ArticleFrontiers in bioengineering and biotechnology2020
A Neural Network Framework for Predicting the Tissue-of-Origin of 15 Common Cancer Types Based on RNA-Seq Data.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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43 citing papers in PubMed, 1 synthesis or guideline pooled it, 83 citations in OpenAlex.
- Deep learning techniques for cancer classification using microarray gene expression data.Frontiers in physiology · 2022Pooled it
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- Soft multiclass feature augmented deep learning to predict tumor origins using cytology or histology whole slide images.NPJ digital medicine · 2026Article
- Unraveling heterogeneity in LUAD via multi-omics integration: molecular classification and therapeutic implications.Discover oncology · 2026Article
- Comparative transcriptome analysis reveals putative transcription factors components of the regulatory network for cannabidiol biosynthesis inFrontiers in plant science · 2026Article
- A multi-task deep learning framework for simultaneous prediction of microsatellite instability and tumor mutational burden in gastric cancer from histopathological images.Frontiers in oncology · 2026Article
- Antiviral Activity of Diltiazem HCl Against Pseudorabies Virus Infection In Vitro.Veterinary sciences · 2025Article
- OncoTrace-TOO: Interpretable Machine Learning Framework for Cancer Tissue-of-Origin Identification Using Transcriptomic Signatures.Cancer reports (Hoboken, N.J.) · 2025Article
- Impact of whole genome sequencing on the care pathway for patients with cancer of unknown primary.ESMO open · 2025Article
- Next-generation sequencing in cancer diagnosis and treatment: clinical applications and future directions.Discover oncology · 2025Review
- A fusion model to predict the survival of colorectal cancer based on histopathological image and gene mutation.Scientific reports · 2025Article
- Application of a single-cell-RNA-based biological-inspired graph neural network in diagnosis of primary liver tumors.Journal of translational medicine · 2024Article
- Mentha arvensis oil exhibits repellent acute toxic and antioxidant activities in Nauphoeta cinerea.Scientific reports · 2024Article
- A comprehensive analysis of the artificial neural networks model for predicting monkeypox outbreaks.Heliyon · 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
- Tracing unknown tumor origins with a biological-pathway-based transformer model.Cell reports methods · 2024Article
- Refining neural network algorithms for accurate brain tumor classification in MRI imagery.BMC medical imaging · 2024Article
- Article
- Deciphering mycobiota and its functional dynamics in root hairs of Rhododendron campanulatum D. Don through Next-gen sequencing.Scientific reports · 2024Article
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Authors and funding
13 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Sequencing-based identification of tumor tissue-of-origin (TOO) is critical for patients with cancer of unknown primary lesions. Even if the TOO of a tumor can be diagnosed by clinicopathological observation, reevaluations by computational methods can help avoid misdiagnosis. In this study, we developed a neural network (NN) framework using the expression of a 150-gene panel to infer the tumor TOO for 15 common solid tumor cancer types, including lung, breast, liver, colorectal, gastroesophageal, ovarian, cervical, endometrial, pancreatic, bladder, head and neck, thyroid, prostate, kidney, and brain cancers. To begin with, we downloaded the RNA-Seq data of 7,460 primary tumor samples across the above mentioned 15 cancer types, with each type of cancer having between 142 and 1,052 samples, from the cancer genome atlas. Then, we performed feature selection by the Pearson correlation method and performed a 150-gene panel analysis; the genes were significantly enriched in the GO:2001242 Regulation of intrinsic apoptotic signaling pathway and the GO:0009755 Hormone-mediated signaling pathway and other similar functions. Next, we developed a novel NN model using the 150 genes to predict tumor TOO for the 15 cancer types. The average prediction sensitivity and precision of the framework are 93.36 and 94.07%, respectively, for the 7,460 tumor samples based on the 10-fold cross-validation; however, the prediction sensitivity and precision for a few specific cancers, like prostate cancer, reached 100%. We also tested the trained model on a 20-sample independent dataset with metastatic tumor, and achieved an 80% accuracy. In summary, we present here a highly accurate method to infer tumor TOO, which has potential clinical implementation.
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