【作者】 陈晨；

【导师】 杨雷；

【作者基本信息】 吉林大学 ， 临床医学硕士（专业学位）， 2024， 硕士

【摘要】 背景:宫颈癌是威胁全球女性健康的一大病因。尽管早期宫颈癌患者在接受标准治疗后预后良好,但仍有许多有淋巴结转移和/或局部晚期肿瘤的患者会出现进展,生存率明显下降。免疫原性细胞死亡是指肿瘤细胞在外部刺激的作用下发生死亡的同时,会经历从非免疫原性到免疫原性的转变,并影响机体产生免疫应答来对抗肿瘤的过程。尽管免疫原性细胞死亡的发现与探索为恶性肿瘤的免疫治疗提供了新的方向,但目前免疫原性细胞死亡相关基因与宫颈癌预后的关系尚不清楚。因此,我们旨在开发一个基于免疫原性细胞死亡相关基因的预后模型,以预测宫颈癌患者的预后及生存结局。材料和方法:本研究分别从TCGA数据库和GEO数据库中获取了宫颈癌患者的基因表达和转录组数据、临床病理学资料以及突变负荷数据。以来自TCGA数据库中宫颈癌患者的基因表达及转录组数据和相应的临床病理学资料作为训练集,以来自GEO数据库中宫颈癌患者的数据作为本研究的测试集。随后,我们基于训练集构建了免疫原性细胞死亡相关基因的预后模型,并使用测试集对该模型进行验证,以此来预测宫颈癌患者的预后及生存结局。结果:本研究基于6个免疫原性细胞死亡相关基因(ATG5、FOXP3、IFNG、IL1B、PDIA3、TNF)而开发的风险模型可以用于预测宫颈癌患者的预后。我们进一步将该模型与其他的临床变量相结合,构建了可以预测宫颈癌患者1年、3年和5年总生存率的列线图,其预测性能优于传统的TNM分期。此外,该模型预测宫颈癌患者1年、3年和5年总生存率的敏感性及特异性的曲线下面积分别为0.809、0.695及0.709。同时,该模型与其他临床性状相比,曲线下面积最大,为0.809,表明此风险模型预测宫颈癌患者总生存期的准确性最高。高风险组与低风险组的预后可能因肿瘤微环境中免疫细胞的浸润程度不同而有所差异,该结果也提示该风险模型将有助于筛选对免疫治疗反映良好的优势人群。结论:本研究所构建的基于免疫原性细胞死亡相关基因的风险模型和列线图,可能会更加准确地预测宫颈癌患者的预后及生存结局。同时,单因素和多因素Cox回归分析也证实了该风险模型可以成为一个独立预后指标,用来预测宫颈癌患者的预后及生存结局。综上所述,通过我们对免疫原性细胞死亡相关基因的研究,为预测其他癌症类型患者的预后提供新的可以应用于临床的生物标志物。更多还原

【Abstract】 Introduction:Cervical cancer is a major threat to women’s health worldwide.After receiving standard care,patients with early-stage cervical cancer have a bright outlook;nevertheless,after developing lymph node metastases and/or locally progressed cancer,their conditions continue to deteriorate and they have much reduced survival rates.Immunogenic cell death refers to the process by which tumor cells die as a result of external stimuli while also undergoing a non-immunogenic to immunogenic transition and influencing the body’s production of an immune response against the tumor.The association between immunogenic cell death-related indicators and cervical cancer prognosis is currently unknown,despite the fact that immunogenic cell death offers fresh perspectives for immunotherapeutic treatments for malignancies.Because of this,we developed a model of genes related to immunogenic cell death to forecast the prognosis of cervical cancer patients.Materials and Methods:This study obtained gene expression data,clinical-pathological information,and mutation burden data of cervical cancer patients from both the TCGA and GEO databases.The transcriptomic data from cervical cancer patients in the TCGA database,along with corresponding clinical-pathological data,were used as the training set.The GSE44001 cervical cancer dataset from the GEO database was used as the validation set.Subsequently,we constructed a prognostic model for immunogenic cell death-related genes based on the training set and validated the model using the test set to predict the survival outcomes of cervical cancer patients.Results:The risk model developed based on 6 immunogenic cell death-related genes in this study can be used to predict the prognosis of cervical cancer patients.Furthermore,we combined this model with other clinical variables to create a nomogram for predicting the overall survival rates of cervical cancer patients at 1 year,3 years,and 5 years.Additionally,the sensitivity and specificity of this model in predicting 1-year,3-year,and5-year overall survival(OS)of cervical cancer patients had area under the curve(AUC)values of 0.809,0.695,and 0.709,respectively.Additionally,this model demonstrated the highest AUC value of 0.809 compared to other clinical features,indicating the highest accuracy in predicting the OS of cervical cancer patients.Conclusion:The predictive model and the nomogram constructed based on immunogenic cell death-related genes in this study can more accurately predict the survival outcomes of cervical cancer patients.Furthermore,both univariate and multivariate Cox regression analyses demonstrated that this risk model can serve as an independent prognostic indicator for predicting the prognosis of cervical cancer patients.In conclusion,our study on immunogenic cell death-related genes provides new insights into predicting the prognosis of other cancer types as well.更多还原