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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (12): 98-108    DOI: 10.11925/infotech.2096-3467.2018.0545
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Predicting Antineoplastic Drug Targets Based on Network Properties
Fan Xinyue, Cui Lei()
School of Medical Informatics, China Medical University, Shenyang 110122, China
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Abstract  

[Objective] This paper tries to identify potential targets of antineoplastic drugs, aiming to provide references for future clinical work and experiment. [Methods] First, we retrieved the targets of antineoplastic drugs from the DrugBank database, which were also combined with the protein interaction information from the HPRD database. Then, we established the PPI network for these targets with Cytoscape and calculated the topology properties of the nodes. Third, we used SPSS single factor analysis and Weka’s information gain principle to choose the variables for topological attributes. Fourth, we introduced the SMOTE algorithm to process unbalanced data sets and constructed the prediction model for antineoplastic drug targets with the decision tree method. Finally, we compared the performance of our new model with those of the classic ones. [Results] The precision of the proposed model reached 73.18%. With the help of CBioPortal, we found 16 targets’ prediction scores higher than 0.9. These targets could mutate and amplify in various tumors, which were analyzed with the case of NR5A1. [Limitations] The characteristics of target functions, sequence attributes, and other factors should also be included to construct the model. [Conclusions] The proposed model could predict the potential targets of antineoplastic drugs effectively.

Key wordsPPI Network      Machine Learning      Decision Tree      Antineoplastic Drug Targets Prediction     
Received: 15 May 2018      Published: 16 January 2019
ZTFLH:  TP391 G353  

Cite this article:

Fan Xinyue,Cui Lei. Predicting Antineoplastic Drug Targets Based on Network Properties. Data Analysis and Knowledge Discovery, 2018, 2(12): 98-108.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0545     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I12/98

靶点 通用名 商品名 疾病 国内是否上市
EGFR
HER2
Necitumumab(耐昔妥珠单抗)
Osimertinib(奥昔替尼)
Portrazza
Tagrisso(泰瑞莎)
肺癌
AKL Ceritinib(色瑞替尼) Zykadia 肺癌
Alectinib(艾乐替尼) Alecensa 肺癌
Brigatinib(布吉替尼) Alunbrig 肺癌
VEGFR2 Ramucirumab(雷莫芦单抗) Cyramza 肺癌、胃癌、结直肠癌
BRAF Dabrafenib(达拉非尼)+
Trametinib(曲美替尼)
Tafinlar+Mekinist 肺癌
PD-1 Nivolumab(纳武单抗) Opdivo 肺癌、结直肠癌、肝癌
Pembrolizumab(派姆单抗) Keytruda(健痊得) 肺癌、结直肠癌
PD-L1 Atezolizumab(阿特珠单抗) Tecentrip 肺癌、胃癌
KIT,PDGFR,
RAF,RET,
VEGFR1/2/3
Regorafenib(瑞戈非尼) Stivarga 结直肠癌、肝癌
VEGFA/B,PIGF Ziv-aflibercept(阿柏西普) Zaltrap 结直肠癌
EGFR,KRAS Panitumumab(帕尼单抗) Vectibix 结直肠癌
—— Trifluridine(曲氟尿苷) Tipiracil 结直肠癌
RTK,VEGF Lenvatinib(乐伐替尼) Lenvima 肝癌
HER2 Ado-transtzumab
Emtansine(TDM-1)
Kadcyla 乳腺癌
Peryuzumab(帕妥珠单抗) Perjeta 乳腺癌
Neratinib Nerlynx 乳腺癌
CDK4 Palbociclib(帕博西尼) Ibrance 乳腺癌
CDK6 Ribociclib(瑞博西尼) Kisqali 乳腺癌
Abemaciclib Verzenio 乳腺癌
网络属性(预测特征) 重要性排序
Average Shortest Path Length ANR
Betweenness Centrality Average Shortest Path Length
Closeness Centrality Degree
Clustering Coefficienty Number Of Directed Edges
Degree Stress
Eccentricity Closeness Centrality
Number Of Directed Edges Eccentricity
Number Of Undirected Edges Clustering Coefficienty
Partner Of MultiEdgedNodePairs SelfLoops
Radiality Topological Coefficient
SelfLoops Betweenness Centrality
Stress Radiality
Topological Coefficient
ANR
算法 Precision Recall F-measure AUC AUPR
C4.5决策树 0.773 0.732 0.747 0.754 0.797
人工神经网络 0.784 0.745 0.759 0.753 0.796
贝叶斯网络 0.758 0.780 0.764 0.752 0.795
支持向量机 0.784 0.743 0.757 0.701 0.748
Gene Protein Mutation Amplification
NR5A1 Steroidogenic factor 1 Cutaneous Melanoma (3.14%) Prostate Cancer, NOS (16.92%)
CSF3R Granulocyte colony-stimulating factor receptor Penile Cancer (14.29%) Ovarian Cancer (5.71%)
NFKB2 Nuclear factor NF-kappa-B p100 subunit Cholangiocarcinoma (100%) Prostate Cancer, NOS (7.69%)
TNK2 Activated CDC42 kinase 1 Myelodysplasia (5.56%) Prostate Cancer, NOS (21.54%)
UBC Polyubiquitin-C Endometrial Cancer (2%) Prostate Cancer, NOS (12.31%)
PIK3R2 Phosphatidylinositol 3-kinase regulatory subunit beta Small Bowel Cancer (5.56%) Prostate Cancer, NOS (13.85%)
IDE Insulin-degrading enzyme Endometrial Cancer (3.78%) Prostate Cancer, NOS (7.69%)
PSMB3 Proteasome subunit beta type-3 Adrenocortical Carcinoma (0.99%) Breast Cancer, NOS (18.75%)
GRM7 Metabotropic glutamate receptor 7 Ovarian/Fallopian Tube Cancer, NOS (14.29%) Prostate Cancer, NOS (23.08%)
THRA Thyroid hormone receptor alpha Colorectal Adenocarcinoma(2.91%) Breast Cancer, NOS (18.75%)
MED1 Mediator of RNA polymerase II transcription subunit 1 Cervical Cancer (4.6%) Breast Cancer, NOS (31.25%)
THRB Thyroid hormone receptor beta Cutaneous Melanoma (5.23%) Prostate Cancer, NOS (21.54%)
NCS1 Neuronal calcium sensor 1 Endometrial Cancer (0.59%) Prostate Cancer, NOS (13.85%)
NR3C2 Mineralocorticoid receptor Ovarian/Fallopian Tube Cancer, NOS (14.29%) Prostate Cancer, NOS (15.38%)
TUB Tubby protein homolog Endometrial Cancer (4.08%) Prostate Cancer, NOS (9.23%)
IL2 Interleukin-2 Cutaneous Melanoma (1.05%) Prostate Cancer, NOS (7.69%)
癌症类型
基因名称
Melanoma Adrenocortical Carcinoma Endometrial Cancer Esophagogastric Cancer Colorectal Adenocarcinoma Cancer of Unknown Primary
AR 2.09% (2.79%a,18b) 1.97% (1.97%,20) 6.08% (6.68%,4) 4.09% (4.75%,11) 4.52% (4.52%,9) 5.14% (5.24%,7)
NCOA1 2.79% (3.83%,6) 1.97% (2.46%,9) 5.34% (7.42%,2) 2.47% (3.20%,7) 3.55% (3.55%,4) 4.40% (5.99%,3)
JUN 0.35% (1.05%,15) 0 (0.99,-) 0.59% (0.96%,8) 0.76% (1.15%,7) 1.94% (1.94%,2) 0.47% (3.27%,11)
MAPK1 1.39% (3.48%,4) 0.99% (2.96%,8) 1.19% (2.23%,6) 0.49% (1.55%,16) 0.65% (0.96%,14) 0.84% (6.08%,9)
NFKB1 2.09% (2.79%,4) 0.99% (0.99%,8) 3.86% (4.15%,2) 0.73% (0.89%,13) 2.91% (2.91%,3) 6.74% (8.23%,1)
基因名称 Case Ampilication Case 比例
AR 65 38 58.46%
NR5A1 65 11 16.92%
NCOA1 65 8 12.31%
JUN 65 6 9.23%
MAPK1 65 5 7.65%
NFKB1 65 4 6.15%
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