{"title":"A novel immune-related gene prognostic signature combining immune cell infiltration and immune checkpoint for glioblastoma patients.","authors":"Xu Liu, Xiaomei Liu","doi":"10.21037/tcr-24-562","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Glioblastoma (GBM) is a highly lethal brain tumor with a complex tumor microenvironment (TME) and poor prognosis. This study aimed to develop and validate a novel immune-related prognostic model for GBM patients to enhance personalized prognosis prediction and develop effective therapeutic strategies.</p><p><strong>Methods: </strong>RNA sequencing and clinical data for GBM patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) (GSE83300). Single-sample gene set enrichment analysis (ssGSEA) was performed using the gene set variation analysis (GSVA) package in R to classify the samples into high and low immune infiltration clusters based on 29 immune cell subtypes. Clustering validations included differential analysis of immune scores and comparison of human leukocyte antigen (HLA) family expression and immune cell subtypes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) analysis compared molecular mechanisms and cellular functions between clusters. Differentially expressed immune-related genes between the high and low immune infiltration clusters were screened out, and the prognostic immune-related genes (PIGs) were identified using univariate Cox regression. Co-expression analysis between PIGs and transcription factors (TFs) (Cistrome) was conducted, and a protein-protein interaction (PPI) network (STRING) was constructed. Least absolute shrinkage and selection operator (LASSO) regression constructed a prognostic model. Correlation analyses between PIGs, immune infiltrates, and GBM-related genes were performed. Tumor mutation burden (TMB) analysis and a nomogram incorporating age, gender, and risk score were developed for individualized prognosis prediction.</p><p><strong>Results: </strong>A total of 312 differentially expressed immune-related genes were identified between high and low immune infiltration clusters. Of these, 28 genes were correlated with GBM prognosis. LASSO regression identified 10 genes (<i>CLCF1, PTX3, TNFRSF14, SDC2, VGF, AREG, PLAUR, GRN, AQP9</i>, and <i>IGLV6-57</i>) for the prognostic model. Patients were divided into high-risk and low-risk groups based on risk scores. Survival analysis showed significantly better overall survival (OS) for the low-risk group (P<0.05). The prognostic signature was validated as an independent prognostic factor. Correlation analyses demonstrated significant associations between the prognostic model, immune cell infiltrates, GBM-related genes, and immune checkpoint-related genes. A nomogram incorporating age, gender, and risk score was developed for personalized prognosis prediction.</p><p><strong>Conclusions: </strong>In summary, our study provided a novel prognostic model based on ssGSEA for GBM patients and offered potential insights for understanding the tumor immune and molecular mechanisms of the disease.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"13 11","pages":"6136-6153"},"PeriodicalIF":1.5000,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11651773/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational cancer research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/tcr-24-562","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/11/19 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Glioblastoma (GBM) is a highly lethal brain tumor with a complex tumor microenvironment (TME) and poor prognosis. This study aimed to develop and validate a novel immune-related prognostic model for GBM patients to enhance personalized prognosis prediction and develop effective therapeutic strategies.
Methods: RNA sequencing and clinical data for GBM patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) (GSE83300). Single-sample gene set enrichment analysis (ssGSEA) was performed using the gene set variation analysis (GSVA) package in R to classify the samples into high and low immune infiltration clusters based on 29 immune cell subtypes. Clustering validations included differential analysis of immune scores and comparison of human leukocyte antigen (HLA) family expression and immune cell subtypes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) analysis compared molecular mechanisms and cellular functions between clusters. Differentially expressed immune-related genes between the high and low immune infiltration clusters were screened out, and the prognostic immune-related genes (PIGs) were identified using univariate Cox regression. Co-expression analysis between PIGs and transcription factors (TFs) (Cistrome) was conducted, and a protein-protein interaction (PPI) network (STRING) was constructed. Least absolute shrinkage and selection operator (LASSO) regression constructed a prognostic model. Correlation analyses between PIGs, immune infiltrates, and GBM-related genes were performed. Tumor mutation burden (TMB) analysis and a nomogram incorporating age, gender, and risk score were developed for individualized prognosis prediction.
Results: A total of 312 differentially expressed immune-related genes were identified between high and low immune infiltration clusters. Of these, 28 genes were correlated with GBM prognosis. LASSO regression identified 10 genes (CLCF1, PTX3, TNFRSF14, SDC2, VGF, AREG, PLAUR, GRN, AQP9, and IGLV6-57) for the prognostic model. Patients were divided into high-risk and low-risk groups based on risk scores. Survival analysis showed significantly better overall survival (OS) for the low-risk group (P<0.05). The prognostic signature was validated as an independent prognostic factor. Correlation analyses demonstrated significant associations between the prognostic model, immune cell infiltrates, GBM-related genes, and immune checkpoint-related genes. A nomogram incorporating age, gender, and risk score was developed for personalized prognosis prediction.
Conclusions: In summary, our study provided a novel prognostic model based on ssGSEA for GBM patients and offered potential insights for understanding the tumor immune and molecular mechanisms of the disease.
期刊介绍：
Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
