Department of Oncology, Jurong Hospital Affiliated to Jiangsu University, Zhenjiang, China.
Department of Radiation Oncology, Shanghai First Maternal and Child Health Care Hospital, Shanghai, China.
Medicine (Baltimore). 2023 Jun 9;102(23):e33994. doi: 10.1097/MD.0000000000033994.
Treatment of head and neck squamous cell carcinoma (HNSCC) is a substantial clinical challenge due to the high local recurrence rate and chemotherapeutic resistance. This project seeks to identify new potential biomarkers of prognosis prediction and precision medicine to improve this condition. A synthetic data matrix for RNA transcriptome datasets and relevant clinical information on HNSCC and normal tissues was downloaded from the Genotypic Tissue Expression Project and The Cancer Genome Atlas (TCGA). The necrosis-associated long-chain noncoding RNAs (lncRNAs) were identified by Pearson correlation analysis. Then 8-necrotic-lncRNA models in the training, testing and entire sets were established through univariate Cox (uni-Cox) regression and Lasso-Cox regression. Finally, the prognostic ability of the 8-necrotic-lncRNA model was evaluated via survival analysis, nomogram, Cox regression, clinicopathological correlation analysis, and receiver operating characteristic (ROC) curve. Gene enrichment analysis, principal component analysis, immune analysis and prediction of risk group semi-maximum inhibitory concentration (IC50) were also conducted. Correlations between characteristic risk score and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anti-cancer drug sensitivity were analyzed. Eight necrosis-associated lncRNAs (AC099850.3, AC243829.2, AL139095.4, SAP30L-AS1, C5orf66-AS1, LIN02084, LIN00996, MIR4435-2HG) were developed to improve the prognosis prediction of HNSCC patients. The risk score distribution, survival status, survival time, and relevant expression standards of these lncRNAs were compared between low- and high-risk groups in the training, testing and entire sets. Kaplan-Meier analysis showed the low-risk patients had significantly better prognosis. The ROC curves revealed the model had an acceptable predictive value in the TCGA training and testing sets. Cox regression and stratified survival analysis indicated that the 8 necrosis-associated lncRNAs were risk factors independent of various clinical parameters. We recombined the patients into 2 clusters through Consensus ClusterPlus R package according to the expressions of necrotic lncRNAs. Significant differences were found in immune cell infiltration, immune checkpoint molecules, and IC50 between clusters, suggesting these characteristics can be used to evaluate the clinical efficacy of chemotherapy and immunotherapy. This risk model may serve as a prognostic signature and provide clues for individualized immunotherapy for HNSCC patients.
治疗头颈部鳞状细胞癌（HNSCC）是一个重大的临床挑战，因为局部复发率高和化疗耐药性。本项目旨在寻找新的潜在生物标志物，以进行预后预测和精准医学，从而改善这种情况。从基因表达组织图谱计划（GTEx）和癌症基因组图谱（TCGA）下载了用于 HNSCC 和正常组织的 RNA 转录组数据集和相关临床信息的综合数据矩阵。通过 Pearson 相关分析鉴定了与坏死相关的长链非编码 RNA（lncRNA）。然后，通过单变量 Cox（uni-Cox）回归和 Lasso-Cox 回归，在训练、测试和整个数据集建立了 8 个坏死-lncRNA 模型。通过生存分析、列线图、Cox 回归、临床病理相关性分析和接受者操作特征（ROC）曲线评估 8 个坏死-lncRNA 模型的预后能力。还进行了基因富集分析、主成分分析、免疫分析和风险组半最大抑制浓度（IC50）的预测。分析了特征风险评分与免疫细胞浸润、免疫检查点分子、体细胞基因突变和抗癌药物敏感性之间的相关性。
八种与坏死相关的 lncRNA（AC099850.3、AC243829.2、AL139095.4、SAP30L-AS1、C5orf66-AS1、LIN02084、LIN00996、MIR4435-2HG）的开发提高了 HNSCC 患者的预后预测能力。在训练、测试和整个数据集的低风险和高风险组之间比较了这些 lncRNA 的风险评分分布、生存状态、生存时间和相关表达标准。Kaplan-Meier 分析显示，低风险患者的预后明显更好。ROC 曲线表明，该模型在 TCGA 训练和测试集中具有可接受的预测价值。Cox 回归和分层生存分析表明，8 种与坏死相关的 lncRNA 是独立于各种临床参数的危险因素。根据坏死 lncRNA 的表达，我们通过 Consensus ClusterPlus R 包将患者重新组合为 2 个簇。在簇之间发现了免疫细胞浸润、免疫检查点分子和 IC50 的显著差异，这表明这些特征可用于评估化疗和免疫治疗的临床疗效。该风险模型可作为预后标志物，并为 HNSCC 患者的个体化免疫治疗提供线索。
