ArticleDiscover oncology2025
Machine learning-based characterization of a PANoptosis-associated model for enhancing prognosis and immunotherapy response in lung adenocarcinoma patients.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundsPANoptosis is a new form of inflammatory programmed cell death, that emphasizes the interaction between pyroptosis, apoptosis and necroptosis. This study aimed to investigate clinical implications of PANoptosis in lung adenocarcinoma.
methodsThe ConsensusClusterPlus software was firstly utilized to identify molecular subtypes in lung adenocarcinoma (LUAD) based upon expression of PANoptosis-related regulators. Then, subtype-associated modules were further screened by using the weight gene correlation network analysis (WGCNA). A PANoptosis-related signature (PRS) was developed using a 10-fold cross-validation framework and 101 combinations of 10 machine-learning algorithms based on module genes. The predictive value of PRS was thoroughly assessed in relation to prognosis and immunotherapy. In addition, chemotherapy drugs sensitivity and candidate drug targets were further screened through cell line analysis.
resultsTwo molecular subtypes related to PANoptosis were distinguished by analyzing the expression of PANoptosis regulators. A total of 789 genes associated with subtype were identified in the yellow module using the WGCNA algorithm. Based on subtype-related genes, the optimal PRS was constructed using the ridge algorithm out of 101 algorithm combinations, and displayed a robust and reliable performance in predicting the survival of LUAD patients across multiple cohorts. Multivariate cox regression analysis result demonstrated that PRS can be serve as an independent prognostic factor. A nomogram, constructed by PRS and independent clinical factors demonstrated outstanding predictive accuracy for overall survival (OS) of LUAD patients. Additionally, the patients in low PRS group exhibited a favorable survival outcome, increased immune cell infiltration, and lower tumor immune dysfunction and exclusion (TIDE) score. Conversely, the high PANoptosis group showed a correlation with higher rates of somatic single nucleotide polymorphisms (SNP) mutation and copy number variation (CNV). The individuals with a high PANoptosis score displayed higher sensitivity to docetaxel and gemcitabine. Ultimately, seven drugs (SB-743921, GSK461364, BI-2536, deferasirox, VLX600, VE-822, epothilone-b) and a therapeutic target (TRPA1) were predicted to the high PANoptosis group patients.
conclusionsThe present study developed a PRS using 101 machine learning combination algorithms, which could aid in risk stratification and prognosis for LUAD patients. The candidate drugs and target may provide new insights in the treatment of high PRS group patients.
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