Construction of a MicroRNA-Based Nomogram for Prediction of Lung Metastasis in Breast Cancer Patients.
Construction of a MicroRNA-Based Nomogram for Prediction of Lung Metastasis in Breast Cancer Patients.
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构建基于 MicroRNA 的列线图来预测乳腺癌患者的肺转移
DOI：
10.3389/fgene.2020.580138
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发表时间：
2020
影响因子：
3.7
通讯作者：
Huang J
中科院分区：
文献类型：
--
作者：
Zhang L;Pan J;Wang Z;Yang C;Huang J
肺是乳腺癌(BC)最常见的远处转移部位之一。识别理想的生物标志物以构建比常规临床参数更准确的预测模型至关重要。MicroRNAs(MiRNAs)数据和临床病理数据来自乳腺癌分子分类国际联合会(METABRIC)数据库。MIR-663、miR-210、miR-17、miR-301a、miR-135b、miR-451、miR-30a和miR-199a-5p与BC患者的肺转移密切相关。基于miRNA的风险评分是基于个体miRNA的Logistic系数而开发的。单变量和多变量Logistic回归选择肿瘤转移(TNM)分期、确诊时年龄和miRNA风险评分作为独立预测参数，并用它们构建诺模图。使用癌症基因组图谱(TCGA)数据库验证签名和诺模图。将诺模图的预测性能与TNM分期进行比较。在三个队列中，诺模图的受试者操作特征曲线下面积均高于TNM阶段(训练队列：0.774比0.727；内部验证队列：0.763比0.583；外部验证队列：0.925比0.840)。诺模图的校准图显示，预测结果与观测结果吻合良好。诺模图的净重分类改进(NRI)、综合判别改进(IDI)和决策曲线分析(DCA)显示其性能优于TNM分类系统。功能浓缩分析提出了几个术语，特别侧重于LM。亚群分析显示miR-30a、miR-135b和miR-17在BC的肺转移中具有独特的作用。泛癌分析表明，8个预测miRNAs在肺转移中具有重要意义。本研究首次建立和验证了基于METABRIC和TCGA数据库的全面肺转移预测诺模图，为临床医生提供了可靠的评估工具，有助于选择合适的治疗方法。
The lung is one of the most common sites of distant metastasis in breast cancer (BC). Identifying ideal biomarkers to construct a more accurate prediction model than conventional clinical parameters is crucial. MicroRNAs (miRNAs) data and clinicopathological data were acquired from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database. miR-663, miR-210, miR-17, miR-301a, miR-135b, miR-451, miR-30a, and miR-199a-5p were screened to be highly relevant to lung metastasis (LM) of BC patients. The miRNA-based risk score was developed based on the logistic coefficient of the individual miRNA. Univariate and multivariate logistic regression selected tumor node metastasis (TNM) stage, age at diagnosis, and miRNA-risk score as independent predictive parameters, which were used to construct a nomogram. The Cancer Genome Atlas (TCGA) database was used to validate the signature and nomogram. The predictive performance of the nomogram was compared to that of the TNM stage. The area under the receiver operating characteristics curve (AUC) of the nomogram was higher than that of the TNM stage in all three cohorts (training cohort: 0.774 vs. 0.727; internal validation cohort: 0.763 vs. 0.583; external validation cohort: 0.925 vs. 0.840). The calibration plot of the nomogram showed good agreement between predicted and observed outcomes. The net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA) of the nomogram showed that its performances were better than that of the TNM classification system. Functional enrichment analyses suggested several terms with a specific focus on LM. Subgroup analysis showed that miR-30a, miR-135b, and miR-17 have unique roles in lung metastasis of BC. Pan-cancer analysis indicated the significant importance of eight predictive miRNAs in lung metastasis. This study is the first to establish and validate a comprehensive lung metastasis predictive nomogram based on the METABRIC and TCGA databases, which provides a reliable assessment tool for clinicians and aids in appropriate treatment selection.
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癌症中的microRNA生物发生途径。
DOI：
10.1038/nrc3932
发表时间：
2015-06
期刊：
Nature reviews. Cancer
影响因子：
--
作者：
Lin S;Gregory RI
通讯作者：
Gregory RI
影响因子：
46.9
作者：
Ma L;Reinhardt F;Pan E;Soutschek J;Bhat B;Marcusson EG;Teruya-Feldstein J;Bell GW;Weinberg RA
通讯作者：
Weinberg RA
影响因子：
64.8
作者：
Curtis, Christina;Shah, Sohrab P.;Chin, Suet-Feung;Turashvili, Gulisa;Rueda, Oscar M.;Dunning, Mark J.;Speed, Doug;Lynch, Andy G.;Samarajiwa, Shamith;Yuan, Yinyin;Graef, Stefan;Ha, Gavin;Haffari, Gholamreza;Bashashati, Ali;Russell, Roslin;McKinney, Steven;Langerod, Anita;Green, Andrew;Provenzano, Elena;Wishart, Gordon;Pinder, Sarah;Watson, Peter;Markowetz, Florian;Murphy, Leigh;Ellis, Ian;Purushotham, Arnie;Borresen-Dale, Anne-Lise;Brenton, James D.;Tavare, Simon;Caldas, Carlos;Aparicio, Samuel
通讯作者：
Aparicio, Samuel
影响因子：
3.7
作者：
Becker PM;Tran TS;Delannoy MJ;He C;Shannon JM;McGrath-Morrow S
通讯作者：
McGrath-Morrow S
影响因子：
64.5
作者：
Lambert AW;Pattabiraman DR;Weinberg RA
通讯作者：
Weinberg RA