Hou Yueting, Pang Haifeng, Xu Xuemei, Zhao Dong
Otolaryngology Head and Neck Surgery, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Curr Med Chem. 2024 May 15. doi: 10.2174/0109298673306245240514064119.
The present study aimed todevelop a prognostic model for HNSCC treatment on the basis of angiogenesis-related signatures.
Head and Neck Squamous Cell Carcinoma (HNSCC) is the most frequent malignancy with poor prognostic outcomes in the head and neck. Angiogenesis plays a critical role in tumorigenesis and is expected to be an effective therapeutic target.
The RNA-seq dataset TCGA-HNSCC and the hallmark gene set were used for angiogenesis-related RiskScore model construction.
The RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA), and the hallmark gene set was used to measure the angiogenesis score using the GSVA R package. Then, the optimal cutoff point for prognostic classification was calculated by the survminer package, and Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify angiogenesis gene modules . Multi/univariable and Lasso Cox analyses were performed to develop the RiskScore model, and the classifier efficiency was evaluated by the Receiver Operating Characteristic curve (ROC). Furthermore, a nomogram was designed for survival probability prediction, and the immune infiltration and immunotherapy differences among different risk patients were assessed.
After calculating the angiogenesis score, we found that this indicator and patients' prognosis were closely correlated, especially when patients with a high angiogenesis score had a poor prognosis. Then, WGCNA identified a blue gene module positively correlated with angiogenesis. Multivariate and Lasso Cox analysis further identified 9 risk model genes for developing a RiskScore, which was used to divide low- and high- -risk groups of patients. Those with a high risk tended to show poor prognosis, immune infiltration, and higher immune escape. Finally, a nomogram was developed to optimize the risk model, and it exhibited excellent short- and long-term survival prediction performance.
We constructed a reliable RiskScore model for the prognostic prediction of HNSCC patients, contributing to precise therapeutic intervention of the cancer.
本研究旨在基于血管生成相关特征建立头颈部鳞状细胞癌（HNSCC）治疗的预后模型。
头颈部鳞状细胞癌（HNSCC）是头颈部最常见的恶性肿瘤，预后较差。血管生成在肿瘤发生中起关键作用，有望成为有效的治疗靶点。
利用RNA测序数据集TCGA-HNSCC和标志性基因集构建血管生成相关风险评分模型。
从癌症基因组图谱（TCGA）下载RNA测序数据，并使用GSVA R包利用标志性基因集测量血管生成评分。然后，通过survminer包计算预后分类的最佳截断点，并使用加权基因共表达网络分析（WGCNA）识别血管生成基因模块。进行多变量/单变量和套索Cox分析以建立风险评分模型，并通过受试者工作特征曲线（ROC）评估分类器效率。此外，设计了列线图用于生存概率预测，并评估不同风险患者之间的免疫浸润和免疫治疗差异。
计算血管生成评分后，我们发现该指标与患者预后密切相关，尤其是血管生成评分高的患者预后较差。然后，WGCNA识别出一个与血管生成呈正相关的蓝色基因模块。多变量和套索Cox分析进一步确定了9个用于建立风险评分的风险模型基因，该评分用于将患者分为低风险和高风险组。高风险患者往往预后较差、免疫浸润和免疫逃逸更高。最后，开发了列线图以优化风险模型，其显示出优异的短期和长期生存预测性能。
我们构建了一个可靠的风险评分模型用于HNSCC患者的预后预测，有助于对该癌症进行精确的治疗干预。
