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Expression profiling using a tumor-specific cDNA microarray predicts the prognosis of intermediate risk neuroblastomas
Open accessAbstract
To predict the prognosis of neuroblastoma patients and choose a better therapeutic protocol, we developed a cDNA microarray carrying 5340 genes obtained from primary neuroblastomas and examined 136 tumor samples. We made a probabilistic output statistical classifier that provided a high accuracy in prognosis prediction (89% at 5 years) and a highly reliable method to validate it. Kaplan-Meier analysis indicated that the patients in an intermediate group defined by existing markers are divided by microarray into two further groups with 5 year survivals for 36% and 89% of patients (p < 10(-4)), i.e., with unfavorably and favorably predicted neuroblastomas, respectively. According to these results, we developed a gene subset chip for a clinical tool, for which our classifier exhibited 88% prediction accuracy.
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“In addition, a number of microarray-based gene expression models have been proposed to predict neuroblastoma patient outcome [16, 17].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [17]
In the citing paper
“The models significantly discriminated patients with favorable and unfavorable outcome in the entire cohort and in the class labeled cohort (Additional file 1: Figure S9a-d, Table S3 and S4), which is consistent with previous observations made by us and others [16–18, 20].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [17]
In the citing paper
“Both gene expression-based models derived from RNA-seq and microarray analyses were capable of predicting patient outcome in the entire neuroblastoma cohort accurately, thereby validating results from previous studies and underscoring their potential clinical utility for risk estimation in neuroblastoma [16–18, 20].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [17]
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Metadata
- Authors
- Ohira, M., Oba, S., Nakamura, Y., Isogai, E., Kaneko, S., Nakagawa, A., Hirata, T., Kubo, H., Goto, T., Yamada, S., Yoshida, Y., Fuchioka, M., Ishii, S., & Nakagawara, A.
- Affiliations
- Chiba Cancer Center (JP)
- Nara Institute of Science and Technology (JP)
- University of Tsukuba (JP)
- Aichi Medical University (JP)
- Hisamitsu Pharmaceutical (Japan) (JP)
- NGK Insulators (Japan) (JP)
- SRL (Japan) (JP)
- Corresponding author
- Nakagawara, Akira
- ORCID
- Venue
- Cancer Cell
- Publisher
- Elsevier BV
- Published
- 2005-04
- Volume
- 7
- Issue
- 4
- Pages
- 337-350
- ISSN
- 1535-6108 (ISSN-L)
- OpenAlex
- W2110417418
- Cited by
- 155
- References
- 51
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 7 · 2022: 4 · 2021: 3 · 2020: 2 · 2018: 5 · 2017: 1 · 2016: 5 · 2015: 5 · 2014: 8 · 2013: 10 · 2012: 16
- FWCI
- 8.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cancer Cell (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Health, Labour and Welfare
- Ministry of Education, Culture, Sports, Science and Technology
- Topics
- Health Sciences › Medicine › Oncology › Neuroblastoma Research and Treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- neuroblastoma, intermediate-risk neuroblastoma, outcome prediction, gene expression profiling, probabilistic classifiers, clinical decision support, survival prediction
- MeSH terms
- Algorithms, Artificial Intelligence, Humans, Neoplasm Staging, Neoplasms, Neuroblastoma, Prognosis, Sensitivity and Specificity, Reproducibility of Results, Models, Statistical, Survival Analysis, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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