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. 2018 Jan 1:2018:bay046.
doi: 10.1093/database/bay046.

CBD: a biomarker database for colorectal cancer

Xueli Zhang  1   2 , Xiao-Feng Sun  3 , Yang Cao  4   5 , Benchen Ye  2 , Qiliang Peng  6 , Xingyun Liu  2 , Bairong Shen  2 , Hong Zhang  1
Affiliations

CBD: a biomarker database for colorectal cancer

Xueli Zhang et al. Database (Oxford). .

Abstract

Colorectal cancer (CRC) biomarker database (CBD) was established based on 870 identified CRC biomarkers and their relevant information from 1115 original articles in PubMed published from 1986 to 2017. In this version of the CBD, CRC biomarker data were collected, sorted, displayed and analysed. The CBD with the credible contents as a powerful and time-saving tool provide more comprehensive and accurate information for further CRC biomarker research. The CBD was constructed under MySQL server. HTML, PHP and JavaScript languages have been used to implement the web interface. The Apache was selected as HTTP server. All of these web operations were implemented under the Windows system. The CBD could provide to users the multiple individual biomarker information and categorized into the biological category, source and application of biomarkers; the experiment methods, results, authors and publication resources; the research region, the average age of cohort, gender, race, the number of tumours, tumour location and stage. We only collect data from the articles with clear and credible results to prove the biomarkers are useful in the diagnosis, treatment or prognosis of CRC. The CBD can also provide a professional platform to researchers who are interested in CRC research to communicate, exchange their research ideas and further design high-quality research in CRC. They can submit their new findings to our database via the submission page and communicate with us in the CBD.Database URL: http://sysbio.suda.edu.cn/CBD/.

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Figures

Figure 1.
Figure 1.
Schematic flow of the CDB construction and application. After a series of standard selections (the detail description in the data collected section) there were 1115 articles selected from PubMed as the articles for our database construction. The biomarkers were categorized as DNA, RNA, protein and others. The multiple statistical and bioinformatic analyses can be used for further study on the biomarkers.
Figure 2.
Figure 2.
Search strategies for the CBD. (A) List search. (B) Key word search. (C) Advanced search.
Figure 3.
Figure 3.
An example of a biomarker work flow in the CBD. The detailed information of the biomarkers can be found in the CBD such as miR-195. (A) The detailed information page; (B) The ontology page; (C) PubMed page. The genes are linked to NCBI gene database, and the original articles are directly linked to PubMed.
Figure 3.
Figure 3.
An example of a biomarker work flow in the CBD. The detailed information of the biomarkers can be found in the CBD such as miR-195. (A) The detailed information page; (B) The ontology page; (C) PubMed page. The genes are linked to NCBI gene database, and the original articles are directly linked to PubMed.
Figure 4.
Figure 4.
Examples of statistical analyses from the CBD. (A) A distribution of CRC biomarker research worldwide. Top 100 cities with most CRC biomarker research are in the map (blue symbol), and the size of symbol represents the research number (The more research, the bigger symbol). (B) The biological categories of biomarkers in the CBD. (C) Research distribution according to the mean age. (D) Gender distribution. (E) Most used biomarkers in Asian and non-Asian countries. (F) Colon and rectal cancer related studies number. (G) Tumour metastasis situation based on research interest. (H)–(J) Showed research number distribution of research in CBD in sample source, cancer stage and biomarker application, respectively.
Figure 5.
Figure 5.
miRNA–gene interaction network for miRNA biomarkers of the CBD. The blue square represents the miRNA, and the red circle represents gene. (A) Overview of the miRNA-gene network. (B) Top 5 miRNA with largest degree and their interaction networks. (C) The interactions of has-mir-133a-5p.
Figure 6.
Figure 6.
PPI networks for protein biomarkers in the CBD. (A) The different circles represent different proteins. Different lines mean different kinds of interactions around the proteins. (B) Predicted functional partners by String. According to the known interactions from curated databases and experiments; predicted interactions from gene neighbourhood, fusion-fission events, occurrence; other information such as text-mining, co-expression and protein homology in PPI, PPS13-PRS25-RPS7 and XRCC5-FEN1 are predicted as functional partners by string. Known interactions: Sky blue (--) from curated databases; Purple (--) experimentally determined; Predicted interactions: Green (--) gene neighborhood; Red (--) gene fusions; Vivid blue (--) gene co-occurrence; Others: Yellow (--) text mining; Black (--) co-expression; Dutch blue (--) protein homology Figure legend.
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References

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