TOTAL VIEWS: 123
Objective: To predict Polyphyllin I (PPI) targets, analyze their effects on non-small cell lung cancer (NSCLC) patients, develop a PANoptosis-based prognostic model, and identify potential molecular markers that can be used as PPI targets for the treatment of NSCLC. Methods: We predicted the targets of PPI and analyzed the main effect of PPI targets in NSCLC patients by principal component analysis (PCA). The PANoptosis genes associated with PPI and immune infiltration were explored using the weighted gene co-expression network analysis (WGCNA) algorithm. Then, the least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE) and extreme gradient boosting (XGBoost) algorithms were used to screen the feature genes, and the prognostic model and clinical nomogram were constructed. Finally, the differences in the immune characteristics and mutations of patients with different risk scores were compared, and the expression of the modeled genes at the gene and protein levels was explored using single-cell sequencing and immunohistochemical techniques. Results: We obtained the PPI effective index (PEI) using the NSCLC data of the training and the validation sets. Meanwhile, both datasets identified strong interaction relationships between PPI and PANoptosis and screened out PPI-induced PANoptosis genes (PPGs). Then, the PPG_score model and clinical nomogram were successfully constructed. Finally, we found that PPG_score patients had different immune responses, and independent predictors were highly expressed in lung cancer tissues and fibroblasts. Conclusion: In this study, a PANoptosis prognostic model regulated by PPI has a guiding role for both immunotherapy and mutations, and the screened independent prognostic factors, IRF1, PTGIS, and G0S2, may become potential molecular markers and PPI targets for NSCLC patients.
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Construction of a Novel Polyphyllin I-regulated PANoptosis Prognosis Model by WGCNA and Machine Learning Algorithms in Non-small Cell Lung Cancer
How to cite this paper: Kai Tan, Changhui Zhang, Zuomei He, Puhua Zeng. (2024) Construction of a Novel Polyphyllin I-regulated PANoptosis Prognosis Model by WGCNA and Machine Learning Algorithms in Non-small Cell Lung Cancer. International Journal of Clinical and Experimental Medicine Research, 8(4), 528-546.
DOI: https://dx.doi.org/10.26855/ijcemr.2024.10.003