ArticleCancer research2025
Combining Spatial Transcriptomics, Pseudotime, and Machine Learning Enables Discovery of Biomarkers for Prostate Cancer.
Article in Cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Exploring the molecular mechanism of benzo[a]pyrene affecting prostate cancer based on network toxicology and external validation.Molecular diversity · 2026Article
- Uncovering genes driving developmental stage progression in prostate cancer through spatial transcriptomics.Genes & diseases · 2026Article
- Integrative Bioinformatics and Machine Learning Analysis Identifies Novel Molecular Biomarkers in Prostate Adenocarcinoma.International journal of molecular sciences · 2026Article
- Microsensors for a Quick Detection of Tissue Functional Age and Precision Medicine.Physiological research · 2026Article
- Review
- SECTOR: structural entropy-based learning of spatiotemporal organisation in spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Defining the combinatorial nature of gene modules in prostate cancer underlying lineage plasticity and metastasis.Research square · 2026Article
- Analysis of risk factors for MRI-invisible prostate cancer-the significance of AGGF1 immunohistochemical detection and PSAD.The Canadian journal of urology · 2026Article
- The multistep progression of areca nut-induced oral cancer: a mechanistic roadmap from pathogenesis to precision therapy.Life medicine · 2026Review
- A single-cell and spatial atlas of prostate cancer reveals the combinatorial nature of gene modules underlying lineage plasticity and metastasis.bioRxiv : the preprint server for biology · 2026Article
- Current Applications and Future Directions of Artificial Intelligence in Prostate Cancer Diagnosis: A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
- Advances in understanding the tumor microenvironment of neuroendocrine prostate cancer.Frontiers in oncology · 2026Review
- Pseudotime-conditioned diffusion models for imputing time-series single-cell data.Bioinformatics advances · 2026Article
- Machine learning-driven comprehensive profiling of tumor heterogeneity and sialylation in hepatocellular carcinoma.NPJ precision oncology · 2025Article
- Exploiting artificial intelligence in precision oncology: an updated comprehensive review.Journal of translational medicine · 2025Review
- Unveiling prognostic biomarkers and immunotherapeutic insights in prostate cancer through multi-omics and machine learning.European journal of medical research · 2025Article
- Reply to 'Enhancing spatial transcriptomics in clear-cell renal cell carcinoma'.Nature reviews. Urology · 2025Article
- Applications of digital twins in medicine.Nature biotechnology · 2025Article
- TAM Plasticity under androgen deprivation therapy and PARP inhibition in prostate cancer: a multi-omics perspective.Frontiers in immunology · 2025Review
- Comparative analysis of ultrasound-guided magnetic resonance imaging-cognitive fusion transrectal versus transperineal prostate biopsy: a 10-year single-center retrospective analysis.Frontiers in medicine · 2025Article
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9 authors.
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Abstract
Early cancer diagnosis is crucial but challenging owing to the lack of reliable biomarkers that can be measured using routine clinical methods. The identification of biomarkers for early detection is complicated by each tumor involving changes in the interactions between thousands of genes. In addition to this staggering complexity, these interactions can vary among patients with the same diagnosis as well as within the same tumor. We hypothesized that reliable biomarkers that can be measured with routine methods could be identified by exploiting three facts: (i) the same tumor can have multiple grades of malignant transformation; (ii) these grades and their molecular changes can be characterized using spatial transcriptomics; and (iii) these changes can be integrated into models of malignant transformation using pseudotime. Pseudotime models were constructed based on spatial transcriptomic data from three independent prostate cancer studies to prioritize the genes that were most correlated with malignant transformation. The identified genes were associated with cancer grade, copy-number aberrations, hallmark pathways, and drug targets, and they encoded candidate biomarkers for prostate cancer in mRNA, IHC, and proteomics data from the sera, prostate tissue, and urine of more than 2,000 patients with prostate cancer and controls. Machine learning-based prediction models revealed that the biomarkers in urine had an AUC of 0.92 for prostate cancer and were associated with cancer grade. Overall, this study demonstrates the diagnostic potential of combining spatial transcriptomics, pseudotime, and machine learning for prostate cancer, which should be further tested in prospective studies. SIGNIFICANCE: Integrating spatial transcriptomics, pseudotime, and machine learning analyses is effective for identifying prostate cancer biomarkers that are reliable in different settings and measurable with routine methods, providing potential early diagnosis strategies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.
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