头颈部鳞状细胞癌铜死亡相关非编码长链RNA预后模型及药敏分析

Cuproptosis-long non-coding RNAs prognostic marker for head and neck squamous cell carcinoma and drug sensitivity analysis

目的 探索铜死亡相关长链非编码RNA(lncRNAs)作为的新生物标志物在头颈部鳞状细胞癌(HNSCC)预后价值。方法 从美国公共基因数据库癌症基因组图谱(TCGA)数据库下载HNSCC的RNA转录组数据和临床数据。通过软件/数据库进行差异lncRNAs筛选,并鉴定出有预后价值的铜死亡相关的lncRNAs。然后进行最小绝对值收敛和选择算子回归(LASSO)回归分析,构建预后预测模型,并利用基因本体功能查询数据库(GO)和京都基因和基因组数据库(KEGG)对其生物学特征进行评价,最后通过化疗药物敏感性分析“pRRophetic”R包预测肿瘤差异lncRNAs表达水平和临床化疗反应的关系。结果 本研究在HNSCC患者肿瘤组织中,鉴定出25个与HNSCC患者的生存预后有相关的lncRNAs(P<0.05),进一步使用LASSO算法,构建出13个lncRNAs的预后风险模型。HNSCC通过模型把所有患者分为高风险组和低风险组绘制风险散点图和Kaplan-Meier生存曲线,高风险组的生存情况分布和生存时间均显著低于低风险组(均P<0.01)。模型预测的HNSCC患者的1年、3年和5年生存期的曲线下面积(AUC)分别为0.680、0.663和0.651。单因素、多因素回归分析中,风险评分的风险比(HRs)分别为1.396和1.488(P<0.01)。基因功能GO富集分析显示,差异表达的基因(DEGs)主要参与了免疫相关的生物过程,如体液免疫反应、淋巴细胞介导的免疫和免疫反应等;KEGG信号通路分析显示,DEGs主要参与人免疫反应、免疫反应调节及B细胞激活等信号通路。药敏分析发现高低风险组对10种抗肿瘤药物的IC50存在显著差异(均P<0.01),且低风险型组对这些抗肿瘤药物的敏感性更强;风险评分与IC50值成正相关关系(P<0.01)。结论 本研究构建的铜死亡基因相关lncRNAs预后模型可以用于预测HNSCC患者的临床结局,并可为抗肿瘤个体化治疗提供参考。

Objective To explore the prognostic value of cuproptosis-associated long non-coding RNAs(lncRNAs)as novel biomarkers for head and neck squamous cell carcinoma(HNSCC).Methods RNA-seq transcriptome data and clinical data for HNSCC were downloaded from the The Cancer Genome Atlas(TCGA)database.Prognostic lncRNAs associated with cuprotosis genes have been identified.Then least absolute shrinkage and selection operator regression(LASSO)analysis was performed to constructed a risk signature.Then,Gene Ontology(GO)and Kyoto absolute shrinkage and selection operator regression(KEGG)were used to evaluate the biological profiles.Finally,the R package“pRRophetic”was used to predict the Antitumor drugs sensitivity.Results In this study,25lncRNAs associated with survival were identified in HNSCC(P<0.05),and the LASSO algorithm was used to construct a 13-lncRNAs risk signature.HNSCC patients were divided into high-risk group and low-risk group.Risk scatter plots and Kaplan-Meier survival curves showed that high-risk group had worse survival outcomes than low-risk group(all P<0.01).For one-,three-,and five-year periods,AUCs were 0.680,0.663,and 0.651,respectively.The hazard ratios(HRs)of the risk score in uni-Cox and multi-Cox regression,were 1.396 and1.488,respectively(P<0.01).GO enrichment analysis of gene function showed that DEGs was mainly involved in immune-related biological processes,such as humoral immune response,lymphocyte-mediated immunity and immune response.KEGG analysis showed that DEGs was mainly involved in human immune response,immune response regulation and B cell activation.Drug sensitivity comparison showed 10 anti-cancer drugs have significant differences in IC_(50)between the two groups(all P<0.01),with lower IC_(50)in the low-risk group.Besides the risk score was positively correlated with the IC_(50)(P<0.01).Conclusion The 13-lncRNAs risk signature might provide insights for predicting clinical outcomes for HNSCC patients,this novel model might also provide recommendations for individual therapeutic approaches.