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用于验证和揭示子宫内膜癌预后生物标志物新应用的计算机模拟方法

In silico Approach for Validating and Unveiling New Applications for Prognostic Biomarkers of Endometrial Cancer.

作者信息

Coll-de la Rubia Eva, Martinez-Garcia Elena, Dittmar Gunnar, Nazarov Petr V, Bebia Vicente, Cabrera Silvia, Gil-Moreno Antonio, Colás Eva

机构信息

Biomedical Research Group in Gynecology, Vall Hebron Institute of Research, Universitat Autònoma de Barcelona, CIBERONC, 08035 Barcelona, Spain.

Luxembourg Institute of Health, L-1445 Strassen, Luxembourg.

出版信息

Cancers (Basel). 2021 Oct 9;13(20):5052. doi: 10.3390/cancers13205052.

DOI:10.3390/cancers13205052
PMID:34680205
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8534093/
Abstract

Endometrial cancer (EC) mortality is directly associated with the presence of prognostic factors. Current stratification systems are not accurate enough to predict the outcome of patients. Therefore, identifying more accurate prognostic EC biomarkers is crucial. We aimed to validate 255 prognostic biomarkers identified in multiple studies and explore their prognostic application by analyzing them in TCGA and CPTAC datasets. We analyzed the mRNA and proteomic expression data to assess the statistical prognostic performance of the 255 proteins. Significant biomarkers related to overall survival (OS) and recurrence-free survival (RFS) were combined and signatures generated. A total of 30 biomarkers were associated either to one or more of the following prognostic factors: histological type ( = 15), histological grade ( = 6), FIGO stage ( = 1), molecular classification ( = 16), or they were associated to OS ( = 11), and RFS ( = 5). A prognostic signature composed of 11 proteins increased the accuracy to predict OS (AUC = 0.827). The study validates and identifies new potential applications of 30 proteins as prognostic biomarkers and suggests to further study under-studied biomarkers such as TPX2, and confirms already used biomarkers such as MSH6, MSH2, or L1CAM. These results are expected to advance the quest for biomarkers to accurately assess the risk of EC patients.

摘要

子宫内膜癌（EC）死亡率与预后因素直接相关。当前的分层系统在预测患者预后方面不够准确。因此，识别更准确的EC预后生物标志物至关重要。我们旨在验证在多项研究中确定的255种预后生物标志物，并通过在TCGA和CPTAC数据集中对它们进行分析来探索其预后应用。我们分析了mRNA和蛋白质组学表达数据，以评估这255种蛋白质的统计学预后性能。将与总生存期（OS）和无复发生存期（RFS）相关的显著生物标志物进行合并并生成特征。共有30种生物标志物与以下一种或多种预后因素相关：组织学类型（ = 15）、组织学分级（ = 6）、国际妇产科联盟（FIGO）分期（ = 1）、分子分类（ = 16），或者它们与OS（ = 11）和RFS（ = 5）相关。由11种蛋白质组成的预后特征提高了预测OS的准确性（曲线下面积[AUC] = 0.827）。该研究验证并确定了30种蛋白质作为预后生物标志物的新潜在应用，并建议进一步研究研究较少的生物标志物，如TPX2，并确认已使用的生物标志物，如MSH6、MSH2或L1CAM。这些结果有望推动对生物标志物的探索，以准确评估EC患者的风险。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a642/8534093/07c8aa940644/cancers-13-05052-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a642/8534093/0a1aa29300f1/cancers-13-05052-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a642/8534093/43d1eda1b5da/cancers-13-05052-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a642/8534093/7aabaabf97c2/cancers-13-05052-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a642/8534093/3c5c26c96439/cancers-13-05052-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a642/8534093/34389ac9c68e/cancers-13-05052-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a642/8534093/07c8aa940644/cancers-13-05052-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a642/8534093/0a1aa29300f1/cancers-13-05052-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a642/8534093/43d1eda1b5da/cancers-13-05052-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a642/8534093/7aabaabf97c2/cancers-13-05052-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a642/8534093/3c5c26c96439/cancers-13-05052-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a642/8534093/34389ac9c68e/cancers-13-05052-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a642/8534093/07c8aa940644/cancers-13-05052-g006.jpg

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