This study utilized a cohort design involving patients with head and neck squamous cell carcinoma (HNSCC). Data were sourced from the TCGA-HNSCC training cohort and validation cohorts from the TCGA and Gene Expression Omnibus (GEO) databases. The analysis focused on a Breg-related gene signature and OLR1 gene expression to evaluate prognosis and biological mechanisms.
High-risk patients exhibited significantly poorer survival compared to low-risk patients. Additionally, high-risk groups demonstrated reduced immune cell infiltration and lower immune molecule expression. The OLR1 gene was identified as oncogenic and linked to immune evasion within the tumor microenvironment.
Prognostic accuracy improved when the risk score was integrated with tumor mutation burden (TMB) or clinicopathological features. The signature reflects the immune landscape and may help direct personalized therapeutic approaches, such as immunotherapy. However, the study did not report specific adverse events, discontinuations, or tolerability data, as these were not applicable to the bioinformatics and functional experiment design.
Key limitations include the reliance on bioinformatics analyses and transcriptome sequencing rather than prospective clinical trial data. The role of Bregs in HNSCC remains unclear based on background statements. Consequently, while associations are strong, causal inferences for all findings require further validation in clinical settings.
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BackgroundRegulatory B cells (Bregs) are critical in regulating immune responses and fostering immune tolerance in various cancers; however, their role in head and neck squamous cell carcinoma (HNSCC) is unclear. This study examined the function of Breg-related genes in HNSCC and their possible prognostic and therapeutic implications.MethodsThe Cancer Genome Atlas (TCGA)-HNSCC training cohort was used to establish a prognostic signature for Breg-related genes by applying consensus clustering, univariate Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression, and multivariate Cox regression analyses. Validation cohorts from the TCGA and Gene Expression Omnibus (GEO) databases were used to assess the robustness of the model. This study investigated the associations among the signature and several clinicopathological features, expression of immune checkpoints, tumor mutation burden (TMB), and sensitivity to pharmacological agents. The underlying mechanisms were examined using weighted gene co-expression network analysis (WGCNA) and gene set enrichment analysis (GSEA). Additionally, various techniques, including ESTIMATE, were used to assess immune infiltration. Functional experiments and transcriptome sequencing were conducted to investigate the role of oxidized low-density lipoprotein receptor 1 (OLR1) gene.ResultsThe analysis identified an eight-gene Breg-related prognostic signature that demonstrated robust predictive power across cohorts. High-risk patients exhibited significantly poorer survival, reduced immune cell infiltration, and lower immune molecule expression. The prognostic accuracy was further improved by integrating the risk score with TMB or clinicopathological features. Functional analyses revealed strong associations with immune-related pathways. Moreover, the signature was reported as a potential biomarker for predicting immunotherapy response and drug sensitivity. Furthermore, OLR1, the most essential gene of the signature, was found to be oncogenic and linked to immune evasion in HNSCC.ConclusionsThe Breg-related gene signature provides an effective prognostic tool for patients with HNSCC, reflects the immune landscape and TMB, and may direct personalized therapeutic approaches, such as immunotherapy.