ArticleCancer reports (Hoboken, N.J.)2025
A New Radiotranscriptomic Approach to Analyze Combined Sets of T3b Stage-Specific Genes and Radiomic Features in Prostate Cancer.
Article in Cancer reports (Hoboken, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prostate cancer research on social media platforms: a bibliometric and thematic analysis.Frontiers in oncology · 2026Pooled it
- A New Radiotranscriptomic Approach to Analyze Combined Sets of T3b Stage-Specific Genes and Radiomic Features in Prostate Cancer.Cancer reports (Hoboken, N.J.) · 2025Article
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9 authors.
Funding
Abstract
backgroundCurrent clinical staging of prostate cancer (PCa) using the tumor-node-metastasis (TNM) system and serum biomarkers remains limited in distinguishing locally advanced (T3b) PCa from organ-confined (T2c) disease.
aimsBuilding on our previous biomarker discovery in differentiating PCa from that of benign prostatic hyperplasia, this study pioneers a radiotranscriptomic model to distinguish T3b stage PCa from T2c stage PCa by integrating contrast-enhanced ultrasound (CEUS) radiomics with stage-specific transcriptomic signatures, addressing a critical knowledge gap in precision staging. METHODS AND
resultsThis prospective study was approved by the review board of Chinese PLA General Hospital (S2021-565-01), and all participants provided written informed consent. Transrectal B-mode ultrasound images and contrast-enhanced ultrasound images on two imaging planes were prospectively analyzed in 48 patients with biopsy-confirmed PCa (35 patients with stage T2c and 13 with stage T3b). Textural features were evaluated using microvascular ultrasonography and contrast-enhanced ultrasound. Radiomic data were then retrieved from all modes. An across-the-board investigation of mRNA and miRNA expressions was also performed in the two PCa stages. Six biomarkers (frizzled 4, ribosomal protein S7, ribosomal protein L29, miR-374c, miR-9, and miR-6510) were identified to differentiate T3b stage from T2c stage. The area under the curve (AUC) values of the combined set (AUC = 0.887, 0.956, and 0.996 for random forest, naïve Bayes, and support vector machine, respectively) and radiomic features alone (AUC = 0.921, 0.957, and 0.998, respectively) were found to be more accurate than those of the transcriptomic data alone (AUC = 0.583, 0.716, and 0.898, respectively) or clinical features alone (AUC = 0.585, 0.675, and 0.953, respectively). The PCa gene regulatory network comprised of four miRNAs (miR-148, miR-141, miR-342, and miR-210) may contribute to accelerating tumor progression.
conclusionWe established the new radiotranscriptomic signatures specifically optimized for differentiating T3b stage from T2c stage by decoding stage-specific imaging-genomic crosstalk. This new approach may overcome TNM staging limitations.
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