%0 Journal Article %T MYEOV与TMB：评价肺癌预后及ICIs疗效的新指标
MYEOV and TMB: Novel Biomarkers for Assessing Lung Cancer Prognosis and ICIs Efficacy %A 管梓彤 %A 宋禄红 %A 代成成 %A 马学真 %J Advances in Clinical Medicine %P 1670-1682 %@ 2161-8720 %D 2025 %I Hans Publishing %R 10.12677/acm.2025.1561903 %X 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 < 0.05)，且高TMB组表达水平更高(p < 0.05)。富集分析显示DEGs主要参与嗅觉传导、脂肪代谢及G蛋白偶联受体信号通路。免疫相关性分析表明MYEOV与TNFSF15、CD80等免疫检查点显著相关(p < 0.05)，且与TMB呈正相关(r = 0.16, p < 0.001)。全外显子测序验证显示肺癌患者TMB水平与体细胞变异数正相关，转录组测序证实MYEOV在癌组织过表达(p < 0.05)。研究结果表明，MYEOV是肺癌独立预后风险基因，其高表达与TMB正相关，且通过调控免疫检查点影响肿瘤微环境，参与免疫逃逸。联合TMB与MYEOV检测有望提升ICIs治疗疗效预测精度，为肺癌精准治疗提供新策略。
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 < 0.05) and elevated levels in high-TMB groups (p < 0.05). Enrichment analysis revealed DEGs primarily involved in olfactory transduction, lipid metabolism, and G protein-coupled receptor signaling pathways. Immune correlation analysis demonstrated MYEOV’s significant associations with immune checkpoints including TNFSF15 and CD80 (p < 0.05), and positive correlation with TMB (r = 0.16, p < 0.001). Whole exome sequencing (WES) confirmed TMB’s positive correlation with somatic mutation counts, while transcriptome sequencing validated MYEOV overexpression in tumor tissues (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 %K 肺癌， %K 肿瘤突变负荷， %K 免疫检查点抑制剂， %K MYEOV， %K 预后
Lung Cancer %K Tumor Mutational Burden %K Immune Checkpoint Inhibitors %K MYEOV %K Prognosis %U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=118252