Evidence map›Paper›PMID 40335872›Full record

ArticleMedical & biological engineering & computing2025

Deep learning assisted identification of SCUBE2 and SLC16 A5 combination in RNA-sequencing data as a novel specific potential diagnostic biomarker in prostate cancer.

Saeideh Khorshid Sokhangouy, Mohsen Zeinali, Sina Fathi, Elham Nazari

Abstract read
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Article in Medical & biological engineering & computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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  3. Article
  4. Review
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

4 authors.

Saeideh Khorshid SokhangouyDepartment of Medical Biotechnology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Mohsen ZeinaliComputer Engineering Department, Islamic Azad University Khomeinishahr Branch, Isfahan, Iran.
Sina FathiDepartment of Health Information Technology and Management, School of Allied Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. fathi.si@sbmu.ac.ir.
Elham NazariProteomics Research Center, System Biology Institute, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Elham.Nazari@sbmu.ac.ir.

Funding

Shahid Beheshti University of Medical Sciences 43009794
6 · The paper itself

Abstract

Despite the extensive use of biomarkers like PSA, AMACR, and PCA3, prostate cancer (PCa) is still a major clinical challenge, demanding the development of more precise and specific methods for diagnosis. In this study, a deep learning model was applied to identify ten key genes from a pool of 68 common differentially expressed genes in the three transcriptomic datasets. The model demonstrated high performance, with the accuracy of 0.969, R

Indexed as

Biomarkers, TumorDeep LearningMonocarboxylic Acid TransportersProstatic NeoplasmsSequence Analysis, RNAGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleBiomarkers, TumorMonocarboxylic Acid TransportersArtificial intelligenceBiomarkerDiagnostic biomarkerProstate cancerRNA-seq

Identifiers

PMID40335872

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.