MYEOV and TMB: Novel Biomarkers for Assessing Lung Cancer Prognosis and ICIs Efficacy

肺癌是目前全球发病率和死亡率最高的恶性肿瘤，精准治疗面临驱动基因突变靶向药物覆盖率不足及免疫检查点抑制剂(ICIs)有效率低的挑战。肿瘤突变负荷(TMB)能够反映新抗原生成潜力，进而影响免疫系统识别并激活抗肿瘤反应的能力，可以作为ICIs治疗疗效的预测标志物，但其具体机制尚不明确。本研究通过整合TCGA、CCLE及cBioPortal数据库的肺癌样本数据，以TMB中位值分组鉴定出751个上调差异表达基因(DEGs)，与1170个预后相关基因取交集获得24个关键基因。多因素Cox分析确定MYEOV为独立预后危险因素(HR > 1)，其在肺癌组织表达显著高于正常组织(p p p p p Lung cancer currently has the highest global incidence and mortality rates among malignant tumors, with precision treatment facing challenges including insufficient coverage of targeted therapies for driver gene mutations and low response rates to immune checkpoint inhibitors (ICIs). Tumor mutational burden (TMB) reflects neoantigen generation potential and influences immune system recognition and activation of anti-tumor responses, serving as a predictive biomarker for ICIs efficacy. However, its underlying mechanisms remain unclear. This study integrated lung cancer sample data from TCGA, CCLE, and cBioPortal databases, identifying 751 upregulated differentially expressed genes (DEGs) through TMB median-based stratification. Intersection with 1170 prognosis-associated genes yielded 24 key genes. Multivariate Cox analysis identified MYEOV as an independent prognostic risk factor (HR > 1), showing significantly higher expression in tumor tissues versus normal tissues (p p p p p < 0.05). These findings establish MYEOV as an independent prognostic risk gene in lung cancer, with its high expression correlating positively with TMB. MYEOV likely modulates immune checkpoints to influence the tumor microenvironment and promote immune escape. Combined assessment of TMB and MYEOV may enhance prediction accuracy for ICIs therapeutic efficacy, offering novel strategies for precision treatment in lung cancer.