ArticleFrontiers in genetics2020
Potential of Immune-Related Genes as Biomarkers for Diagnosis and Subtype Classification of Preeclampsia.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled 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.
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- L-Arginine and L-Citrulline for Prevention and Treatment of Pre-Eclampsia: A Systematic Review and Meta-Analysis.BJOG : an international journal of obstetrics and gynaecology · 2025Pooled it
- Integration of single-cell sequencing and transcriptomic analysis to identify novel diagnostic biomarkers for pre-eclampsia.BMC pregnancy and childbirth · 2026Article
- A placental DNA methylation score associated with early-onset preeclampsia and maternal vascular malperfusion pathology.Frontiers in epigenetics and epigenomics · 2026Article
- Demystifying the impact of prenatal tobacco exposure on the placental immune microenvironment: Avoiding the tragedy of mending the fold after death.Journal of cellular and molecular medicine · 2023Article
- Potential biomarkers for late-onset and term preeclampsia: A scoping review.Frontiers in physiology · 2023Article
- Association of theFrontiers in molecular biosciences · 2023Article
- Ethanol promoting the upregulation of C-X-C Motif Chemokine Ligand 1（CXCL1） and C-X-C Motif Chemokine Ligand 6（CXCL6） in models of early alcoholic liver disease.Bioengineered · 2022Article
- Characterizing and correcting immune dysfunction in non-tuberculous mycobacterial disease.Frontiers in immunology · 2022Article
- Development of a prediction model on preeclampsia using machine learning-based method: a retrospective cohort study in China.Frontiers in physiology · 2022Article
- Identification of Immune-Related Biomarkers for Sciatica in Peripheral Blood.Frontiers in genetics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivePreeclampsia is the main cause of maternal mortality due to a lack of diagnostic biomarkers and effective prevention and treatment. The immune system plays an important role in the occurrence and development of preeclampsia. This research aimed to identify significant immune-related genes to predict preeclampsia and possible prevention and control methods.
methodsDifferential expression analysis between normotensive and PE pregnancies was performed to identify significantly changed immune-related genes. Generalized linear model (GLM), random forest (RF), and support vector machine (SVM) models were established separately to screen the most suitable biomarkers for the diagnosis of PE among these significantly changed immune-related genes. The consensus clustering method was used to divide the PE cases into several subgroups to explore the function of the significantly changed immune-related genes in PE.
resultsThirteen significantly changed immune-related genes were obtained by the differential expression analysis. RF was the best model and was used to select the four most important explanatory variables (CRH, PI3, CCL18, and CCL2) to diagnose PE. A nomogram model was constructed to predict PE based on these four variables. The decision curve analysis (DCA) and clinical impact curves revealed that PE patients could significantly benefit from this nomogram. Consensus clustering analysis of the 13 differentially expressed immune-related genes (DIRGs) was used to identify 3 subgroups of PE pregnancies with different clinical outcomes and immune cell infiltration.
conclusionOur study identified four immune-related genes to predict PE and three subgroups of PE with different clinical outcomes and immune cell infiltration. Future studies on the three subgroups may provide direction for individualized treatment of PE patients.
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.