Unique Biological Characteristics of Patients with High Gleason Score and Localized/Locally Advanced Prostate Cancer Using an In Silico Translational Approach.

Shiori Miyachi, Masanori Oshi, Takeshi Sasaki, Itaru Endo, Kazuhide Makiyama, Takahiro Inoue

Abstract read

In one paragraph

Article in Current oncology (Toronto, Ont.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Machine learning-based prognostic model integrating preoperative HALP score and lactate dehydrogenase for predicting postoperative recurrence of prostate cancer.World journal of surgical oncology · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Shiori MiyachiDepartment of Nephro-Urologic Surgery and Andrology, Mie University Graduate School of Medicine, Tsu 514-8507, Mie, Japan.

Masanori OshiDepartment of Gastroenterological Surgery, Yokohama City University Graduate School of Medicine, Yokohama 236-0004, Kanagawa, Japan.ORCID 0000-0002-1404-1570

Takeshi SasakiDepartment of Nephro-Urologic Surgery and Andrology, Mie University Graduate School of Medicine, Tsu 514-8507, Mie, Japan.ORCID 0000-0002-2458-5686

Itaru EndoDepartment of Gastroenterological Surgery, Yokohama City University Graduate School of Medicine, Yokohama 236-0004, Kanagawa, Japan.

Kazuhide MakiyamaDepartment of Urology, Yokohama City University Graduate School of Medicine, Yokohama 236-0004, Kanagawa, Japan.ORCID 0000-0002-7449-2015

Takahiro InoueDepartment of Nephro-Urologic Surgery and Andrology, Mie University Graduate School of Medicine, Tsu 514-8507, Mie, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gleason score (GS) is one of the best predictors of prostate cancer (PCa) aggressiveness; however, its biological features need to be elucidated. This study aimed to explore the biological characteristics of localized/locally advanced PCa stratified using in silico GS analysis. Biological features were analyzed using gene set variation analysis and the xCell algorithm with mRNA expression in two independent public databases: The Cancer Genome Atlas (TCGA) (

Indexed as

Prostatic NeoplasmsComputer SimulationHumansMaleNeoplasm Gradinggene expressionGleason scoreGSVAprostate cancerxCell

Identifiers

PMID40710219

DOI10.3390/curroncol32070409

PMCPMC12294092

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.