Li Yanxi, Li Peiran, Liu Yuqi, Geng Wei
Department of Dental Implant Center, Beijing Stomatological Hospital, School of Stomatology, Capital Medical University, Beijing, 100050, China.
Department of Maxillofacial Surgery, Beijing Stomatological Hospital, School of Stomatology, Capital Medical University, Beijing, 100050, China.
Heliyon. 2024 Apr 16;10(8):e29449. doi: 10.1016/j.heliyon.2024.e29449. eCollection 2024 Apr 30.
Head and neck squamous cell carcinoma (HNSCC) is a significant global health challenge. The identification of reliable prognostic biomarkers and construction of an accurate prognostic model are crucial.
In this study, mRNA expression data and clinical data of HNSCC patients from The Cancer Genome Atlas were used. Overlapping candidate genes (OCGs) were identified by intersecting differentially expressed genes and prognosis-related genes. Best prognostic genes were selected using the least absolute shrinkage and selection operator Cox regression based on OCGs, and a risk score was developed using the Cox coefficient of each gene. The prognostic power of the risk score was assessed using Kaplan-Meier survival analysis and time-dependent receiver operating characteristic analysis. Univariate and multivariate Cox regression were performed to identify independent prognostic parameters, which were used to construct a nomogram. The predictive accuracy of the nomogram was evaluated using calibration plots. Functional enrichment analysis of risk score related genes was performed to explore the potential biological functions and pathways. External validation was conducted using data from the Gene Expression Omnibus and ArrayExpress databases.
FADS3, TNFRSF12A, TJP3, and FUT6 were screened to be significantly related to prognosis in HNSCC patients. The risk score effectively stratified patients into high-risk group with poor overall survival (OS) and low-risk group with better OS. Risk score, age, clinical M stage and clinical N stage were regarded as independent prognostic parameters by Cox regression analysis and used to construct a nomogram. The nomogram performed well in 1-, 2-, 3-, 5- and 10-year survival predictions. Functional enrichment analysis suggested that tight junction was closely related to the cancer. In addition, the prognostic power of the risk score was validated by external datasets.
This study constructed a gene-based model integrating clinical prognostic parameters to accurately predict prognosis in HNSCC patients.
头颈部鳞状细胞癌（HNSCC）是一项重大的全球健康挑战。识别可靠的预后生物标志物并构建准确的预后模型至关重要。
本研究使用了来自癌症基因组图谱的HNSCC患者的mRNA表达数据和临床数据。通过将差异表达基因与预后相关基因相交来识别重叠候选基因（OCG）。基于OCG，使用最小绝对收缩和选择算子Cox回归选择最佳预后基因，并使用每个基因的Cox系数开发风险评分。使用Kaplan-Meier生存分析和时间依赖性受试者工作特征分析评估风险评分的预后能力。进行单变量和多变量Cox回归以识别独立的预后参数，这些参数用于构建列线图。使用校准图评估列线图的预测准确性。对风险评分相关基因进行功能富集分析，以探索潜在的生物学功能和途径。使用来自基因表达综合数据库和ArrayExpress数据库的数据进行外部验证。
筛选出FADS3、TNFRSF12A、TJP3和FUT6与HNSCC患者的预后显著相关。风险评分有效地将患者分为总生存期（OS）较差的高危组和OS较好的低危组。通过Cox回归分析，风险评分、年龄、临床M分期和临床N分期被视为独立的预后参数，并用于构建列线图。列线图在1年、2年、3年、5年和10年生存预测中表现良好。功能富集分析表明紧密连接与癌症密切相关。此外，风险评分的预后能力通过外部数据集得到验证。
本研究构建了一个整合临床预后参数的基于基因的模型，以准确预测HNSCC患者的预后。
