Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer
Open accessAbstract
BACKGROUND: microRNA (miRNA) expression plays an influential role in cancer classification and malignancy, and miRNAs are feasible as alternative diagnostic markers for pancreatic cancer, a highly aggressive neoplasm with silent early symptoms, high metastatic potential, and resistance to conventional therapies. METHODS: In this study, we evaluated the benefits of multi-omics data analysis by integrating miRNA and mRNA expression data in pancreatic cancer. Using support vector machine (SVM) modelling and leave-one-out cross validation (LOOCV), we evaluated the diagnostic performance of single- or multi-markers based on miRNA and mRNA expression profiles from 104 PDAC tissues and 17 benign pancreatic tissues. For selecting even more reliable and robust markers, we performed validation by independent datasets from the Gene Expression Omnibus (GEO) data depository. For validation, miRNA activity was estimated by miRNA-target gene interaction and mRNA expression datasets in pancreatic cancer. RESULTS: Using a comprehensive identification approach, we successfully identified 705 multi-markers having powerful diagnostic performance for PDAC. In addition, these marker candidates annotated with cancer pathways using gene ontology analysis. CONCLUSIONS: Our prediction models have strong potential for the diagnosis of pancreatic cancer.
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mRNA-miRNA integration was investigated using Neural Fuzzy Network for colorectal cancer, SVM for pancreatic cancer, and RF for cardiac tissue ageing and ovarian cancer.
“Similarly, mRNA -miRNA integration was investigated using Neural Fuzzy Network for colorectal cancer (Vineetha et al., 2013), SVM for pancreatic cancer (Kwon et al., 2015), and RF for cardiac tissue ageing (Dimitrakopoulos et al., 2014) and ovarian cancer (Anděl et al., 2015) respectively.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Kwon et al., 2015
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- Authors
- Kwon, M.-S., Kim, Y., Lee, S., Namkung, J., Yun, T., Yi, S. G., Han, S., Kang, M., Kim, S. W., Jang, J.-Y., & Park, T.
- Affiliations
- Seoul National University (KR)
- Sejong University (KR)
- Corresponding author
- Jang, Jin-Young
- Park, Taesung
- ORCID
- Venue
- BMC Genomics
- Published
- 2015-08-17
- Volume
- 16
- Issue
- S9
- Pages
- 88-88
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC4547403
- OpenAlex
- W2143778205
- Cited by
- 47
- References
- 36
- Citations by year
- 2025: 4 · 2024: 6 · 2023: 3 · 2022: 4 · 2021: 6 · 2020: 6 · 2019: 6 · 2018: 5 · 2017: 4 · 2016: 2 · 2015: 1
- FWCI
- 1.32
- Type
- journal article
- Language
- en
- Update notices
- correction (2017-01-16)
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Genomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion, cc-by)
- Funders
- National Research Foundation (2013M3A9C4078158, 2012R1A3A2026438)
- Ministry of Science, ICT and Future Planning (2013M3A9C4078158, 2012R1A3A2026438)
- National Research Foundation of Korea (2013R1A1A3010025, 2013M3A9C4078158, 2012R1A3A2026438)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
- Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- pancreatic ductal adenocarcinoma, pancreatic cancer diagnosis, microRNA expression, mRNA expression, multi-omics integration, diagnostic biomarkers, support vector machine
- MeSH terms
- Humans, Pancreatic Neoplasms, RNA, Messenger, Biomarkers, Tumor, Computational Biology, MicroRNAs, Transcriptome
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