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Chromogranin-A Expression as a Novel Biomarker for Early Diagnosis of Colon Cancer Patients
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 Clinical and Experimental Medicine, Linköping University, Linköping, Sweden.
2019 (English)In: International Journal of Molecular Sciences, ISSN 1422-0067, E-ISSN 1422-0067, Vol. 20, no 12, article id 2919Article in journal (Refereed) Published
Abstract [en]

Colon cancer is one of the major causes of cancer death worldwide. The five-year survival rate for the early-stage patients is more than 90%, and only around 10% for the later stages. Moreover, half of the colon cancer patients have been clinically diagnosed at the later stages. It is; therefore, of importance to enhance the ability for the early diagnosis of colon cancer. Taking advantages from our previous studies, there are several potential biomarkers which have been associated with the early diagnosis of the colon cancer. In order to investigate these early diagnostic biomarkers for colon cancer, human chromogranin-A (CHGA) was further analyzed among the most powerful diagnostic biomarkers. In this study, we used a logistic regression-based meta-analysis to clarify associations of CHGA expression with colon cancer diagnosis. Both healthy populations and the normal mucosa from the colon cancer patients were selected as the double normal controls. The results showed decreased expression of CHGA in the early stages of colon cancer as compared to the normal controls. The decline of CHGA expression in the early stages of colon cancer is probably a new diagnostic biomarker for colon cancer diagnosis with high predicting possibility and verification performance. We have also compared the diagnostic powers of CHGA expression with the typical oncogene KRAS, classic tumor suppressor TP53, and well-known cellular proliferation index MKI67, and the CHGA showed stronger ability to predict early diagnosis for colon cancer than these other cancer biomarkers. In the protein-protein interaction (PPI) network, CHGA was revealed to share some common pathways with KRAS and TP53. CHGA might be considered as a novel, promising, and powerful biomarker for early diagnosis of colon cancer.

Place, publisher, year, edition, pages
MDPI , 2019. Vol. 20, no 12, article id 2919
Keywords [en]
CHGA, colon cancer, biomarker, early diagnosis, logistic regression, meta-analysis, PPI
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
URN: urn:nbn:se:oru:diva-75234DOI: 10.3390/ijms20122919ISI: 000473756000069PubMedID: 31207989Scopus ID: 2-s2.0-85068403124OAI: oai:DiVA.org:oru-75234DiVA, id: diva2:1339069
Funder
Swedish Cancer SocietySwedish Research CouncilAvailable from: 2019-07-25 Created: 2019-07-25 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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