Evidence map›Paper›PMID 41073551›Full record

ArticleScientific reports2025

Evaluation of biomarkers and immune microenvironment of gestational diabetes mellitus evidence from omics data and machine learning.

Ming Chen, Xueyan Cao, Sijia Huang, Jiaqi Yang, Junze Bao, Min Su

Abstract read
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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Review
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

6 authors.

Ming Chen *Department of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, Jiangsu, China.
Xueyan Cao *Department of Obstetrics and Gynecology, Nantong Rici Hospital Affiliated to Yangzhou University, Nantong, 226010, Jiangsu, China.
Sijia Huang *Department of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, Jiangsu, China.
Jiaqi YangDepartment of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, Jiangsu, China.
Junze BaoSchool of Nantong University, Nantong, 226001, Jiangsu, China.
Min SuDepartment of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, Jiangsu, China. sumin_nt@126.com.

Funding

Jiangsu Provincial Research Hospital YJXYY202204-YSC02Scientific research project of Jiangsu Provincial Health Commission H2023001Scientific research project of Nantong Health Commission MS2023018
6 · The paper itself

Abstract

This study aimed to identify core genes of Gestational diabetes mellitus (GDM) and explore its immune microenvironment. Using the limma package, we were able to identify differentially expressed genes (DEGs) between GDM and normal placental tissue. Weighted gene co-expression network analysis (WGCNA) and various machine-learning algorithms were subsequently employed to identify core genes that may influence the occurrence of GDM. Analysis was used to evaluate the diagnostic usefulness of the core genes by using the receiver operating characteristic (ROC) analysis method. In gene enrichment analysis, we utilized the CIBERSORT algorithm to assess the immune cell composition in various samples, followed by the application of the Wilcoxon test to evaluate the immune cell content in diabetes samples during pregnancy. Conversely, analysis was done on the relationship between immune cells and core genes. Finally, we used RUN PCA to integrate different data sets and cluster cells with different functions. 527 up-regulated genes were found, and 690 down-regulated were found. Combining the results of the algorithms and ROC analysis, we identified CCL3/FAM3B/IL1RL1 as potential diagnostic biomarkers for GDM, and validated their diagnosibility using an external dataset. The results of the functional enrichment analysis indicated that core genes are associated with immune cells. When compared to pregnant women who were having diabetes, there was a considerable rise in the percentage of macrophages in immunological cells. The expression of three core genes in different cells of different samples showed that the expression of CCL3 was increased in macrophages of GDM. Cell communication analysis showed that macrophage communication was significantly active in GDM, and CCL signal was significantly increased, which mainly played a significant role through CCL3-CCR1 pathway. The findings suggest that CCL3 closely related to GDM occurrence and progression, represent new GDM marker, and that the modification of immune microenvironment plays a significant role in the occurrence of GDM.

Indexed as

BiomarkersDiabetes, GestationalMachine LearningAlgorithmsChemokine CCL3FemaleGene Expression ProfilingGene Regulatory NetworksHumansPlacentaPregnancyROC CurveBiomarkersChemokine CCL3Core genesGestational diabetes mellitusImmune microenviromentMacrophages

Identifiers

PMID41073551
PMCPMC12514063

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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.