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血管生成相关长链非编码RNA指数:一种用于宫颈癌预后、免疫治疗疗效和化疗敏感性的预测指标。

Angiogenesis-related lncRNAs index: A predictor for CESC prognosis, immunotherapy efficacy, and chemosensitivity.

作者信息

Huang Xueyuan, Yi Guangming, Xu Jiayu, Gou Siqi, Chen Haiqing, Chen Xiaoyan, Quan Xiaomin, Xie Linjia, Teichmann Alexander Tobias, Yang Guanhu, Chi Hao, Wang Qin

机构信息

Clinical Medical College, Southwest Medical University, Luzhou 646000, China.

Department of Oncology, The Third Hospital of Mianyang (Sichuan Mental Health Center), Mianyang, Sichuan 621000, China.

出版信息

J Cancer. 2024 Apr 8;15(10):3095-3113. doi: 10.7150/jca.94332. eCollection 2024.

Abstract

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is a common gynecologic tumor and patients with advanced and recurrent disease usually have a poor clinical outcome. Angiogenesis is involved in the biological processes of tumors and can promote tumor growth and invasion. In this paper, we created a signature for predicting prognosis based on angiogenesis-related lncRNAs (ARLs). This provides a prospective direction for enhancing the efficacy of immunotherapy in CESC patients. We screened seven OS-related ARLs by univariate and multivariate regression analyses and Lasso analysis and developed a prognostic signature at the same time. Then, we performed an internal validation in the TCGA-CESC cohort to increase the precision of the study. In addition, we performed a series of analyses based on ARLs, including immune cell infiltration, immune function, immune checkpoint, tumor mutation load, and drug sensitivity analysis. Our created signature based on ARLs can effectively predict the prognosis of CESC patients. To strengthen the prediction accuracy of the signature, we built a nomogram by combining signature and clinical features.

摘要

宫颈鳞状细胞癌和宫颈管腺癌(CESC)是一种常见的妇科肿瘤,晚期和复发性疾病患者的临床预后通常较差。血管生成参与肿瘤的生物学过程,可促进肿瘤生长和侵袭。在本文中,我们基于血管生成相关长链非编码RNA(ARL)创建了一个预测预后的特征模型。这为提高CESC患者免疫治疗的疗效提供了一个前瞻性方向。我们通过单变量和多变量回归分析以及Lasso分析筛选出7个与总生存期(OS)相关的ARL,并同时开发了一个预后特征模型。然后,我们在TCGA-CESC队列中进行了内部验证,以提高研究的准确性。此外,我们基于ARL进行了一系列分析,包括免疫细胞浸润、免疫功能、免疫检查点、肿瘤突变负荷和药物敏感性分析。我们创建的基于ARL的特征模型可以有效地预测CESC患者的预后。为了提高特征模型的预测准确性,我们通过结合特征模型和临床特征构建了一个列线图。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5f7/11064265/e65776745001/jcav15p3095g001.jpg

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