ArticleScientific reports2023
A cross-cohort computational framework to trace tumor tissue-of-origin based on RNA sequencing.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- 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
- Rethinking cancer of unknown primary: from diagnostic challenge to targeted treatment.Nature reviews. Clinical oncology · 2025Review
- Antiviral Activity of Diltiazem HCl Against Pseudorabies Virus Infection In Vitro.Veterinary sciences · 2025Article
- Detection and isolation of brain tumors in cancer patients using neural network techniques in MRI images.Scientific reports · 2024Article
- Application of a single-cell-RNA-based biological-inspired graph neural network in diagnosis of primary liver tumors.Journal of translational medicine · 2024Article
- Association between thyroid disorders and extra-thyroidal cancers, a review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Review
- Refining neural network algorithms for accurate brain tumor classification in MRI imagery.BMC medical imaging · 2024Article
- Deciphering mycobiota and its functional dynamics in root hairs of Rhododendron campanulatum D. Don through Next-gen sequencing.Scientific reports · 2024Article
- Diabetes and diabetic associative diseases: An overview of epigenetic regulations of TUG1.Saudi journal of biological sciences · 2024Review
- Tumor immune dysfunction and exclusion subtypes in bladder cancer and pan-cancer: a novel molecular subtyping strategy and immunotherapeutic prediction model.Journal of translational medicine · 2024Article
- The applications of internet of things in smart healthcare sectors: a bibliometric and deep study.Heliyon · 2024Review
- Regulation of the Nrf2/HO-1 axis by mesenchymal stem cells-derived extracellular vesicles: implications for disease treatment.Frontiers in cell and developmental biology · 2024Review
- ROR1-AS1: A Meaningful Long Noncoding RNA in Oncogenesis.Mini reviews in medicinal chemistry · 2024Review
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
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Authors and funding
7 authors.
Funding
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Abstract
Carcinoma of unknown primary (CUP) is a type of metastatic cancer with tissue-of-origin (TOO) unidentifiable by traditional methods. CUP patients typically have poor prognosis but therapy targeting the original cancer tissue can significantly improve patients' prognosis. Thus, it's critical to develop accurate computational methods to infer cancer TOO. While qPCR or microarray-based methods are effective in inferring TOO for most cancer types, the overall prediction accuracy is yet to be improved. In this study, we propose a cross-cohort computational framework to trace TOO of 32 cancer types based on RNA sequencing (RNA-seq). Specifically, we employed logistic regression models to select 80 genes for each cancer type to create a combined 1356-gene set, based on transcriptomic data from 9911 tissue samples covering the 32 cancer types with known TOO from the Cancer Genome Atlas (TCGA). The selected genes are enriched in both tissue-specific and tissue-general functions. The cross-validation accuracy of our framework reaches 97.50% across all cancer types. Furthermore, we tested the performance of our model on the TCGA metastatic dataset and International Cancer Genome Consortium (ICGC) dataset, achieving an accuracy of 91.09% and 82.67%, respectively, despite the differences in experiment procedures and pipelines. In conclusion, we developed an accurate yet robust computational framework for identifying TOO, which holds promise for clinical applications. Our code is available at http://github.com/wangbo00129/classifybysklearn .
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