新型免疫相关基因标志物用于卵巢癌的风险分层和预后预测。

Novel immune-related gene signature for risk stratification and prognosis prediction in ovarian cancer.

机构信息

Department of Reproductive Genetics, International Peace Maternity and Child Health Hospital, Shanghai Key Laboratory of Embryo Original Diseases, Shanghai Municipal Key Clinical Specialty, Shanghai Jiao Tong University School of Medicine, No.910, Hengshan Road, Shanghai, 200030, China.

出版信息

J Ovarian Res. 2023 Oct 19;16(1):205. doi: 10.1186/s13048-023-01289-w.

DOI:10.1186/s13048-023-01289-w

PMID:37858138

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10585734/

Abstract

BACKGROUND

The immune system played a multifaceted role in ovarian cancer (OC) and was a significant mediator of ovarian carcinogenesis. Various immune cells and immune gene products played an integrated role in ovarian cancer (OC) progression, proved the significance of the immune microenvironment in prognosis. Therefore, we aimed to establish and validate an immune gene prognostic signature for OC patients' prognosis prediction.

METHODS

Differently expressed Immune-related genes (DEIRGs) were identified in 428 OC and 77 normal ovary tissue specimens from 9 independent GEO datasets. The Cancer Genome Atlas (TCGA) cohort was used as a training cohort, Univariate Cox analysis was used to identify prognostic DEIRGs in TCGA cohort. Then, an immune gene-based risk model for prognosis prediction was constructed using the LASSO regression analysis, and validated the accuracy and stability of the model in 374 and 93 OC patients in TCGA training cohort and International Cancer Genome Consortium (ICGC) validation cohort respectively. Finally, the correlation among risk score model, clinicopathological parameters, and immune cell infiltration were analyzed.

RESULTS

Five DEIRGs were identified to establish the immune gene signature and divided OC patients into the low- and high-risk groups. In TCGA and ICGC datasets, patients in the low-risk group showed a substantially higher survival rate than high-risk group. Receiver operating characteristic (ROC) curves, t-distributed stochastic neighbor embedding (t-SNE) analysis and principal component analysis (PCA) showed the good performance of the risk model. Clinicopathological correlation analysis proved the risk score model could serve as an independent prognostic factor in 2 independent datasets.

CONCLUSIONS

The prognostic model based on immune-related genes can function as a superior prognostic indicator for OC patients, which could provide evidence for individualized treatment and clinical decision making.

摘要

背景

免疫系统在卵巢癌（OC）中发挥着多方面的作用，是卵巢癌发生的重要介质。各种免疫细胞和免疫基因产物在卵巢癌（OC）进展中发挥着综合作用，证明了免疫微环境在预后中的重要性。因此，我们旨在建立和验证用于 OC 患者预后预测的免疫基因预后特征。

方法

从 9 个独立的 GEO 数据集的 428 个 OC 和 77 个正常卵巢组织标本中鉴定出差异表达的免疫相关基因（DEIRGs）。使用癌症基因组图谱（TCGA）队列作为训练队列，使用单变量 Cox 分析鉴定 TCGA 队列中与预后相关的 DEIRGs。然后，使用 LASSO 回归分析构建基于免疫基因的风险模型，用于预测预后，并分别在 TCGA 训练队列的 374 名和 93 名 OC 患者以及国际癌症基因组联盟（ICGC）验证队列的 93 名 OC 患者中验证模型的准确性和稳定性。最后，分析风险评分模型与临床病理参数和免疫细胞浸润之间的相关性。

结果

鉴定出 5 个 DEIRGs 建立免疫基因特征，并将 OC 患者分为低风险和高风险组。在 TCGA 和 ICGC 数据集中，低风险组患者的生存率明显高于高风险组。受试者工作特征（ROC）曲线、t 分布随机邻域嵌入（t-SNE）分析和主成分分析（PCA）显示了风险模型的良好性能。临床病理相关性分析证明，风险评分模型在 2 个独立的数据集中可作为独立的预后因素。