{"title":"An LNM-Associated Gene Signature for Prognostic Prediction and Immune Profiling in Head and Neck Squamous Cell Carcinoma.","authors":"Zhenzhen Wang, Zhenhua Wu, Lixin Cheng, Qi Huang, Jian Zhang, Yuan Ren, Juntao Huang, Yi Shen","doi":"10.1089/cbr.2024.0147","DOIUrl":null,"url":null,"abstract":"<p><p>Lymph node metastasis (LNM) plays a critical role in the prognosis of head and neck squamous cell carcinoma (HNSCC). To enhance prognostic predictions and investigate the molecular interplay between LNM and HNSCC, we developed an LNM-associated gene signature. Data was sourced from The Cancer Genome Atlas (TCGA), encompassing RNA-sequencing and clinical profiles. We stratified patients based on LNM status and identified differentially expressed genes (DEGs) between lymph node-negative (N0) and lymph node-positive (N1-3) groups. A prognostic model was then constructed while employing Least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses. Patients were randomly allocated into training (70%) and internal validation (30%) cohorts, with an additional external dataset used for validation. The predictive model's performance was assessed through receiver operating characteristic curves and survival analyses. We identified 79 LNM-related prognostic DEGs that formed the basis of our LNM-related risk score (LNMRS). This score stratified patients into low- and high-risk categories, with those having lower LNMRS exhibiting improved survival outcomes, increased immune cell infiltration, and enhanced responses to immunotherapy (PD-1/CTLA4 inhibitors) and chemotherapy. In contrast, patients with high LNMRS showed poorer prognosis and reduced immune responsiveness. Our LNM-related model provides insights into the molecular mechanisms linking LNM and HNSCC and offers a promising tool for personalized treatment strategies. This approach underscores the potential of integrating LNM status with gene expression profiles to refine prognosis and optimize therapeutic interventions in HNSCC.</p>","PeriodicalId":55277,"journal":{"name":"Cancer Biotherapy and Radiopharmaceuticals","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Biotherapy and Radiopharmaceuticals","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1089/cbr.2024.0147","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Lymph node metastasis (LNM) plays a critical role in the prognosis of head and neck squamous cell carcinoma (HNSCC). To enhance prognostic predictions and investigate the molecular interplay between LNM and HNSCC, we developed an LNM-associated gene signature. Data was sourced from The Cancer Genome Atlas (TCGA), encompassing RNA-sequencing and clinical profiles. We stratified patients based on LNM status and identified differentially expressed genes (DEGs) between lymph node-negative (N0) and lymph node-positive (N1-3) groups. A prognostic model was then constructed while employing Least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses. Patients were randomly allocated into training (70%) and internal validation (30%) cohorts, with an additional external dataset used for validation. The predictive model's performance was assessed through receiver operating characteristic curves and survival analyses. We identified 79 LNM-related prognostic DEGs that formed the basis of our LNM-related risk score (LNMRS). This score stratified patients into low- and high-risk categories, with those having lower LNMRS exhibiting improved survival outcomes, increased immune cell infiltration, and enhanced responses to immunotherapy (PD-1/CTLA4 inhibitors) and chemotherapy. In contrast, patients with high LNMRS showed poorer prognosis and reduced immune responsiveness. Our LNM-related model provides insights into the molecular mechanisms linking LNM and HNSCC and offers a promising tool for personalized treatment strategies. This approach underscores the potential of integrating LNM status with gene expression profiles to refine prognosis and optimize therapeutic interventions in HNSCC.
期刊介绍:
Cancer Biotherapy and Radiopharmaceuticals is the established peer-reviewed journal, with over 25 years of cutting-edge content on innovative therapeutic investigations to ultimately improve cancer management. It is the only journal with the specific focus of cancer biotherapy and is inclusive of monoclonal antibodies, cytokine therapy, cancer gene therapy, cell-based therapies, and other forms of immunotherapies.
The Journal includes extensive reporting on advancements in radioimmunotherapy, and the use of radiopharmaceuticals and radiolabeled peptides for the development of new cancer treatments.