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CBD: a biomarker database for colorectal cancer
Örebro University, School of Medical Sciences. Centre for Systems Biology, Soochow University, Suzhou, China.ORCID iD: 0000-0001-5963-9261
Department of Oncology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden.
Örebro University, School of Medical Sciences. Örebro University Hospital. Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden. (Clinical Epidemiology and Biostatistics)ORCID iD: 0000-0002-3552-9153
Centre for Systems Biology, Soochow University, Suzhou, China.
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2018 (English)In: Database: The Journal of Biological Databases and Curation, ISSN 1758-0463, E-ISSN 1758-0463, article id bay046Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Oxford University Press, 2018. article id bay046
National Category
Bioinformatics (Computational Biology) Cancer and Oncology
Identifiers
URN: urn:nbn:se:oru:diva-68074DOI: 10.1093/database/bay046ISI: 000436293800001PubMedID: 29846545OAI: oai:DiVA.org:oru-68074DiVA, id: diva2:1235268
Funder
Swedish Cancer SocietySwedish Research CouncilAvailable from: 2018-07-25 Created: 2018-07-25 Last updated: 2020-05-18Bibliographically approved
In thesis
1. Biomarkers for Diagnosis, Therapy and Prognosis in Colorectal Cancer: a study from databases, machine learning predictions to laboratory confirmations
Open this publication in new window or tab >>Biomarkers for Diagnosis, Therapy and Prognosis in Colorectal Cancer: a study from databases, machine learning predictions to laboratory confirmations
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Early diagnosis and better therapy response have been believed to be associated with better prognosis. CRC biomarkers are considered as precise indicators for the early diagnosis and better therapy response. It is, therefore, of importance to find out, analyze and evaluate the CRC biomarkers to further provide the more precis evidence for predicting novel potential biomarkers and eventually to improve early diagnosis, personalized therapy and prognosis for CRC.

In this study, we started with creating and establishing a CRC biomarker database. (CBD: http://sysbio.suda.edu.cn/CBD/index.html) In the CBD database, there were 870 reported CRC biomarkers collected from the published articles in PubMed. In this version of the CBD, CRC biomarker data was carefully collected, sorted, displayed, and analyzed. The major applications of the CBD are to provide 1) the records of CRC biomarkers (DNA, RNA, protein and others) concerning diagnosis, treatment and prognosis; 2) the basic and clinical research information concerning the CRC biomarkers; 3) the primary results for bioinformatics and biostatics analysis of the CRC biomarkers; 4) downloading/uploading the biomedicine information for CRC biomarkers.

Based on our CBD and other public databases, we further analyzed the presented CRC biomarkers (DNAs, RNAs, proteins) and predicted novel potential multiple biomarkers (the combination of single biomarkers) with biological networks and pathways analysis for diagnosis, therapy response and prognosis in CRC. We found several hub biomarkers and key pathways for the diagnosis, treatment and prognosis in CRC. Receiver operating characteristic (ROC) test and survival analysis by microarray data revealed that multiple biomarkers could be better biomarkers than the single biomarkers for the diagnosis and prognosis of CRC.

There are 62 diagnosis biomarkers for colon cancer in our CBD. In the previous studies, we found these present biomarkers were not enough to improve significantly the diagnosis of colon cancer. In order to find out novel biomarkers for the colon cancer diagnosis, we have performed /machine learning (ML) techniques such as support vector machine (SVM) and regression tree to predict candidate to discover diagnostic biomarkers for colon cancer. Based on the protein-protein interaction (PPI) network topology features of the identified biomarkers, we found 12 protein biomarkers which were considered as the candidate colon cancer diagnosis biomarkers. Among these protein biomarkers Chromogranin-A (CHGA)  was the most powerful biomarker, which showed good performance in bioinformatics test and Immunohistochemistry(IHC). We are now expanding this study to CRC.

Expression of CHGA protein in colon cancer was further verified with a novel logistic regressionbased meta-analysis, and convinced as a valuable diagnostic biomarker as compared with the typical diagnostic biomarkers, such as TP53, KRAS and MKI67.

microRNAs (miRNAs/miRs) have been considered as potential biomarkers. A novel miRNA-mRNA interaction network-based model was used to predict miRNA biomarkers for CRC and found that miRNA-186-5p, miRNA-10b-5p and miRNA-30e-5p might be the novel biomarkers for CRC diagnosis. In conclusion, we have created a useful CBD database for CRC biomarkers and provided detailed information for how to use the CBD in CRC biomarker investigations. Our studies have been focusing on the biomarkers in diagnosis, therapy and prognosis. Based on our CBD and other powerful cancer associated databases, ML has been used to analyze the characteristics of the CRC biomarkers and predict novel potential CRC biomarkers. The predicted potential biomarkers were further confirmed at biomedical laboratory.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2020. p. 58
Series
Örebro Studies in Medicine, ISSN 1652-4063 ; 214
Keywords
biomarkers, diagnosis, therapy response, prognosis, database, machine learning, CRC
National Category
Other Basic Medicine
Identifiers
urn:nbn:se:oru:diva-81184 (URN)978-91-7529-341-7 (ISBN)
Public defence
2020-06-11, Örebro universitet, Campus USÖ, hörsal C1, Södra Grev Rosengatan 32, Örebro, 09:00 (Swedish)
Opponent
Supervisors
Available from: 2020-04-17 Created: 2020-04-17 Last updated: 2020-05-18Bibliographically approved

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Zhang, XueliCao, YangZhang, Hong

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