Related Experiment Video
Updated: Jul 2, 2025
Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
Published on: June 18, 2020
Identification of platelet-related subtypes and diagnostic markers in pediatric Crohn's disease based on WGCNA and
Dadong Tang1, Yingtao Huang2, Yuhui Che1
1Clinical Medical College, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Insights
Researchers identified five key diagnostic markers for pediatric Crohn's disease (PCD), a condition with increasing incidence. These markers aid in developing predictive models and understanding distinct PCD subtypes for improved early diagnosis and personalized treatment strategies.
Area of Science:
- Genomics and Bioinformatics
- Immunology
- Pediatric Gastroenterology
Background:
- Pediatric Crohn's disease (PCD) incidence is rising globally.
- Early diagnosis and treatment are challenging due to PCD's heterogeneity.
- There is a need for novel diagnostic markers and molecular subtypes to improve PCD prognosis.
Purpose of the Study:
- To identify novel diagnostic markers for PCD.
- To discover molecular subtypes of PCD.
- To enhance diagnostic and prognostic capabilities for PCD patients.
Main Methods:
- Weighted gene co-expression network analysis (WGCNA) and differential analysis were used to identify candidate genes.
- Five machine learning algorithms screened for pivotal diagnostic markers.
- Consensus clustering identified PCD subtypes, followed by pathway and immune infiltration analysis.
Main Results:
- Five key diagnostic markers (GNA15, PIK3R3, PLEK, SERPINE1, STAT1) were identified.
- A high-performing nomogram was developed based on these markers.
- Two distinct platelet-related PCD subtypes with differential gene expression and immune infiltration were discovered.
Conclusions:
- Five promising diagnostic markers and a predictive nomogram for PCD were successfully developed.
- The identification of distinct PCD subtypes deepens the understanding of pathogenic mechanisms.
- These findings offer potential for improved early diagnosis and personalized treatment of PCD.
Background:
The incidence of pediatric Crohn's disease (PCD) is increasing worldwide every year. The challenges in early diagnosis and treatment of PCD persist due to its inherent heterogeneity. This study's objective was to discover novel diagnostic markers and molecular subtypes aimed at enhancing the prognosis for patients suffering from PCD.
Methods:
Candidate genes were obtained from the GSE117993 dataset and the GSE93624 dataset by weighted gene co-expression network analysis (WGCNA) and differential analysis, followed by intersection with platelet-related genes. Based on this, diagnostic markers were screened by five machine learning algorithms. We constructed predictive models and molecular subtypes based on key markers. The models were evaluated using the GSE101794 dataset as the validation set, combined with receiver operating characteristic curves, decision curve analysis, clinical impact curves, and calibration curves. In addition, we performed pathway enrichment analysis and immune infiltration analysis for different molecular subtypes to assess their differences.
Results:
Through WGCNA and differential analysis, we successfully identified 44 candidate genes. Following this, employing five machine learning algorithms, we ultimately narrowed it down to five pivotal markers: GNA15, PIK3R3, PLEK, SERPINE1, and STAT1. Using these five key markers as a foundation, we developed a nomogram exhibiting exceptional performance. Furthermore, we distinguished two platelet-related subtypes of PCD through consensus clustering analysis. Subsequent analyses involving pathway enrichment and immune infiltration unveiled notable disparities in gene expression patterns, enrichment pathways, and immune infiltration landscapes between these subtypes.
Conclusion:
In this study, we have successfully identified five promising diagnostic markers and developed a robust nomogram with high predictive efficacy. Furthermore, the recognition of distinct PCD subtypes enhances our comprehension of potential pathogenic mechanisms and paves the way for future prospects in early diagnosis and personalized treatment.
More Related Videos
11:18Real-time Imaging of Heterotypic Platelet-neutrophil Interactions on the Activated Endothelium During Vascular Inflammation and Thrombus Formation in Live Mice
Published on: April 2, 2013
04:37Comprehensive Analysis of Procoagulant Platelets Exhibiting Features of Necrosis, Apoptosis and Platelet Activation
Published on: May 23, 2025
