Evidence map›Paper›PMID 42728524›Full record

ArticleReproductive sciences (Thousand Oaks, Calif.)2026

Diagnostic and Predictive Values of Ferroptosis-Related Genes in Endometriosis Based on Integrated Bioinformatic Analysis.

Buhaiqiemu Kadeer, Shaadaiti Wufuer, Adilai Maimaitimin, Nuramatjan Ablat, Xiaopeng Li, Zhifang Chen

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Article in Reproductive sciences (Thousand Oaks, Calif.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

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6 authors.

Buhaiqiemu KadeerDepartment of Cynecology, First Affliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China.
Shaadaiti WufuerDepartment of Cynecology, First Affliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China.
Adilai MaimaitiminDepartment of Cynecology, First Affliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China.
Nuramatjan AblatSchool of Mental Health, Bengbu Medical University, Bengbu, 233030, China.
Xiaopeng LiDepartment of Cynecology, First Affliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China.
Zhifang ChenDepartment of Cynecology, First Affliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China. 645286940@qq.com.ORCID http://orcid.org/0009-0003-1621-5903

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Endometriosis (EMs) is a prevalent gynecological disorder. Ferroptosis, known as a novel type of programmed cell death, contributes to EMs pathogenesis by promoting ectopic endometrial migration. This study was designed to investigate ferroptosis-related genes as diagnostic/predictive biomarkers for EMs using bioinformatics. EMs-related datasets were obtained from the Gene Expression Omnibus (GEO) database, while ferroptosis-related data were retrieved from the FerrDb database. Through differential expression analysis (Limma), weighted gene co-expression network analysis (WGCNA), functional enrichment analysis (GO, KEGG, and Metascape), protein-protein interaction (PPI) network analysis, LASSO regression, Random Forest algorithm, immune infiltration analysis, and matrix correlation analysis, miRNA-target gene regulatory network was constructed. Identification of the potential immune-related hub genes and their regulatory miRNAs was performed to develop early diagnostic strategies, prognostic biomarkers, and therapeutic targets for EMs. Bioinformatics analysis revealed 18 differentially expressed genes (DEGs) and 36 WGCNA module genes associated with ferroptosis in EMs. Eleven core genes were identified, and a multi-gene predictive logistic regression model was established. LASSO regression and Random Forest analyses refined the selection to six genes: BRD7, OSBPL9, AGPS, NRAS, PEX12, and NCOA4, with high diagnostic value. CIBERSORT analysis showed that immune microenvironment alterations in EMs may be closely related to these six hub genes. Additionally, three key miRNAs-hsa-mir-125a-5p, hsa-mir-218-5p, and hsa-mir-124-3p-were predicted. The six immune-related hub genes (BRD7, OSBPL9, AGPS, NRAS, PEX12, and NCOA4) and three regulatory miRNAs (hsa-mir-125a-5p, hsa-mir-218-5p, and hsa-mir-124-3p) may serve as promising targets for the early diagnosis, prognostic evaluation, and therapeutic intervention of EMs.

Indexed as

DiagnosisEndometriosisFerroptosisHub geneIntegrated bioinformatics analysisMiRNAPrognosis

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