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卵巢癌患者中坏死性凋亡相关长链非编码RNA特征的推导与验证

Derivation and Validation of a Necroptosis-Related lncRNA Signature in Patients with Ovarian Cancer.

作者信息

Zhu Linling, He Jiaoyan, Yang Xinyun, Zheng Jianfeng, Liu Wenhua, Chen Hao

机构信息

Department of Pathology, Hangzhou Women's Hospital, Hangzhou, China.

Department of Gynecology, Hangzhou Women's Hospital, Hangzhou, China.

出版信息

J Oncol. 2022 May 23;2022:6228846. doi: 10.1155/2022/6228846. eCollection 2022.

Abstract

BACKGROUND

Ovarian cancer (OC) is the leading cause of gynecologic malignant tumors. The role of necroptosis-related lncRNAs (NRLs) in OC remains unclear. This study aims to explore the association between NRLs and prognosis in OC patients.

METHODS

The Cancer Genome Atlas (TCGA) and GTEx datasets were used to obtain OC's data. A NRLs signature associated with overall survival (OS) was constructed by Cox-LASSO regression analysis in training cohort for calculating risk score and then validated in testing cohort. Subsequently, the area under the curve (AUC) and Kaplan-Meier survival analysis were used to evaluate the predictive accuracy of the risk score. Finally, the immune infiltration and functional enrichment were compared between different risk groups.

RESULTS

A 8-NRLs signature including AC245128.3, AL355488.1, AC092794.1, AC068888.2, AL590652.1, AC008982.2, FOXP4-AS1, and Z94721.1 was identified to assess the OS of OC. Kaplan-Meier survival analysis, AUC value, and Cox regression analysis confirmed its predictive value and showed that the clinical outcomes were worse for high-risk patients. There were also differences in immunological functioning and immune pathways between the high-risk and low-risk groups.

CONCLUSIONS

The signature based on eight NRLs has significant values in predicting prognostic prediction in OC, as well as providing a new sight for targeted therapies.

摘要

背景

卵巢癌(OC)是妇科恶性肿瘤的主要病因。坏死性凋亡相关长链非编码RNA(NRLs)在OC中的作用尚不清楚。本研究旨在探讨NRLs与OC患者预后之间的关联。

方法

使用癌症基因组图谱(TCGA)和GTEx数据集获取OC的数据。通过Cox-LASSO回归分析在训练队列中构建与总生存期(OS)相关的NRLs特征,以计算风险评分,然后在测试队列中进行验证。随后,使用曲线下面积(AUC)和Kaplan-Meier生存分析来评估风险评分的预测准确性。最后,比较不同风险组之间的免疫浸润和功能富集情况。

结果

鉴定出一个包含AC245128.3、AL355488.1、AC092794.1、AC068888.2、AL590652.1、AC008982.2、FOXP4-AS1和Z94721.1的8-NRLs特征,用于评估OC的OS。Kaplan-Meier生存分析、AUC值和Cox回归分析证实了其预测价值,并表明高危患者的临床结局更差。高危组和低危组之间在免疫功能和免疫途径方面也存在差异。

结论

基于八个NRLs的特征在预测OC的预后方面具有重要价值,同时为靶向治疗提供了新的视角。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3ff/9152429/47ced2af72f5/JO2022-6228846.001.jpg

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