ArticleBioMed research international2025
Bioinformatics Analysis of Hypoxia-Related Mechanisms in Endometriosis: DDR2 as a Potential Diagnostic and Therapeutic Biomarker.
Article in BioMed research international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Emerging Non-Pharmacological Approaches in Endometriosis: Mechanistic Insights into Phototherapy, Hyperthermia, and Acupuncture-Literature Review.Journal of clinical medicine · 2026Review
- Bioinformatics Analysis of Hypoxia-Related Mechanisms in Endometriosis: DDR2 as a Potential Diagnostic and Therapeutic Biomarker.BioMed research international · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Endometriosis is a chronic gynecological disorder characterized by the presence of endometrial-like tissue outside the uterine cavity, causing chronic pain and infertility. Hypoxia plays a significant role in the progression of endometriosis. Methods: We performed bioinformatics analysis on GEO datasets to identify differentially expressed genes (DEGs) in endometriosis, using weighted gene coexpression network analysis (WGCNA)and GeneCards for hypoxia-related genes. Machine learning models identified key hub genes. CCK-8, EdU, and Transwell assays assessed cell proliferation, migration, and invasion. Molecular docking was performed to investigate the interactions between the drug and the protein. Results: In the GEO dataset analysis, 2834 DEGs were identified. Using WGCNA, a green module strongly correlated with endometriosis was identified. Intersecting this module with the hypoxia-related genes resulted in the selection of 449 key genes. Machine learning models, including support vector machines (SVMs), were employed to identify hypoxia-related DEGs with significant predictive value. LASSO and SVM-RFE were used to refine this list, ultimately selecting six hub genes: DDR2, ENO3, ESM1, NMBR, PRKAB1, and PRPF19. Validation with an independent dataset confirmed DDR2 as a promising diagnostic biomarker. Functional assays demonstrated that DDR2 knockdown significantly inhibited cell proliferation, migration, and invasion in the endometriosis cell lines VK2/E6E7 and 12Z. DDR2, a receptor tyrosine kinase, mediates extracellular matrix remodeling and cell invasion under hypoxia. By interacting with collagen and HIFs, DDR2 activates pathways that promote MMP secretion, angiogenesis, and migration, facilitating endometriotic cell progression in the hypoxic microenvironment. Molecular docking identified key amino acids near DDR2's binding pocket that form hydrophobic interactions, hydrogen bonds, and Conclusion: DDR2 is a key hypoxia-related gene in endometriosis and a promising diagnostic and therapeutic biomarker.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.