Meng Zilu, Yang Wenhan, Zhu Lei, Liu Wanyu, Wang Yudong
Department of Maxillofacial Surgery, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China.
Front Pharmacol. 2022 Aug 9;13:963072. doi: 10.3389/fphar.2022.963072. eCollection 2022.
Long non-coding RNAs (lncRNAs) play an essential role in the occurrence and prognosis of tumors, and it has great potential as biomarkers of tumors. However, the roles of Necroptosis-related lncRNA (NRLs) in Head and neck squamous cell carcinoma (HNSCC) remain elusive. We comprehensively analyzed the gene expression and clinical information of 964 HNSCC in four cohorts. LASSO regression was utilized to construct a necroptosis-related lncRNA prognosis signature (NLPS). We used univariate and multivariate regression to assess the independent prognostic value of NLPS. Based on the optimal cut-off, patients were divided into high- and low-risk groups. In addition, the immune profile, multi-omics alteration, and pharmacological landscape of NLPS were further revealed. A total of 21 NRLs associated with survival were identified by univariate regression in four cohorts. We constructed and validated a best prognostic model (NLPS). Compared to the low-risk group, patients in the high group demonstrated a more dismal prognosis. After adjusting for clinical features by multivariate analysis, NLPS still displayed independent prognostic value. Additionally, further analysis found that patients in the low-risk group showed more abundant immune cell infiltration and immunotherapy response. In contrast, patients in the high-risk group were more sensitive to multiple chemotherapeutic agents. As a promising tool, the establishment of NLPS provides guidance and assistance in the clinical management and personalized treatment of HNSCC.
长链非编码RNA（lncRNAs）在肿瘤的发生和预后中起着至关重要的作用，并且作为肿瘤生物标志物具有巨大潜力。然而，坏死性凋亡相关lncRNA（NRLs）在头颈部鳞状细胞癌（HNSCC）中的作用仍不清楚。我们全面分析了四个队列中964例HNSCC的基因表达和临床信息。利用LASSO回归构建了坏死性凋亡相关lncRNA预后特征（NLPS）。我们使用单变量和多变量回归来评估NLPS的独立预后价值。基于最佳临界值，将患者分为高风险组和低风险组。此外，进一步揭示了NLPS的免疫特征、多组学改变和药理学特征。通过四个队列中的单变量回归共鉴定出21个与生存相关的NRLs。我们构建并验证了一个最佳预后模型（NLPS）。与低风险组相比，高风险组患者的预后更差。通过多变量分析调整临床特征后，NLPS仍显示出独立的预后价值。此外，进一步分析发现，低风险组患者表现出更丰富的免疫细胞浸润和免疫治疗反应。相反，高风险组患者对多种化疗药物更敏感。作为一种有前景的工具，NLPS的建立为HNSCC的临床管理和个性化治疗提供了指导和帮助。
