Tumor immune infiltration associated with DRLC genes was also assessed. Potential drugs were identified using GDSC and CTRP databases.
RESULTS: We identified 28 key DRLC genes, enriched GO and Reactome terms, with CCT6A occupying a relatively central position in a low-confidence PPI network. We found 79 potential downstream disulfide proteins and established Cox hazard models for survival prediction. Notably, a 27-factor model predicted 1-to-5-year survival in high-SLC7A11 cases, with apparent AUCs above 0.8 at 3 and 4 years. Additionally, we computationally predicted candidate drugs, including TGX-221, dabrafenib and panobinostat, for future testing in combination with DRLC gene and downstream disulfide protein targeting.
CONCLUSION: DRLC genes play vital roles in LUAD development, progression, immune microenvironment, and patient survival. We developed Cox regression models based on 28 key DRLC genes for survival prediction, which were internally consistent, pending external validation. Furthermore, potential drugs were suggested to be effective in LUAD when used in conjunction with disulfidptosis strategies.
METHODS: We identified key genes affected by glucose starvation and analyzed their correlation with LUAD patient survival using TCGA data. Enrichment analysis and protein-protein interaction networks were used for DRLC genes. We explored downstream disulfide proteins and developed Cox regression models for survival prediction. Tumor immune infiltration associated with DRLC genes was also assessed. Potential drugs were identified using GDSC and CTRP databases.
RESULTS: We identified 28 key DRLC genes, enriched GO and Reactome terms, with CCT6A occupying a relatively central position in a low-confidence PPI network. We found 79 potential downstream disulfide proteins and established Cox hazard models for survival prediction. Notably, a 27-factor model predicted 1-to-5-year survival in high-SLC7A11 cases, with apparent AUCs above 0.8 at 3 and 4 years. Additionally, we computationally predicted candidate drugs, including TGX-221, dabrafenib and panobinostat, for future testing in combination with DRLC gene and downstream disulfide protein targeting.
CONCLUSION: DRLC genes play vital roles in LUAD development, progression, immune microenvironment, and patient survival. We developed Cox regression models based on 28 key DRLC genes for survival prediction, which were internally consistent, pending external validation. Furthermore, potential drugs were suggested to be effective in LUAD when used in conjunction with disulfidptosis strategies.