Evidence map›Paper›PMID 41290833›Full record

ArticleScientific reports2025

Identification of novel biomarkers for epithelial ovarian cancer through machine learning and explainable artificial intelligence using in silico and in vitro analysis.

Zahra Chekini, Toktam Dehghani, Reza Khayami, Amir Hossein Jafarian, Marjaneh Farazestanian, Malihe Hassanzadeh, Alireza Pasdar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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, 1 synthesis or guideline pooled it.

  1. Pooled it
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

7 authors.

Zahra ChekiniDepartment of Medical Genetics and Molecular Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Toktam DehghaniDepartment of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Reza KhayamiDepartment of Medical Genetics and Molecular Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Amir Hossein JafarianCancer Molecular Pathology Research Center, Faculty of Medicine, Mashhad University of Medical Science, Mashhad, Iran.
Marjaneh FarazestanianDepartment of Obstetrics and Gynecology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Malihe HassanzadehDepartment of Obstetrics and Gynecology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Alireza PasdarDepartment of Medical Genetics and Molecular Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. PasdarA@mums.ac.ir.

Funding

Mashhad Medical Science University of Medical Science, research grant number 4011793
6 · The paper itself

Abstract

Epithelial ovarian cancer (EOC) is a lethal gynecological malignancy. Ongoing research aimed to identify novel biomarkers and develop combined algorithms to improve diagnosis and prognosis prediction for EOC. RNA-seq related to EOC were obtained from the Recount 3 databases data. We categorized early, late stages versus control, all stages (I-IV) versus control, and early versus late stages for machine learning, deep learning model and explainable AI (XAI). The hub genes were selected by considering SHapley Additive exPlanations (SHAP) analysis. The expression of hub genes was validated in fresh tissue (34 EOC, and 33 benign neoplastic ovarian) and peripheral blood mononuclear cell (PBMC) from 39 EOC, and 33 benign neoplastic ovarian patients using real time-PCR. For late stage and also all stages versus control, three hub genes were selected including SGO1, VTA1, RBM5-AS1 were mainly involved in cell cycle, estrogen signaling and vesicle trafficking. SGO1, and VTA1 were increased significantly in both tissue and PBMC of EOC patients (p < 0.05). RBM5-AS1 was significantly decreased in EOC tissue as well as PBMC compared to benign neoplastic ovarian patients (p < 0.05). VTA1 and RBM5-AS1 show promise as both diagnostic and prognostic markers, while SGO1 may serve primarily as a diagnostic biomarker for EOC.

Indexed as

Artificial IntelligenceBiomarkers, TumorCarcinoma, Ovarian EpithelialMachine LearningOvarian NeoplasmsComputer SimulationFemaleGene Expression Regulation, NeoplasticHumansLeukocytes, MononuclearMiddle AgedPrognosisBiomarkers, TumorDeep learning modelDiagnostic biomarkerEpithelial ovarian cancer (EOC)Explainable AI (XAI)Machine learningPrognostic biomarker

Identifiers

PMID41290833
PMCPMC12647161

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Registered trials

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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.