卵巢癌免疫相关基因预后模型的构建及验证

Construction and Validation of an Immune-Related Gene Prognostic Model for Ovarian Cancer

摘要: 目的：卵巢癌是女性生殖系统病死率最高的恶性肿瘤，早期症状隐匿、易复发耐药，单纯TNM分期难以满足精准预后评估需求。免疫相关基因与肿瘤微环境在卵巢癌进展、耐药及预后中发挥关键调控作用，可作为新型预后生物标志物。本研究基于TCGA、GTEx与ImmPort数据库，系统筛选卵巢癌免疫相关差异表达基因，构建并验证免疫基因预后模型，为临床预后分层与个体化治疗提供依据，同时初步筛选具有潜力的预后标志物。方法：从TCGA和GTEx数据集下载卵巢癌与正常卵巢组织转录组及临床数据，经数据质控后剔除临床信息不完整的样本、非原发性肿瘤及总生存期 ≤ 60天的病例，筛选差异表达免疫相关基因；对差异基因进行GO功能与KEGG通路富集分析；采用单因素Cox、LASSO与多因素Cox回归构建风险评分模型。在UCSC外部队列独立验证，以风险评分中位值为界划分高低风险组，通过生存曲线与ROC曲线分析评估模型效能。结果：共筛选出220个差异表达免疫相关基因，主要富集于中性粒细胞趋化、细胞因子–受体相互作用等通路。最终构建包含RELB、PI3等19个基因的预后模型，高风险组总生存期显著低于低风险组(P < 0.0001)，模型1、3、5年AUC分别为0.602、0.710、0.731；风险评分可作为独立预后因子，RELB与PI3高表达提示预后不良，外部验证集生存曲线与ROC曲线显示高低风险组生存差异显著，证实模型较为稳定可靠。结论：本研究构建的卵巢癌免疫相关基因预后模型预测效能良好、稳定性相对较强，可在一定程度上实现患者风险分层，为卵巢癌预后评估、免疫靶点挖掘与精准治疗提供新的参考依据。

Abstract: Objective: Ovarian cancer stands as the malignancy with the highest mortality rate among female reproductive system cancers, characterized by insidious early symptoms, a propensity for recurrence and drug resistance. Simple Tumor-Node-Metastasis (TNM) staging falls short of meeting the demands for precise prognostic evaluation. Immune-related genes and the tumor microenvironment play pivotal regulatory roles in the progression, drug resistance, and prognosis of ovarian cancer, serving as potential novel prognostic biomarkers. This study systematically screened immune-related differentially expressed genes in ovarian cancer based on data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and ImmPort databases. It aimed to construct and validate an immune gene prognostic model to provide a basis for clinical prognostic stratification and individualized treatment, while also preliminarily identifying promising prognostic markers. Methods: Transcriptomic and clinical data of ovarian cancer and normal ovarian tissues were downloaded from the TCGA and GTEx datasets. After data quality control, samples with incomplete clinical information, non-primary tumors, and cases with an overall survival period of ≤60 days were excluded. Differentially expressed immune-related genes were then identified. Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the differentially expressed genes. A risk score model was constructed using univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses. The model was independently validated in an external cohort from the University of California, Santa Cruz (UCSC). High- and low-risk groups were delineated based on the median value of the risk score, and the model’s performance was evaluated through survival curve and ROC curve analyses. Results: A total of 220 differentially expressed immune-related genes were identified, primarily enriched in pathways such as neutrophil chemotaxis and cytokine-receptor interactions. A prognostic model comprising 19 genes, including RELB and PI3, was ultimately constructed. The overall survival of the high-risk group was significantly lower than that of the low-risk group (P < 0.0001). The area under the curve (AUC) values for 1-year, 3-year, and 5-year survival predictions were 0.602, 0.710, and 0.731, respectively. The risk score emerged as an independent prognostic factor, with high expression of RELB and PI3 indicating a poor prognosis. Survival curves and ROC curves in the external validation set demonstrated significant survival differences between the high- and low-risk groups, confirming the model’s relatively stable and reliable performance. Conclusion: The immune-related gene prognostic model for ovarian cancer constructed in this study exhibits good predictive performance and relatively high stability. To a certain extent, it enables the stratification of patients based on risk levels, providing new reference insights for prognostic evaluation, immune target discovery, and precision treatment of ovarian cancer.