Han Yiyuan, Cao Xiaolin, Wang Xuemei, He Qing
The Fourth Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.
Department of Otorhinolaryngology and Head Neck Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Front Genet. 2022 Jan 3;12:721199. doi: 10.3389/fgene.2021.721199. eCollection 2021.
Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancer worldwide and seriously threats public health safety. Despite the improvement of diagnostic and treatment methods, the overall survival for advanced patients has not improved yet. This study aimed to sort out prognosis-related molecular biomarkers for HNSCC and establish a prognostic model to stratify the risk hazards and predicate the prognosis for these patients, providing a theoretical basis for the formulation of individual treatment plans. We firstly identified differentially expressed genes (DEGs) between HNSCC tissues and normal tissues joint analysis based on GEO databases. Then a total of 11 hub genes were selected for single-gene prognostic analysis to identify the prognostic genes. Later, the clinical information and transcription information of HNSCC were downloaded from the TCGA database. With the application of least absolute shrinkage and selection operator (LASSO) algorithm analyses for the prognostic genes on the TCGA cohort, a prognostic model consisting of three genes (COL4A1, PLAU and ITGA5) was successfully established and the survival analyses showed that the prognostic model possessed a robust performance in the overall survival prediction. Afterward, the univariate and multivariate regression analysis indicated that the prognostic model could be an independent prognostic factor. Finally, the predicative efficiency of this model was well confirmed in an independent external HNSCC cohort.
头颈部鳞状细胞癌（HNSCC）是全球最常见的癌症之一，严重威胁公众健康安全。尽管诊断和治疗方法有所改进，但晚期患者的总生存率尚未提高。本研究旨在梳理HNSCC的预后相关分子生物标志物，建立预后模型以分层风险危害并预测这些患者的预后，为制定个体化治疗方案提供理论依据。我们首先基于GEO数据库进行联合分析，鉴定HNSCC组织与正常组织之间的差异表达基因（DEG）。然后选择总共11个枢纽基因进行单基因预后分析以鉴定预后基因。随后，从TCGA数据库下载HNSCC的临床信息和转录信息。通过对TCGA队列中的预后基因应用最小绝对收缩和选择算子（LASSO）算法分析，成功建立了一个由三个基因（COL4A1、PLAU和ITGA5）组成的预后模型，生存分析表明该预后模型在总生存预测中具有强大的性能。之后，单变量和多变量回归分析表明该预后模型可能是一个独立的预后因素。最后，该模型的预测效率在一个独立的外部HNSCC队列中得到了很好的证实。
