使用紫杉醇耐药免疫相关 lncRNAs 预测卵巢癌患者的治疗反应模型。

Prediction Model for Therapeutic Responses in Ovarian Cancer Patients using Paclitaxel-resistant Immune-related lncRNAs.

机构信息

Research Center for Clinical Medicine, Jinshan Hospital of Fudan University, Shanghai, 201508, China.

Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.

出版信息

Curr Med Chem. 2024;31(26):4213-4231. doi: 10.2174/0109298673281438231217151129.

DOI:10.2174/0109298673281438231217151129

PMID:38357948

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11340295/

Abstract

BACKGROUND

Ovarian cancer (OC) is the deadliest malignant tumor in women with a poor prognosis due to drug resistance and lack of prediction tools for therapeutic responses to anti- cancer drugs.

OBJECTIVE

The objective of this study was to launch a prediction model for therapeutic responses in OC patients.

METHODS

The RNA-seq technique was used to identify differentially expressed paclitaxel (PTX)- resistant lncRNAs (DE-lncRNAs). The Cancer Genome Atlas (TCGA)-OV and ImmPort database were used to obtain immune-related lncRNAs (ir-lncRNAs). Univariate, multivariate, and LASSO Cox regression analyses were performed to construct the prediction model. Kaplan- meier plotter, Principal Component Analysis (PCA), nomogram, immune function analysis, and therapeutic response were applied with Genomics of Drug Sensitivity in Cancer (GDSC), CIBERSORT, and TCGA databases. The biological functions were evaluated in the CCLE database and OC cells.

RESULTS

The RNA-seq defined 186 DE-lncRNAs between PTX-resistant A2780-PTX and PTXsensitive A2780 cells. Through the analysis of the TCGA-OV database, 225 ir-lncRNAs were identified. Analyzing 186 DE-lncRNAs and 225 ir-lncRNAs using univariate, multivariate, and LASSO Cox regression analyses, 9 PTX-resistant immune-related lncRNAs (DEir-lncRNAs) acted as biomarkers were discovered as potential biomarkers in the prediction model. Single-cell RNA sequencing (scRNA-seq) data of OC confirmed the relevance of DEir-lncRNAs in immune responsiveness. Patients with a low prediction score had a promising prognosis, whereas patients with a high prediction score were more prone to evade immunotherapy and chemotherapy and had poor prognosis.

CONCLUSION

The novel prediction model with 9 DEir-lncRNAs is a valuable tool for predicting immunotherapeutic and chemotherapeutic responses and prognosis of patients with OC.

摘要

背景

卵巢癌（OC）是女性中最致命的恶性肿瘤，由于耐药性和缺乏预测抗癌药物治疗反应的工具，预后较差。

目的

本研究旨在建立 OC 患者治疗反应的预测模型。

方法

采用 RNA-seq 技术鉴定紫杉醇（PTX）耐药差异表达长链非编码 RNA（DE-lncRNAs）。使用癌症基因组图谱（TCGA）-OV 和 ImmPort 数据库获得免疫相关长链非编码 RNA（ir-lncRNAs）。采用单因素、多因素和 LASSO Cox 回归分析构建预测模型。Kaplan-Meier 绘图仪、主成分分析（PCA）、列线图、免疫功能分析和治疗反应分析应用于癌症药物敏感性基因组学（GDSC）、CIBERSORT 和 TCGA 数据库。在 CCLE 数据库和 OC 细胞中评估生物学功能。

结果

RNA-seq 定义了 PTX 耐药 A2780-PTX 和 PTX 敏感 A2780 细胞之间的 186 个 DE-lncRNAs。通过 TCGA-OV 数据库分析，鉴定了 225 个 ir-lncRNAs。通过单因素、多因素和 LASSO Cox 回归分析对 186 个 DE-lncRNAs 和 225 个 ir-lncRNAs 进行分析，发现 9 个 PTX 耐药免疫相关 lncRNAs（DEir-lncRNAs）作为生物标志物，可作为预测模型中的潜在生物标志物。OC 的单细胞 RNA 测序（scRNA-seq）数据证实了 DEir-lncRNAs 与免疫反应性的相关性。预测评分低的患者预后较好，而预测评分高的患者更倾向于逃避免疫治疗和化疗，预后较差。