基于TCGA和ImmPort的上皮性卵巢癌免疫相关LncRNAs预后不良的预测方案研究

doi:10.3760/cma.j.cn441417-20250324-21014

摘要/Abstract

摘要： 目的　基于癌症基因组图谱（TCGA）数据库和免疫学数据库和分析门户（ImmPort）构建上皮性卵巢癌免疫相关长链非编码核糖核酸（LncRNAs）预后不良的预测方案。方法　从TCGA数据库中下载上皮性卵巢癌和癌旁正常组织LncRNAs数据和患者临床资料，并结合ImmPort下载的免疫相关LncRNAs数据筛选差异表达免疫相关LncRNAs（截止至2024年5月31日）。采用单因素和多因素Cox回归分析筛选与上皮性卵巢癌预后不良独立相关的免疫相关LncRNAs，构建风险评分模型，并通过Kaplan-Meier生存分析曲线、受试者操作特征曲线（ROC）分析风险评分模型的预测价值。结果　从TCGA数据库中共下载208例上皮性卵巢癌组织和104例癌旁正常组织的LncRNAs数据，筛选出128个差异表达免疫相关LncRNAs。单因素Cox回归分析共筛选出11个与上皮性卵巢癌预后相关的免疫相关LncRNAs（均P<0.05）；进一步进行多因素Cox回归分析，得出5个独立影响上皮性卵巢癌预后的免疫相关LncRNAs（MALAT1、LINC00958、AC011445.1、PRKAR1B-AS2、SNHG17）（均P<0.05）。基于多因素Cox回归分析结果构建上皮性卵巢癌免疫相关LncRNAs预后预测风险评分模型，风险评分=0.592×MALAT1 + 0.491×LINC00958 + 0.413×AC011445.1 + 0.641×PRKAR1B-AS2 + 0.309×SNHG17。Kaplan-Meier生存分析曲线显示，高风险组（风险评分≥中位数）与低风险组（风险评分<中位数）相比，生存率较低（Log-rank检验：χ²=13.784，P<0.001）。ROC分析显示，风险评分模型预测1年、3年、5年预后不良的曲线下面积分别为0.815（95%CI：0.754～0.867）、0.735（95%CI：0.667～0.796）、0.691（95%CI：0.621～0.755）。结论　基于上述5个上皮性卵巢癌预后相关的免疫相关LncRNAs构建的风险评分模型能够预测预后情况，也为上皮性卵巢癌提供潜在免疫治疗靶点。

Abstract: Objective　To construct a prediction scheme for poor prognosis of epithelial ovarian cancer-related immune-related long non-coding ribonucleic acids (LncRNAs) based on the Cancer Genome Atlas (TCGA) database and the Immunology Database and Analysis Portal (ImmPort). Methods　LncRNAs data of epithelial ovarian cancer and adjacent normal tissues, as well as patients' clinical data, were downloaded from the TCGA database; differentially expressed immune-related LncRNAs were screened by combining immune related LncRNA data downloaded from ImmPort (as of May 31, 2024). Immune related LncRNAs independently associated with poor prognosis of epithelial ovarian cancer were screened using univariate and multivariate Cox regression analysis, and a risk scoring model was constructed. The predictive value of the risk scoring model was analyzed using the Kaplan-Meier survival analysis curve and receiver operating characteristic curve (ROC). Results　A total of 208 cases of epithelial ovarian cancer tissues and 104 cases of normal tissues adjacent to the cancer were downloaded from the TCGA database, and 128 differentially expressed immune-related LncRNAs were screened out. Univariate Cox regression analysis identified 11 immune-related LncRNAs that were associated with the prognosis of epithelial ovarian cancer (all P<0.05). Multivariate Cox regression analysis revealed 5 immune-related LncRNAs (MALAT1, LINC00958, AC011445.1, PRKAR1B-AS2, and SNHG17) that independently affected the prognosis of epithelial ovarian cancer (all P<0.05). Based on the results of multivariate Cox regression analysis, an immune-related LncRNA prognostic risk scoring model for epithelial ovarian cancer was constructed: the risk score = 0.592 × MALAT1 + 0.491 × LINC00958 + 0.413 × AC011445.1 + 0.641 × PRKAR1B-AS2 + 0.309 × SNHG17. The Kaplan-Meier survival analysis curve showed that the survival rate of the high-risk group (risk score ≥ median) was lower than that of the low-risk group (risk score < median) (Log-rank test: χ²=13.784, P<0.001). The ROC analysis showed that the area under the curve for predicting poor prognosis at 1 year, 3 years, and 5 years by the risk scoring model was 0.815 (95%CI: 0.754 - 0.867), 0.735 (95%CI: 0.667 - 0.796), and 0.691 (95%CI: 0.621 - 0.755), respectively. Conclusion　A risk scoring model based on above-mentioned 5 immune-related LncRNAs related to the prognosis of epithelial ovarian cancer can predict the prognosis and provide potential immunotherapy targets for epithelial ovarian cancer.

Key words: Epithelial ovarian cancer, Immune related genes, Long non-coding ribonucleic acids, Poor prognosis, Cancer Genome Atlas Database, Immunology Database and Analysis Portal

颜雪刘媛媛赵刚. 基于TCGA和ImmPort的上皮性卵巢癌免疫相关LncRNAs预后不良的预测方案研究[J]. 国际医药卫生导报, 2025, 31(21): 3592-3596.

Yan Xue, Liu Yuanyuan, Zhao Gang. Study on the prediction scheme for poor prognosis of epithelial ovarian cancer-related immune-related LncRNAs based on TCGA and ImmPort[J]. International Medicine and Health Guidance News, 2025, 31(21): 3592-3596.

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