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Potential Applications of DNA, RNA and Protein Biomarkers in Diagnosis, Therapy and Prognosis for Colorectal Cancer: A Study from Databases to AI-Assisted Verification
Örebro University, School of Medical Sciences. Centre for Systems Biology, Soochow University, Suzhou, China.ORCID iD: 0000-0001-5963-9261
Department of Oncology and Clinical and Experimental Medicine, Linköping University, Linköping, Sweden.
Centre for Systems Biology, Soochow University, Suzhou, China.
Örebro University, School of Medical Sciences.ORCID iD: 0000-0003-1834-1578
2019 (English)In: Cancers, ISSN 2072-6694, Vol. 11, no 2, article id 172Article in journal (Refereed) Published
Abstract [en]

In order to find out the most valuable biomarkers and pathways for diagnosis, therapy and prognosis in colorectal cancer (CRC) we have collected the published CRC biomarkers and established a CRC biomarker database (CBD: http://sysbio.suda.edu.cn/CBD/index.html). In this study, we analysed the single and multiple DNA, RNA and protein biomarkers as well as their positions in cancer related pathways and protein-protein interaction (PPI) networks to describe their potential applications in diagnosis, therapy and prognosis. CRC biomarkers were collected from the CBD. The RNA and protein biomarkers were matched to their corresponding DNAs by the miRDB database and the PubMed Gene database, respectively. The PPI networks were used to investigate the relationships between protein biomarkers and further detect the multiple biomarkers. The Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Ontology (GO) annotation were used to analyse biological functions of the biomarkers. AI classification techniques were utilized to further verify the significances of the multiple biomarkers in diagnosis and prognosis for CRC. We showed that a large number of the DNA, RNA and protein biomarkers were associated with the diagnosis, therapy and prognosis in various degrees in the CRC biomarker networks. The CRC biomarkers were closely related to the CRC initiation and progression. Moreover, the biomarkers played critical roles in cellular proliferation, apoptosis and angiogenesis and they were involved in Ras, p53 and PI3K pathways. There were overlaps among the DNA, RNA and protein biomarkers. AI classification verifications showed that the combined multiple protein biomarkers played important roles to accurate early diagnosis and predict outcome for CRC. There were several single and multiple CRC protein biomarkers which were associated with diagnosis, therapy and prognosis in CRC. Further, AI-assisted analysis revealed that multiple biomarkers had potential applications for diagnosis and prognosis in CRC.

Place, publisher, year, edition, pages
MDPI , 2019. Vol. 11, no 2, article id 172
Keywords [en]
DNA, RNA, protein, single-biomarkers, multiple-biomarkers, cancer-related pathways, colorectal cancer
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:oru:diva-73340DOI: 10.3390/cancers11020172ISI: 000460747200046PubMedID: 30717315Scopus ID: 2-s2.0-85062386858OAI: oai:DiVA.org:oru-73340DiVA, id: diva2:1299143
Funder
Swedish Cancer SocietySwedish Research CouncilAvailable from: 2019-03-26 Created: 2019-03-26 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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