Construction of prognostic model of head and neck squamous carcinoma with lymph node metastasis-related gene andanalysis of tumor immunity microenvironment
10.16066/j.1672-7002.2024.05.004
- VernacularTitle:基于淋巴结转移相关基因的头颈部鳞状细胞癌预后模型构建和肿瘤免疫微环境分析
- Author:
Guanghao ZHU
1
;
Hui YAO
;
Haopu LI
;
Jingjie WANG
;
Minhui ZHU
;
Hongliang ZHENG
Author Information
1. 海军军医大学附属长海医院耳鼻咽喉头颈外科,上海 200433
- Keywords:
Head and Neck Neoplasms;
Squamous Cell Carcinoma of Head and Neck;
Prognosis;
lymph node metastasis;
WGCNA;
immune infiltration
- From:
Chinese Archives of Otolaryngology-Head and Neck Surgery
2024;31(5):287-291
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To identify the key genes associated with lymph node metastasis in head and neck squamous carcinoma(HNSCC)and construct a prognostic model based on The Cancer Genome Atlas(TCGA)database.METHODS Differentially expressed genes(DEGs)between tumor tissues and normal tissues in the HNSCC dataset in the TCGA database were screened by R software,and gene modules related to lymph node metastasis were screened by weighted gene co-expression network(weighted gene co-expression network analysis,WGCNA).Prognostic risk models were constructed by univariate cox regression and Lasso regression analyses.Survival analyses and ROC curves were performed to verify the Reliability of prognostic models.CIBERSORT,TIMER and ESTIMATE algorithms analysed the differences in the tumor micro environment(TME)of different risk groups.RESULTS There were 2 565 DEGs screened,and a set of gene modules highly correlated with disease prognosis and lymph node metastasis were obtained by WGCNA analysis,and correlation analysis verified that the expression of genes in this gene module was highly correlated with lymph node metastasis.Univariate cox regression and Lasso regression were used to identify 6 key prognostic genes:CDKN2A,CCNE2,KNSTRN,AURKA,KPNA2,and ORC1.A prognostic model was constructed based on the 6 genes,and survival analysis showed that the prognosis of the high-risk group was significantly worse than that of the low-risk group(P<0.0001).The ROC curves demonstrated the good predictive performance of this prognostic model.CIBERSORT analyses revealed differences in the immune microenvironment of tumors in different risk groups.CONCLUSION The 6 key prognostic genes screened were helpful in predicting the prognosis of HNSCC patients and were closely associated with the immune microenvironment of HNSCC,suggesting that they may serve as potential therapeutic targets.
