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Updated: Jun 23, 2025
A Model for Perineural Invasion in Head and Neck Squamous Cell Carcinoma
Published on: January 5, 2017
Integrative machine learning and neural networks for identifying PANoptosis-related lncRNA molecular subtypes and
Zhenzhen Wang1, Lixin Cheng1, Juntao Huang2
1Department of Otolaryngology Head and Neck Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang, China.
Purpose:
PANoptosis is considered a novel type of cell death that plays important roles in tumor progression. In this study, we applied machine learning algorithms to explore the relationships between PANoptosis-related lncRNAs (PRLs) and head and neck squamous cell carcinoma (HNSCC) and established a neural network model for prognostic prediction.
Methods:
Information about the HNSCC cohort was downloaded from the TCGA database, and the differentially expressed prognostic PRLs between tumor and normal samples were assessed in patients with different tumor subtypes via nonnegative matrix factorization (NMF) analysis. Subsequently, five kinds of machine-learning algorithms were used to select the core PRLs across the subtypes, and the interactive features were pooled into a neural network model to establish a PRL-related risk score (PLRS) system. Survival differences were compared via Kaplan‒Meier analysis, and the predictive effects were assessed with the areas under the ROCs. Moreover, functional enrichment analysis, immune infiltration, tumor mutation burden (TMB) and clinical therapeutic response were also conducted to further evaluate the novel predictive model.
Results:
A total of 347 PRLs were identified, 225 of which were differentially expressed between tumor and normal samples. Patients were divided into two clusters via NMF analysis, in which cluster 1 had a better prognosis and more immune cells and functional infiltrates. With the application of five machine learning algorithms, we selected 13 interactive PRLs to construct the predictive model. The AUCs for the ROCs in the entire set were 0.735, 0.740 and 0.723, respectively. Patients in the low-PLRS group exhibited a better prognosis, greater immune cell enrichment, greater immune function activation, lower TMB and greater sensitivity to immunotherapy.
Conclusion:
In this study, we established a novel neural network prognostic model to predict survival and identify tumor subtypes in HNSCC patients. This novel assessment system is useful for prediction, providing ideas for clinical treatment.
