Tumor immunotherapy has emerged as a promising method in cancer treatment,but patient responses vary,necessitating personalized strategies and prognostic biomarkers.This study aimed to identify prognostic factors and ...Tumor immunotherapy has emerged as a promising method in cancer treatment,but patient responses vary,necessitating personalized strategies and prognostic biomarkers.This study aimed to identify prognostic factors and construct a predictive model for patient survival outcomes and immunotherapy response.We curated six immunotherapy datasets representing diverse cancer types and treatment regimens.After data preprocessing,patients were stratified based on immunotherapy response.Differential gene expression analysis identified 22 genes consistently dysregulated across multiple datasets.Functional analysis provided critical insights,highlighting the enrichment of these dysregulated genes in immune response pathways and tumor microenvironment-related processes.To create a robust prognostic model,we meticulously employed a multistep approach.Initially,the identified 22 genes underwent rigorous univariate Cox regression analysis to evaluate their individual associations with patient survival outcomes.Genes showing statistical significance(p-values<0.05)at this stage advanced to the subsequent multivariate Cox regression analysis,which aimed to address potential confounding factors and collinearity among genes.From this analysis,we ultimately identified four key genes—ST6GALNAC2,SNORA65,MFAP2,and CDKN2B—that were significantly associated with patient survival outcomes.Incorporating these four key genes along with their corresponding coefficients,we constructed a predictive model.This model’s efficacy was validated through extensive Cox regression analyses,demonstrating its robustness in predicting patient survival outcomes.Furthermore,our model exhibited promising predictive capability for immunotherapy response,providing a potential tool for anticipating treatment efficacy.These findings provide insights into immunotherapy response mechanisms and suggest potential prognostic biomarkers for personalized treatment.Our study contributes to advancing cancer immunotherapy and personalized medicine.展开更多
文摘Tumor immunotherapy has emerged as a promising method in cancer treatment,but patient responses vary,necessitating personalized strategies and prognostic biomarkers.This study aimed to identify prognostic factors and construct a predictive model for patient survival outcomes and immunotherapy response.We curated six immunotherapy datasets representing diverse cancer types and treatment regimens.After data preprocessing,patients were stratified based on immunotherapy response.Differential gene expression analysis identified 22 genes consistently dysregulated across multiple datasets.Functional analysis provided critical insights,highlighting the enrichment of these dysregulated genes in immune response pathways and tumor microenvironment-related processes.To create a robust prognostic model,we meticulously employed a multistep approach.Initially,the identified 22 genes underwent rigorous univariate Cox regression analysis to evaluate their individual associations with patient survival outcomes.Genes showing statistical significance(p-values<0.05)at this stage advanced to the subsequent multivariate Cox regression analysis,which aimed to address potential confounding factors and collinearity among genes.From this analysis,we ultimately identified four key genes—ST6GALNAC2,SNORA65,MFAP2,and CDKN2B—that were significantly associated with patient survival outcomes.Incorporating these four key genes along with their corresponding coefficients,we constructed a predictive model.This model’s efficacy was validated through extensive Cox regression analyses,demonstrating its robustness in predicting patient survival outcomes.Furthermore,our model exhibited promising predictive capability for immunotherapy response,providing a potential tool for anticipating treatment efficacy.These findings provide insights into immunotherapy response mechanisms and suggest potential prognostic biomarkers for personalized treatment.Our study contributes to advancing cancer immunotherapy and personalized medicine.
