Development and validation of a phospholipid metabolism-associated lncRNA model for prognostic stratification and therapeutic guidance in HNSCC.

Alterations in phospholipid metabolism are increasingly recognized as critical in cancer progression, yet their specific contributions to head and neck squamous cell carcinoma (HNSCC) remain unclear. This study aimed to establish a prognostic model based on phospholipid metabolism-related long non-coding RNAs (lncRNAs) to predict clinical outcomes in HNSCC patients. Transcriptomic data from The Cancer Genome Atlas (TCGA) were analyzed to identify differentially expressed lncRNAs linked to phospholipid metabolism. Using univariate and multivariate Cox regression analyses, a 10-lncRNA signature (AC104041.1, AC106820.3, AL355488.1, LINC01305, AC133644.1, AC091185.1, AL158166.1, AL512274.1, DBH-AS1, AC025176.1) was developed. The model’s predictive accuracy was confirmed through Kaplan–Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis (DCA), surpassing traditional clinical indicators for overall survival (OS). Gene set enrichment analysis, immune profiling, and drug sensitivity testing revealed distinct biological differences between clusters. Low-risk patients and those in cluster 2 exhibited better prognoses, enhanced immune infiltration, and heightened immune activity, while high-risk patients in cluster 1 showed increased sensitivity to chemotherapeutic agents. Functional assays demonstrated that AL158166.1 knockdown inhibited HNSCC cell proliferation, migration, invasion, and phospholipid metabolism, likely through suppression of the PI3K/AKT/mTOR pathway. These findings suggest that AL158166.1 plays a pivotal role in HNSCC progression. In conclusion, the proposed lncRNA-based prognostic model provides a reliable tool for survival prediction and personalized treatment guidance in HNSCC, highlighting promising therapeutic targets and advancing precision immunotherapy and chemotherapy strategies.