ArticleNeoplasia (New York, N.Y.)2014
RNA-Seq accurately identifies cancer biomarker signatures to distinguish tissue of origin.
Article in Neoplasia (New York, N.Y.), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- Optimizing bioinformatic workflows to extract clinically usable gene expression data from targeted tumor RNA sequencing panels: comparison with total RNA-seq in cancer samples.Bioinformatics advances · 2026Article
- A Comprehensive Review: Molecular Diagnostics and Multi-Omics Approaches to Understanding Bovine Respiratory Disease.Veterinary sciences · 2025Review
- HPD is an RNA-Binding Protein Sustaining Ovarian Cancer Cell Glycolysis, Tumor Growth, and Drug Resistance.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- An Integrative Genomics Approach for the Discovery of Potential Clinically Actionable Diagnostic and Prognostic Biomarkers in Colorectal Cancer.Biomedicines · 2025Article
- Tracing unknown tumor origins with a biological-pathway-based transformer model.Cell reports methods · 2024Article
- A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.BMC bioinformatics · 2024Article
- Application of Machine Learning in Predicting Hepatic Metastasis or Primary Site in Gastroenteropancreatic Neuroendocrine Tumors.Current oncology (Toronto, Ont.) · 2023Article
- Machine learning for pan-cancer classification based on RNA sequencing data.Frontiers in molecular biosciences · 2023Review
- A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery.Medical oncology (Northwood, London, England) · 2022Review
- Transcriptomics and solid tumors: The next frontier in precision cancer medicine.Seminars in cancer biology · 2022Review
- Article
- Simulating the restoration of normal gene expression from different thyroid cancer stages using deep learning.BMC cancer · 2022Article
- Identifying common transcriptome signatures of cancer by interpreting deep learning models.Genome biology · 2022Article
- Molecular Pathology of Lung Cancer.Cold Spring Harbor perspectives in medicine · 2022Review
- [CDK1, CCNB1 and NDC80 are associated with prognosis and progression of hepatitis B virus-associated hepatocellular carcinoma: a bioinformatic analysis].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Article
- Integrated Bioinformatic Analysis Identifies Networks and Promising Biomarkers for Hepatitis B Virus-Related Hepatocellular Carcinoma.International journal of genomics · 2020Article
- Rapid preliminary purity evaluation of tumor biopsies using deep learning approach.Computational and structural biotechnology journal · 2020Article
- An expanded landscape of human long noncoding RNA.Nucleic acids research · 2019Article
- Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?RNA (New York, N.Y.) · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Metastatic cancer of unknown primary (CUP) accounts for up to 5% of all new cancer cases, with a 5-year survival rate of only 10%. Accurate identification of tissue of origin would allow for directed, personalized therapies to improve clinical outcomes. Our objective was to use transcriptome sequencing (RNA-Seq) to identify lineage-specific biomarker signatures for the cancer types that most commonly metastasize as CUP (colorectum, kidney, liver, lung, ovary, pancreas, prostate, and stomach). RNA-Seq data of 17,471 transcripts from a total of 3,244 cancer samples across 26 different tissue types were compiled from in-house sequencing data and publically available International Cancer Genome Consortium and The Cancer Genome Atlas datasets. Robust cancer biomarker signatures were extracted using a 10-fold cross-validation method of log transformation, quantile normalization, transcript ranking by area under the receiver operating characteristic curve, and stepwise logistic regression. The entire algorithm was then repeated with a new set of randomly generated training and test sets, yielding highly concordant biomarker signatures. External validation of the cancer-specific signatures yielded high sensitivity (92.0% ± 3.15%; mean ± standard deviation) and specificity (97.7% ± 2.99%) for each cancer biomarker signature. The overall performance of this RNA-Seq biomarker-generating algorithm yielded an accuracy of 90.5%. In conclusion, we demonstrate a computational model for producing highly sensitive and specific cancer biomarker signatures from RNA-Seq data, generating signatures for the top eight cancer types responsible for CUP to accurately identify tumor origin.
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