Screening And Prognostic Study Of Immune-related LncRNAs In Head And Neck Squamous Carcinoma

Head and neck squamous cell carcinoma(HNSCC)is a common malignancies with a high mortality rate.This thesis analyzes the prognostic value of potentially prognostic immune-related long non-coding RNAs(lnc RNAs)based on RNA sequencing data of HNSCC samples from The Cancer Genome Atlas(TCGA)database and clinical data of HNSCC samples from UCSC Xena database,and constructs a prognostic model to assess the survival outcomes of HNSCC patients,providing a new way to predict the prognosis of HNSCC.The main contents are as follows:(1)Cox proportional risk model and Lasso-Cox regression model were used to identify seven immune lnc RNAs associated with overall survival in HNSCC and to construct a prognostic risk score model.The performance of the prognostic model was evaluated using the Kaplan-Meier method and time-dependent receiver operating characteristic curve The results suggest that the risk score model based on the above seven immune-related lnc RNAs could provide reliable prognostic prediction for patients of HNSCC.A smaller number of immune-related lnc RNAs were obtained for prognostic modeling using different methods under conditions of similar performance.(2)The relationship between clinical characteristics such as age,gender,clinical stage and grade and prognosis of patients with HNSCC was investigated using the Kaplan-Meier method and the Cox proportional risk model.The results suggest that age is an important factor influencing patient prognosis and that the older the patient,the worse the survival outcome.Among them,there was a significant difference in the prognosis of HNSCC around the age of 70 years.(3)For the first time,the relationship between immune-related lnc RNAs and the prognosis of patients with advanced HNSCC was investigated,and eight immune lnc RNAs associated with the prognosis of advanced patients were identified based on univariate Cox proportional risk model,Lasso-Cox regression model and Coxboost model analysis,and a prognostic model was successfully constructed using randomized survival forest.The results show that the prognostic model based on the eight immune-related lnc RNAs described above has good predictive power for survival prognosis in patients with advanced disease,and that the model is applicable in different clinical subgroups.