Deep learning-based pan-cancer classification model reveals tissue-of-origin specific gene expression signatures
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Abstract
Cancer tissue-of-origin specific biomarkers are needed for effective diagnosis, monitoring, and treatment of cancers. In this study, we analyzed transcriptomics data from 37 cancer types provided by The Cancer Genome Atlas (TCGA) to identify cancer tissue-of-origin specific gene expression signatures. We developed a deep neural network model to classify cancers based on gene expression data. The model achieved a predictive accuracy of >97% across cancer types indicating the presence of distinct cancer tissue-of-origin specific gene expression signatures. We interpreted the model using Shapley additive explanations to identify specific gene signatures that significantly contributed to cancer-type classification. We evaluated the model and the validity of gene signatures using an independent test data set from the International Cancer Genome Consortium. In conclusion, we present a robust neural network model for accurate classification of cancers based on gene expression data and also provide a list of gene signatures that are valuable for developing biomarker panels for determining cancer tissue-of-origin. These gene signatures serve as valuable biomarkers for determining tissue-of-origin for cancers of unknown primary.
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Transcriptomic-based algorithms for cell-of-origin prediction have been developed, but they require expansion of clinical transcriptome profiling for generalizability assessments.
“Transcriptomic-based algorithms have also been developed (136, 137), although these approaches will require expansion of clinical transcriptome profiling for assessments of generalizability.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [137]
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- Authors
- Divate, M., Tyagi, A., Richard, D. J., Prasad, P. A., Gowda, H., & Nagaraj, S. H.
- Affiliations
- Queensland University of Technology (AU)
- Indian Institute of Technology Delhi (IN)
- Translational Research Institute (AU)
- Indian Institute of Science Bangalore (IN)
- Corresponding author
- Gowda, Harsha
- Nagaraj, Shivashankar H.
- Venue
- Cancers
- Publisher
- MDPI AG
- Published
- 2022-02-24
- Volume
- 14
- Issue
- 5
- Pages
- 1185
- ISSN
- 2072-6694 (ISSN-L)
- PMC
- PMC8909043
- OpenAlex
- W4214618644
- Cited by
- 55
- References
- 46
- Citations by year
- 2026: 11 · 2025: 16 · 2024: 16 · 2023: 8 · 2022: 4
- FWCI
- 3.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cancers (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) (acceptedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Health Sciences › Medicine › Oncology › Cancer Diagnosis and Treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- pan-cancer classification, tissue of origin, gene expression signatures, cancer of unknown primary, biomarker panel, deep neural network, SHAP, transcriptomics, TCGA, ICGC, cancer diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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