肿瘤突变负担相关 6 基因风险评分在喉癌患者中的预后价值。
The prognostic value of tumor mutational burden related 6-gene-based Risk Score in laryngeal cancer patients.
机构信息
Department of Otolaryngology, Qingdao Chengyang People's Hospital, Qingdao, 266109, Shandong, China.
Department of Anesthesiology, Qingdao Chengyang People's Hospital, Qingdao, 266109, Shandong, China.
出版信息
BMC Oral Health. 2022 Nov 17;22(1):510. doi: 10.1186/s12903-022-02534-2.
BACKGROUND
Laryngeal cancer (LC) is the second frequent malignant head and neck cancer around world, while LC patients' prognosis is unsatisfactory. This study aims to investigate the prognostic value of tumor mutation burden (TMB)-related genes in LC.
METHODS
LC data was downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. TMB values of all samples were calculated basing on mutation data. The differentially expressed genes (DEGs) between LC samples with distinct TMB were subjected to univariate and LASSO Cox regression analysis to build Risk Score. Immune cell infiltration analysis was conducted in CIBERSORT.
RESULTS
Between high and low TMB LC samples, we identified 210 DEGs. Of which, six optimal genes were included to construct Risk Score, comprising FOXJ1, EPO, FGF5, SPOCK1, KCNF1 and PSG5. High risk LC patients had significantly poorer overall survival than low risk patients. The nomogram model constructed basing on Risk Score and gender showed good performance in predicting LC patients' survival probability.
CONCLUSIONS
The prognostic Risk Score model, basing on six TMB-related genes (FOXJ1, EPO, FGF5, SPOCK1, KCNF1 and PSG5), was a reliable prognostic model to separate LC patients with different prognoses.
背景
喉癌（LC）是全球第二常见的头颈部恶性肿瘤，而 LC 患者的预后并不理想。本研究旨在探讨肿瘤突变负荷（TMB）相关基因在 LC 中的预后价值。
方法
从 The Cancer Genome Atlas 和 Gene Expression Omnibus 数据库中下载 LC 数据。基于突变数据计算所有样本的 TMB 值。对 TMB 不同的 LC 样本进行差异表达基因（DEGs）的单变量和 LASSO Cox 回归分析，构建风险评分。通过 CIBERSORT 进行免疫细胞浸润分析。
结果
在高 TMB 和低 TMB 的 LC 样本之间，我们鉴定出 210 个 DEGs。其中，包含 FOXJ1、EPO、FGF5、SPOCK1、KCNF1 和 PSG5 在内的六个最优基因被纳入构建风险评分。高风险 LC 患者的总体生存率明显低于低风险患者。基于风险评分和性别构建的列线图模型在预测 LC 患者的生存概率方面表现出良好的性能。
结论
基于六个 TMB 相关基因（FOXJ1、EPO、FGF5、SPOCK1、KCNF1 和 PSG5）的预后风险评分模型是一种可靠的预后模型，可以区分具有不同预后的 LC 患者。
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