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一种用于预测乳腺癌预后、免疫格局和治疗反应的乳酸相关长链非编码RNA模型。

A lactate-related LncRNA model for predicting prognosis, immune landscape and therapeutic response in breast cancer.

作者信息

Li Jia, Zhang Yinbin, Li Chaofan, Wu Huizi, Feng Cong, Wang Weiwei, Liu Xuan, Zhang Yu, Cai Yifan, Jia Yiwei, Qiao Hao, Wu Fei, Zhang Shuqun

机构信息

Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

Department of Orthopedics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

出版信息

Front Genet. 2022 Oct 5;13:956246. doi: 10.3389/fgene.2022.956246. eCollection 2022.

Abstract

Breast cancer (BC) has the highest incidence rate of all cancers globally, with high heterogeneity. Increasing evidence shows that lactate and long non-coding RNA (lncRNA) play a critical role in tumor occurrence, maintenance, therapeutic response, and immune microenvironment. We aimed to construct a lactate-related lncRNAs prognostic signature (LRLPS) for BC patients to predict prognosis, tumor microenvironment, and treatment responses. The BC data download from the Cancer Genome Atlas (TCGA) database was the entire cohort, and it was randomly assigned to the training and test cohorts at a 1:1 ratio. Difference analysis and Pearson correlation analysis identified 196 differentially expressed lactate-related lncRNAs (LRLs). The univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were used to construct the LRLPS, which consisted of 7 LRLs. Patients could be assigned into high-risk and low-risk groups based on the medium-risk sore in the training cohort. Then, we performed the Kaplan-Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, and univariate and multivariate analyses. The results indicated that the prognosis prediction ability of the LRLPS was excellent, robust, and independent. Furthermore, a nomogram was constructed based on the LRLPS risk score and clinical factors to predict the 3-, 5-, and 10-year survival probability. The GO/KEGG and GSEA indicated that immune-related pathways differed between the two-risk group. CIBERSORT, ESTIMATE, Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore (IPS) showed that low-risk patients had higher levels of immune infiltration and better immunotherapeutic response. The pRRophetic and CellMiner databases indicated that many common chemotherapeutic drugs were more effective for low-risk patients. In conclusion, we developed a novel LRLPS for BC that could predict the prognosis, immune landscape, and treatment response.

摘要

乳腺癌(BC)是全球所有癌症中发病率最高的,具有高度异质性。越来越多的证据表明,乳酸和长链非编码RNA(lncRNA)在肿瘤发生、维持、治疗反应和免疫微环境中起着关键作用。我们旨在构建一个用于BC患者的乳酸相关lncRNA预后特征(LRLPS),以预测预后、肿瘤微环境和治疗反应。从癌症基因组图谱(TCGA)数据库下载的BC数据作为整个队列,并以1:1的比例随机分配到训练队列和测试队列。差异分析和Pearson相关分析确定了196个差异表达的乳酸相关lncRNA(LRL)。使用单变量Cox回归分析、最小绝对收缩和选择算子(LASSO)以及多变量Cox回归分析构建LRLPS,其由7个LRL组成。根据训练队列中的中位风险评分,患者可分为高风险组和低风险组。然后,我们进行了Kaplan-Meier生存分析、时间依赖性受试者工作特征(ROC)曲线以及单变量和多变量分析。结果表明,LRLPS的预后预测能力优异、稳健且独立。此外,基于LRLPS风险评分和临床因素构建了列线图,以预测3年、5年和10年生存概率。GO/KEGG和GSEA表明,两个风险组之间免疫相关途径存在差异。CIBERSORT、ESTIMATE、肿瘤免疫功能障碍与排除(TIDE)和免疫表型评分(IPS)显示,低风险患者的免疫浸润水平更高,免疫治疗反应更好。pRRophetic和CellMiner数据库表明,许多常用化疗药物对低风险患者更有效。总之,我们为BC开发了一种新型LRLPS,它可以预测预后、免疫格局和治疗反应。

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