A Diagnostic Model Using Exosomal Genes for Colorectal Cancer.

A Diagnostic Model Using Exosomal Genes for Colorectal Cancer.
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使用外泌体基因诊断结直肠癌的模型

DOI:
10.3389/fgene.2022.863747
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发表时间:
2022
影响因子:
3.7
通讯作者:
--
中科院分区:
生物学3区
文献类型:
--
作者:

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结直肠癌(CRC)是全世界癌症相关死亡的主要原因。外来体由于其在体液中的存在和稳定性而具有作为液体活检标本的巨大潜力。然而,外泌体基因在结直肠癌中的功能和诊断价值知之甚少。在本研究中,使用exoRBase 2.0和基因表达Omnibus(GEO)数据库中CRC和健康样本的外泌体数据,并鉴定了38个共同的外泌体基因。通过最小绝对收缩和选择算子(Lasso)分析、支持向量机递归特征消除(SVM-RFE)分析和逻辑回归分析,构建了基于6个外体基因的训练集诊断模型。诊断模型在测试和exoRBase 2.0数据库中进行了内部验证,并在GEO数据库中进行了外部验证。此外,共表达分析被用于聚类共表达模块,并对模块基因进行富集分析。然后构建了蛋白质相互作用和竞争的内源RNA网络,并利用模块基因鉴定了10个枢纽基因。总之,这些结果提供了一个全面的了解外泌体基因在CRC中的功能,以及与外泌体基因相关的诊断模型。
Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Exosomes have great potential as liquid biopsy specimens due to their presence and stability in body fluids. However, the function and diagnostic values of exosomal genes in CRC are poorly understood. In the present study, exosomal data of CRC and healthy samples from the exoRBase 2.0 and Gene Expression Omnibus (GEO) databases were used, and 38 common exosomal genes were identified. Through the least absolute shrinkage and selection operator (Lasso) analysis, support vector machine recursive feature elimination (SVM-RFE) analysis, and logistic regression analysis, a diagnostic model of the training set was constructed based on 6 exosomal genes. The diagnostic model was internally validated in the test and exoRBase 2.0 database and externally validated in the GEO database. In addition, the co-expression analysis was used to cluster co-expression modules, and the enrichment analysis was performed on module genes. Then a protein–protein interaction and competing endogenous RNA network were constructed and 10 hub genes were identified using module genes. In conclusion, the results provided a comprehensive understanding of the functions of exosomal genes in CRC as well as a diagnostic model related to exosomal genes.
DOI: 10.1016/j.gene.2019.01.001
发表时间: 2019-04-15
期刊: GENE
影响因子: 3.5
作者:
Chen, Linbo;Lu, Dewen;Xu, Feng
通讯作者: Xu, Feng
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DOI: 10.1186/1471-2105-9-559
发表时间: 2008-12-29
期刊: BMC bioinformatics
影响因子: 3
作者:
Langfelder P;Horvath S
通讯作者: Horvath S
DOI: 10.1093/nar/gkv007
发表时间: 2015-04-20
影响因子: 14.9
作者:
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DOI: 10.1093/nar/gkx891
发表时间: 2018-01-04
影响因子: 14.9
作者:
Li S;Li Y;Chen B;Zhao J;Yu S;Tang Y;Zheng Q;Li Y;Wang P;He X;Huang S
通讯作者: Huang S
DOI: 10.18632/aging.103537
发表时间: 2020-08-29
期刊: Aging
影响因子: --
作者:
Li J;Zhou Y;Yan Y;Zheng Z;Hu Y;Wu W
通讯作者: Wu W