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Cited by 25Gene expression and cancer classificationMicroRNA in disease regulationBreast Cancer Treatment Studies

Cancer survival classification using integrated data sets and intermediate information

Kim, Shinuk, Park, Taesung, Kon, Mark·2014·Artificial Intelligence in Medicine
DOI 10.1016/j.artmed.2014.06.003
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Abstract

Objective Although numerous studies related to cancer survival have been published, increasing the prediction accuracy of survival classes still remains a challenge. Integration of different data sets, such as microRNA (miRNA) and mRNA, might increase the accuracy of survival class prediction. Therefore, we suggested a machine learning (ML) approach to integrate different data sets, and developed a novel method based on feature selection with Cox proportional hazard regression model (FSCOX) to improve the prediction of cancer survival time.Methods FSCOX provides us with intermediate survival information, which is usually discarded when separating survival into 2 groups (short- and long-term), and allows us to perform survival analysis. We used an ML-based protocol for feature selection, integrating information from miRNA and mRNA expression profiles at the feature level. To predict survival phenotypes, we used the following classifiers, first, existing ML methods, support vector machine (SVM) and random forest (RF), second, a new median-based classifier using FSCOX (FSCOX_median), and third, an SVM classifier using FSCOX (FSCOX_SVM). We compared these methods using 3 types of cancer tissue data sets: (i) miRNA expression, (ii) mRNA expression, and (iii) combined miRNA and mRNA expression. The latter data set included features selected either from the combined miRNA/mRNA profile or independently from miRNAs and mRNAs profiles (IFS).Results In the ovarian data set, the accuracy of survival classification using the combined miRNA/mRNA profiles with IFS was 75% using RF, 86.36% using SVM, 84.09% using FSCOX_median, and 88.64% using FSCOX_SVM with a balanced 22 short-term and 22 long-term survivor data set. These accuracies are higher than those using miRNA alone (70.45%, RF; 75%, SVM; 75%, FSCOX_median; and 75%, FSCOX_SVM) or mRNA alone (65.91%, RF; 63.64%, SVM; 72.73%, FSCOX_median; and 70.45%, FSCOX_SVM). Similarly in the glioblastoma multiforme data, the accuracy of miRNA/mRNA using IFS was 75.51% (RF), 87.76% (SVM) 85.71% (FSCOX_median), 85.71% (FSCOX_SVM). These results are higher than the results of using miRNA expression and mRNA expression alone. In addition we predict 16 hsa-miR-23b and hsa-miR-27b target genes in ovarian cancer data sets, obtained by SVM-based feature selection through integration of sequence information and gene expression profiles.Conclusion Among the approaches used, the integrated miRNA and mRNA data set yielded better results than the individual data sets. The best performance was achieved using the FSCOX_SVM method with independent feature selection, which uses intermediate survival information between short-term and long-term survival time and the combination of the 2 different data sets. The results obtained using the combined data set suggest that there are some strong interactions between miRNA and mRNA features that are not detectable in the individual analyses.

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Crossref · 10.1016/j.artmed.2014.06.003

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  • Metabolomics is integrated with mRNA for studying ulcerative colitis and cancer survival.

    “Similarly, metabolomics is integrated with mRNA for studying ulcerative colitis (Bjerrum et al., 2014) and cancer survival (Kim et al., 2014).”

    From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Kim et al., 2014

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Authors
Kim, S., Park, T., & Kon, M.
Affiliations
  • Boston University (US)
  • Seoul National University (KR)
ORCID
Venue
Artificial Intelligence in Medicine
Publisher
Elsevier BV
Published
2014-09
Volume
62
Issue
1
Pages
23-31
ISSN
0933-3657 (ISSN-L)
DOI
10.1016/j.artmed.2014.06.003
OpenAlex
W2112124032
Cited by
25
References
71
Citations by year
2025: 4 · 2023: 2 · 2022: 1 · 2021: 8 · 2020: 3 · 2019: 1 · 2017: 3 · 2016: 2 · 2015: 1
FWCI
0.82
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Artificial Intelligence in Medicine (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • NIH (1R21CA13582-01, 1R01GM080625-01A1)
  • National Resesarch Foundation of Korea (355-2008-C00004, MEST 2012R1A3A2026438, 2012-0000644)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
Keywords
cancer survival prediction, Cox proportional hazards model, feature selection, microRNA expression, mRNA expression, multi-omics integration, ovarian cancer, glioblastoma, prognostic factors, random forest, support vector machine, gene expression profiles, survival time prediction, prognostic signature
MeSH terms
Algorithms, Artificial Intelligence, Brain Neoplasms, Female, Glioblastoma, Humans, Ovarian Neoplasms, RNA, Messenger, Sensitivity and Specificity, Survival Rate, Proportional Hazards Models, MicroRNAs, Datasets as Topic

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