免疫反应和长链非编码RNA都在卵巢癌（OC）的增殖、侵袭和转移中发挥重要作用。本研究旨在为OC患者构建一个免疫相关的lncRNA风险模型。使用单样本GSEA（ssGSEA）算法分析癌症基因组图谱（TCGA）中免疫细胞的比例，使用hclust算法根据免疫细胞的比例进行免疫分类。应用ESTIMATE算法计算出基质和免疫得分。使用加权基因共表达网络分析（WGCNA）和差异表达基因（DEGs）分析检测免疫簇相关的lncRNA。进行最小绝对收缩和选择算子（LASSO）回归进行lncRNA选择。所选的lncRNA被用于构建与预后相关的风险模型，并在基因表达混杂物数据库和体外验证中进行验证。通过ssGSEA分析，我们鉴定出两个亚型，高免疫簇（immunity_H）和低免疫簇（immunity_L）。在高免疫簇（immunity_H）的病人比例显著高于低免疫簇（immunity_L）的病人比例。ESTIMATE相关分数在高免疫组中相对较高。通过WGCNA和LASSO分析，我们鉴定出141个免疫簇相关lncRNA，并发现这些基因主要富集在自噬中。包括AL391832.3、LINC00892、LINC02207、LINC02416、PSMB8.AS1、AC078788.1和AC104971.3在内的由7个lncRNA组成的标志物被选择作为将患者分为高和低风险组的基础。生存分析和ROC曲线下面积（AUC）表明，该风险模型在预测OC患者预后方面具有很高的准确性。我们还进行了药物敏感预测，并发现雷帕霉素在高风险评分患者中效果更佳。体外实验也证实了我们的预测。我们发现了7个免疫相关的预后性lncRNA，有效地预测了OC患者的生存。这些发现可能为这些患者的临床分层管理和个性化治疗提供有价值的指标。©2023年作者。

Both immune-reaction and lncRNAs play significant roles in the proliferation, invasion, and metastasis of ovarian cancer (OC). In this study, we aimed to construct an immune-related lncRNA risk model for patients with OC.Single sample GSEA (ssGSEA) algorithm was used to analyze the proportion of immune cells in The Cancer Genome Atlas (TCGA) and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells for OC patients. The stromal and immune scores were computed utilizing the ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) analyses were utilized to detect immune cluster-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) regression was conducted for lncRNA selection. The selected lncRNAs were used to construct a prognosis-related risk model, which was then validated in Gene Expression Omnibus (GEO) database and in vitro validation.We identify two subtypes based on the ssGSEA analysis, high immunity cluster (immunity_H) and low immunity cluster (immunity_L). The proportion of patients in immunity_H cluster was significantly higher than that in immunity_L cluster. The ESTIMATE related scores are relative high in immunity_H group. Through WGCNA and LASSO analyses, we identified 141 immune cluster-related lncRNAs and found that these genes were mainly enriched in autophagy. A signature consisting of 7 lncRNAs, including AL391832.3, LINC00892, LINC02207, LINC02416, PSMB8.AS1, AC078788.1 and AC104971.3, were selected as the basis for classifying patients into high- and low-risk groups. Survival analysis and area under the ROC curve (AUC) of the signature pointed out that this risk model had high accuracy in predicting the prognosis of patients with OC. We also conducted the drug sensitive prediction and found that rapamycin outperformed in patient with high risk score. In vitro experiments also confirmed our prediction.We identified 7 immune-related prognostic lncRNAs that effectively predicted survival in OC patients. These findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for these patients.© 2023. The Author(s).