Analysis of Key GO Terms and KEGG Pathways Associated with Carcinogenic Chemicals

被引:21
作者
Ding, Jing [1 ]
Zhang, Ying [2 ]
机构
[1] Zhejiang Pharmaceut Coll, Sch Pharm, Ningbo 315100, Zhejiang, Peoples R China
[2] Shanghai Municipal Food & Drug Adm, Ctr Certificat & Evaluat, Shanghai 200020, Peoples R China
关键词
Carcinogenic chemical; non-carcinogenic chemical; enrichment score; GO term; KEGG pathway; minimal redundancy maximal relevance; CLATHRIN-DEPENDENT ENDOCYTOSIS; Y1 RECEPTOR GENE; RETINOIC ACID; BREAST-CANCER; METABOLIZING-ENZYMES; PROTEIN INTERACTIONS; MOLECULAR FRAGMENTS; NEGATIVE REGULATOR; CA2+ SENSITIZATION; FEATURE-SELECTION;
D O I
10.2174/1386207321666171218120133
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Aim and Objective: Cancer is one of the serious diseases that cause several human deaths every year. Up to now, we have spent lots of time and money to investigate this disease, thereby designing effective treatments. Previous studies mainly focus on studying genetic background of different subtypes of cancer and neglect another important factor, i.e. environmental factor. Carcinogenic chemical is one of the types of environmental factor; the exposure of such chemical may definitely initiate and promote the tumorigenesis. In this study, we tried to partly describe the differences between carcinogenic and non-carcinogenic chemicals using gene ontology (GO) terms and KEGG pathways. Material and Methods: The carcinogenic and non-carcinogenic chemicals that were retrieved from Carcinogenic Potency Database (CPDB) were encoded into numeric vectors using the enrichment theories of GO terms and KEGG pathways. Then, the minimal redundancy maximal relevance (mRMR) method was adopted to analyze all the features, resulting in some important GO terms and KEGG pathways. Results and Conclusion: The extensive analysis of the identified GO terms and KEGG pathways indicates that they all play roles during tumorigenesis, inducing that they can be the key indicator for the identification of carcinogenic chemicals.
引用
收藏
页码:861 / 871
页数:11
相关论文
共 108 条
[11]   Analysis of cancer-related IncRNAs using gene ontology and KEGG pathways [J].
Chen, Lei ;
Zhang, Yu-Hang ;
Lu, Guohui ;
Huang, Tao ;
Cai, Yu-Dong .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 2017, 76 :27-36
[12]   Application of the Shortest Path Algorithm for the Discovery of Breast Cancer-Related Genes [J].
Chen, Lei ;
Xing, ZhiHao ;
Huang, Tao ;
Shu, Yang ;
Huang, GuoHua ;
Li, Hai-Peng .
CURRENT BIOINFORMATICS, 2016, 11 (01) :51-58
[13]   Gene expression profiling gut microbiota in different races of humans [J].
Chen, Lei ;
Zhang, Yu-Hang ;
Huang, Tao ;
Cai, Yu-Dong .
SCIENTIFIC REPORTS, 2016, 6
[14]   Predicting the Types of Metabolic Pathway of Compounds Using Molecular Fragments and Sequential Minimal Optimization [J].
Chen, Lei ;
Chu, Chen ;
Feng, Kaiyan .
COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING, 2016, 19 (02) :136-143
[15]   A computational method for the identification of new candidate carcinogenic and non-carcinogenic chemicals [J].
Chen, Lei ;
Chu, Chen ;
Lu, Jing ;
Kong, Xiangyin ;
Huang, Tao ;
Cai, Yu-Dong .
MOLECULAR BIOSYSTEMS, 2015, 11 (09) :2541-2550
[16]   Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System [J].
Chen, Lei ;
Chu, Chen ;
Lu, Jing ;
Kong, Xiangyin ;
Huang, Tao ;
Cai, Yu-Dong .
PLOS ONE, 2015, 10 (05)
[17]   A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes [J].
Chen, Lei ;
Lu, Jing ;
Zhang, Ning ;
Huang, Tao ;
Cai, Yu-Dong .
MOLECULAR BIOSYSTEMS, 2014, 10 (04) :868-877
[18]   Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities [J].
Chen, Lei ;
Zeng, Wei-Ming ;
Cai, Yu-Dong ;
Feng, Kai-Yan ;
Chou, Kuo-Chen .
PLOS ONE, 2012, 7 (04)
[19]   Prediction of Interactiveness Between Small Molecules and Enzymes by Combining Gene Ontology and Compound Similarity [J].
Chen, Lei ;
Qian, Ziliang ;
Fen, Kaiyan ;
Cai, Yudong .
JOURNAL OF COMPUTATIONAL CHEMISTRY, 2010, 31 (08) :1766-1776
[20]   Retinoic acid receptors: From molecular mechanisms to cancer therapy [J].
di Masi, Alessandra ;
Leboffe, Loris ;
De Marinis, Elisabetta ;
Pagano, Francesca ;
Cicconi, Laura ;
Rochette-Egly, Cecile ;
Lo-Coco, Francesco ;
Ascenzi, Paolo ;
Nervi, Clara .
MOLECULAR ASPECTS OF MEDICINE, 2015, 41 :1-115