ArticleFrontiers in immunology2025
Identification and validation of tissue-based gene biomarkers for acute intestinal graft-versus-host disease(AIGVHD).
Article in Frontiers in immunology, 2025. 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Acute intestinal graft-versus-host disease (AIGVHD) is a common complication of allogeneic hematopoietic stem cell transplantation (allo HSCT) with a high mortality rate. The primary aim of the present study is to identify tissue-based gene biomarkers pertinent to AIGVHD, thereby facilitating early diagnosis and exploration of potential therapeutic targets. Method: The dataset was obtained from the GEO database. DEGs were identified, followed by GO and KEGG pathways analysis for the common DEGs. PPI networks and WGCNA analysis were used to identify essential genes, and correlations between critical genes and immune cell infiltration were also examined. The diagnostic efficacy of these essential genes was evaluated using ROC curves, leading to the development of 11 machine learning models based on this gene set. Furthermore, we established a mouse model of aGVHD, which was identified by clinical score, pathological analysis, flow cytometry detection of implantation rate, and immunohistochemical detection of CD4 expression. Finally, we measured the mRNA expression levels of the key genes in the mice's intestinal tissue using real-time PCR. Result: DEGs showed a marked enrichment in immune and inflammatory response pathways. Our analysis identified three key genes, FCGR3A, SERPING1, and IFITM3, which were positively associated with M1 macrophage and neutrophil infiltration. Subsequently, we developed machine learning models utilizing these three genes and found that the RF model exhibited a robust predictive capacity for AIGVHD occurrence, achieving an AUC of 0.9802 (95% CI: 0.966-0.9945). An aGVHD mouse model was also successfully created, and we discovered that the aGVHD group's mRNA expression levels of three key genes were noticeably higher than the control group's. Conclusion: In this study, we identified FCGR3A, SERPING1, and IFITM3 as tissue-based gene biomarkers for AIGVHD, highlighting their diagnostic efficacy. Furthermore, we confirmed the association of these genes with AIGVHD through investigations conducted in aGVHD mouse models.
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.