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Cancer Statistics, 2017.《2017 年癌症统计》
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FCGR2A, FCGR3A, FCGR3B polymorphisms and susceptibility to rheumatoid arthritis: a meta-analysis.FCGR2A、FCGR3A、FCGR3B基因多态性与类风湿关节炎易感性：一项荟萃分析。
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基于递归特征消除的两种支持向量策略的胰腺癌生物标志物检测。

Pancreatic cancer biomarker detection by two support vector strategies for recursive feature elimination.

机构信息

Key Laboratory of Symbol Computation & Knowledge Engineering of Ministry of Education, College of Computer Science & Technology, Jilin University, Changchun 130012, PR China.

Cancer Systems Biology Center, China-Japan Union Hospital, Jilin University, Changchun 130033, PR China.

出版信息

Biomark Med. 2019 Feb;13(2):105-121. doi: 10.2217/bmm-2018-0273. Epub 2019 Feb 15.

DOI:10.2217/bmm-2018-0273
PMID:30767554
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6737501/
Abstract

AIM

Pancreatic cancer is one of the worst malignant tumors in prognosis. Therefore, to reduce the mortality rate of pancreatic cancer, early diagnosis and prompt treatment are particularly important.

RESULTS

We put forward a new feature-selection method that was used to find clinical markers for pancreatic cancer by combination of Support Vector Machine Recursive Feature Elimination (SVM-RFE) and Large Margin Distribution Machine Recursive Feature Elimination (LDM-RFE) algorithms. As a result, seven differentially expressed genes were predicted as specific biomarkers for pancreatic cancer because of their highest accuracy of classification on cancer and normal samples.

CONCLUSION

Three (MMP7, FOS and A2M) out of the seven predicted gene markers were found to encode proteins secreted into urine, providing potential diagnostic evidences for pancreatic cancer.

摘要

目的

胰腺癌是预后最差的恶性肿瘤之一。因此，降低胰腺癌的死亡率，早期诊断和及时治疗尤为重要。

结果

我们提出了一种新的特征选择方法，该方法结合支持向量机递归特征消除（SVM-RFE）和大间隔分布机递归特征消除（LDM-RFE）算法，用于寻找胰腺癌的临床标志物。结果，预测了七个差异表达基因作为胰腺癌的特异性生物标志物，因为它们对癌症和正常样本的分类准确性最高。

结论

在预测的七个基因标志物中，有三个（MMP7、FOS 和 A2M）被发现编码分泌到尿液中的蛋白质，为胰腺癌提供了潜在的诊断依据。