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Novel MicroRNA Biomarkers for Colorectal Cancer Early Diagnosis and 5-Fluorouracil Chemotherapy Resistance but Not Prognosis: A Study from Databases to AI-Assisted Verifications
Örebro University, School of Medical Sciences. Centre for Systems Biology, Soochow University, Suzhou, China.ORCID iD: 0000-0001-5963-9261
Örebro University, School of Medical Sciences.
Centre for Systems Biology, Soochow University, Suzhou, China.
Department of Oncology and Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.
2020 (English)In: Cancers, ISSN 2072-6694, Vol. 12, no 2, article id E341Article in journal (Refereed) Published
Abstract [en]

Colorectal cancer (CRC) is one of the major causes of cancer death worldwide. In general, early diagnosis for CRC and individual therapy have led to better survival for the cancer patients. Accumulating studies concerning biomarkers have provided positive evidence to improve cancer early diagnosis and better therapy. It is, however, still necessary to further investigate the precise biomarkers for cancer early diagnosis and precision therapy and predicting prognosis. In this study, AI-assisted systems with bioinformatics algorithm integrated with microarray and RNA sequencing (RNA-seq) gene expression (GE) data has been approached to predict microRNA (miRNA) biomarkers for early diagnosis of CRC based on the miRNA-messenger RNA (mRNA) interaction network. The relationships between the predicted miRNA biomarkers and other biological components were further analyzed on biological networks. Bayesian meta-analysis of diagnostic test was utilized to verify the diagnostic value of the miRNA candidate biomarkers and the combined multiple biomarkers. Biological function analysis was performed to detect the relationship of candidate miRNA biomarkers and identified biomarkers in pathways. Text mining was used to analyze the relationships of predicted miRNAs and their target genes with 5-fluorouracil (5-FU). Survival analyses were conducted to evaluate the prognostic values of these miRNAs in CRC. According to the number of miRNAs single regulated mRNAs (NSR) and the number of their regulated transcription factor gene percentage (TFP) on the miRNA-mRNA network, there were 12 promising miRNA biomarkers were selected. There were five potential candidate miRNAs (miRNA-186-5p, miRNA-10b-5, miRNA-30e-5p, miRNA-21 and miRNA-30e) were confirmed as CRC diagnostic biomarkers, and two of them (miRNA-21 and miRNA-30e) were previously reported. Furthermore, the combinations of the five candidate miRNAs biomarkers showed better prediction accuracy for CRC early diagnosis than the single miRNA biomarkers. miRNA-10b-5p and miRNA-30e-5p were associated with the 5-FU therapy resistance by targeting the related genes. These miRNAs biomarkers were not statistically associated with CRC prognosis.

Place, publisher, year, edition, pages
MDPI, 2020. Vol. 12, no 2, article id E341
Keywords [en]
CRC, biomarkers, diagnosis, miRNA, network models
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:oru:diva-79936DOI: 10.3390/cancers12020341ISI: 000522477300087PubMedID: 32028703Scopus ID: 2-s2.0-85079242365OAI: oai:DiVA.org:oru-79936DiVA, id: diva2:1394780
Funder
Swedish Cancer Society, CAN 2016/341Swedish Research Council Formas, 2016-01098Available from: 2020-02-20 Created: 2020-02-20 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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