目的

建立基于免疫和代谢相关基因的头颈部鳞状细胞癌的预后模型，并评估该模型在HNSCC患者预后中的有效性。

方法

通过TCGA数据库下载HNSCC患者数据集，GEO数据库下载GSE65858数据集，IMMPORT、MsigDB数据库获取免疫和代谢相关基因集。TCGA数据集分为训练队列和验证队列，训练队列采用单因素和多因素Cox回归以及LASSO回归构建IMRGs预后模型。qPCR检测预后基因组织表达。TCGA验证队列和GSE65858数据集进行内外部验证。免疫浸润分析、GSEA通路富集分析、肿瘤突变负荷分析比较高低风险亚组的通路机制差异。

结果

基于TCGA训练集数据构建了包含10个基因（HLA-F、SLC11A1、GNRH1、LGR5、ICOS、HPRT1肿瘤组织中高表达，DES、BTC、DDO、CDO1肿瘤组织中低表达）的IMRGs头颈部鳞状细胞癌预后模型。低风险亚组生存时间明显长于高风险组。单因素和多因素Cox分析显示风险评分是患者独立预后因素。风险评分结合多种临床特征构建的列线图可有效用于患者预后预测，ROC曲线下面积0.825，校准曲线C-dex 0.751。GSEA富集分析和免疫细胞浸润分析表明B细胞在低风险组患者中高表达。基因相关性分析和肿瘤突变负荷分析表明高风险组患者DNA复制基因（MCM6、POLD3）、错配修复基因（MSH6）上皮间质转化基因（LOXL2）表达更高，肿瘤突变率更高。

结论

基于10个基因构建的HNSCC患者IMRGs模型可以有效预测患者预后，可为临床疾病治疗提供新的靶点。

Objective

To develop a prognostic model for head and neck squamous cell carcinoma（HNSCC） utilizing immune and metabolic-related genes， and to assess its efficacy in predicting the prognosis of HNSCC patients.

Methods

The HNSCC patient dataset was obtained from the TCGA database， while the GSE65858 dataset was sourced from the GEO database. Additionally， immune and metabolic-related gene sets were acquired from the IMMPORT and MsigDB databases， respectively. Subsequently， the TCGA dataset was partitioned into a training cohort and a validation cohort. Univariate and multifactor Cox regression，along with LASSO regression， were employed to develop prognostic models for immune-related gene signatures （IMRGs） in the training cohort. Tissue expression of prognostic genes was detected by qPCR.Internal validation was carried out in the TCGA validation cohort， followed by external validation in the GSE65858 dataset. Immune infiltration analysis， Gene Set Enrichment Analysis （GSEA） pathway enrichment analysis， and tumor mutation load analysis were conducted to delineate pathway mechanism disparities between high and low-risk subgroups.

Results

Utilizing TCGA training set data， a prognostic model for head and neck squamous cell carcinoma was developed， comprising 10 genes （HLA-F， SLC11A1， GNRH1，LGR5， ICOS， and HPRT1 were highly expressed， while DES， BTC， DDO， and CDO1 were lowly expressed in tumor tissues）. The survival duration of the low-risk subgroup significantly exceeded that of the high-risk group. Both univariate and multivariate COX analyses confirmed the risk score as an independent prognostic determinant. The combination of a risk score and various clinical features proves effective in predicting patient prognosis， as evidenced by an area under the receiver operating curve of 0.825 and a C-index of 0.751.GSEA enrichment and immune cell infiltration analyses revealed elevated B cell expression in the low-risk group. Moreover， gene correlation and tumor mutation load analyses indicated heightened levels of DNA replicators （MCM6 and POLD3）， mismatch repair gene （MSH6）， epithelial mesenchymal transformation gene （LOXL2）， and tumor mutation rate in the high-risk group.

Conclusion

The IMRGs model for HNSCC patients constructed based on 10 genes can effectively predict the prognosis of patients， and provide a new target for clinical prognosis identification and treatment.