ArticleReproductive sciences (Thousand Oaks, Calif.)2026
Identifying Predictive Biomarkers and Immune Infiltration Features in Endometriosis.
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.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Endometriosis is a common gynecological disorder in which inflammatory and immune responses play a crucial role in its development and progression. This study aimed to identify potential inflammation-related biomarkers for the diagnosis and therapeutic monitoring of endometriosis. Differentially expressed genes (DEGs) between endometriosis and control groups were identified using the limma R package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed with the clusterProfiler R package to explore functional categories and biological processes associated with the DEGs. Immune cell proportions were estimated using CIBERSORT and xCell, followed by correlation analysis between gene expression and immune cell ratios. Data were obtained from the GEO datasets GSE104948 and GSE116626. Under the criteria |fold-change (FC)| > 1 and p-value < 0.05, a total of 357 DEGs were identified, including 136 down-regulated and 221 up-regulated genes. GO analysis revealed enriched biological processes, such as regulation of cell-cell adhesion mediated by cadherin, cell-cell adhesion mediated by cadherin, and response to interleukin-6. Functional enrichment included extracellular matrix structural constituents and protein-binding activities. KEGG analysis highlighted pathways related to protein digestion and absorption. Three inflammation-related genes, PGI2 synthase (PTGIS), E26 transformation-specific homologous factor (EHF), and collagen type X alpha 1 (COL10A1), were identified as potential biomarkers for endometriosis. In 12Z endometriotic epithelial cells, PTGIS knockdown reduced viability, enhanced apoptosis, and impaired migration and invasion, whereas PTGIS overexpression had the opposite effects. Collectively, this study suggests that PTGIS, EHF, and COL10A1 may serve as valuable predictors for the progression of endometriosis.
Indexed as
Identifiers
41872379What 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.