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基于肿瘤免疫的 7 个长链非编码 RNA 预后模型的建立与验证及其在卵巢癌患者中的应用

Development and verification of a 7-lncRNA prognostic model based on tumor immunity for patients with ovarian cancer.

机构信息

Gynecology Department 2, Cangzhou Central Hospital, No. 16, Xinhua West Road, Yunhe District, Cangzhou, Hebei Province, 061000, China.

出版信息

J Ovarian Res. 2023 Feb 4;16(1):31. doi: 10.1186/s13048-023-01099-0.

Abstract

BACKGROUND

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.

METHOD

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.

RESULTS

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.

CONCLUSIONS

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.

摘要

背景

免疫反应和长链非编码 RNA(lncRNA)都在卵巢癌(OC)的增殖、侵袭和转移中发挥重要作用。本研究旨在构建 OC 患者的免疫相关 lncRNA 风险模型。

方法

使用单样本基因集富集分析(ssGSEA)算法分析癌症基因组图谱(TCGA)中免疫细胞的比例,并根据免疫细胞的比例使用 hclust 算法对 OC 患者进行免疫分型。使用 ESTIMATE 算法计算基质和免疫评分。利用加权基因共表达网络分析(WGCNA)和差异表达基因(DEGs)分析检测免疫聚类相关 lncRNA。使用最小绝对值收缩和选择算子(LASSO)回归进行 lncRNA 选择。选择的 lncRNA 用于构建预后相关的风险模型,并在基因表达综合数据库(GEO)和体外验证中进行验证。

结果

我们根据 ssGSEA 分析确定了两个亚型,高免疫聚类(immunity_H)和低免疫聚类(immunity_L)。immunity_H 聚类的患者比例明显高于 immunity_L 聚类。immunity_H 组的 ESTIMATE 相关评分相对较高。通过 WGCNA 和 LASSO 分析,我们确定了 141 个与免疫聚类相关的 lncRNA,并发现这些基因主要富集在自噬中。一个由 7 个 lncRNA 组成的特征,包括 AL391832.3、LINC00892、LINC02207、LINC02416、PSMB8.AS1、AC078788.1 和 AC104971.3,被选为将患者分为高风险和低风险组的依据。该特征的生存分析和 ROC 曲线下面积(AUC)表明,该风险模型在预测 OC 患者的预后方面具有很高的准确性。我们还进行了药物敏感性预测,发现雷帕霉素在高风险评分患者中表现更优。体外实验也证实了我们的预测。

结论

我们确定了 7 个与免疫相关的预后 lncRNA,它们可以有效地预测 OC 患者的生存情况。这些发现可能为 OC 患者的临床分层管理和个性化治疗选择提供有价值的指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b42/9898952/05a0c55ee528/13048_2023_1099_Fig1_HTML.jpg

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