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Machine learning identifies inflammation-related diagnostic biomarkers for primary myelofibrosis with clinical
Rumeng Li1, Qinmei Han1, Tian Zeng1
1Department of Haematology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China.
Abstract:
Primary myelofibrosis (PMF) is a heterogeneous bone marrow disorder, and substantial evidence indicates the involvement of inflammatory mediators in its progression. However, a diagnostic model based on inflammation-related genes has not yet been established. The aim of this study was to identify specific inflammation-related genes (IRGs) with potential value in myelofibrosis diagnosis and risk prediction. Transcriptomic data from the Gene Expression Omnibus (GEO) database were analysed to identify inflammation-related differentially expressed genes (DEGs). Machine learning approaches, including the least absolute shrinkage and selection operator (LASSO) and random forest, were used to select hub genes. A nomogram was constructed and validated externally using independent GEO datasets and local sequencing data. Immune cell infiltration and functional enrichment were also investigated. HBEGF, TIMP1 and PSEN1 show significant differences in expression between normal individuals and those with PMF. A nomogram based on three genes was established to assess the risk of PMF in healthy individuals. The ROC curve revealed that the three hub genes have outstanding diagnostic value for this disease (AUC = 0.994; 95% CI: 0.985-1.000); the results were subsequently validated in an external validation set (AUC = 0.807; 95% CI: 0.723-0.891), and a sequencing dataset from the First Affiliated Hospital of Zhejiang University (AUC = 0.982; 95% CI: 0.841-1). Enrichment analyses implicated cancer-related and immune pathways, and the model genes correlated significantly with immune cell infiltration and function. We developed and validated a robust three gene diagnostic model for PMF based on inflammation-related genes, offering a noninvasive molecular tool with potential clinical utility for auxiliary diagnosis.
Insights
A new diagnostic model using three inflammation-related genes (IRGs) can help detect primary myelofibrosis (PMF). This model shows high accuracy in identifying PMF, offering a potential noninvasive tool for early diagnosis and risk assessment.
Area of Science:
- Hematology
- Oncology
- Genomics
Background:
- Primary myelofibrosis (PMF) is a complex bone marrow disorder where inflammation plays a key role in its progression.
- Currently, no established diagnostic model effectively utilizes inflammation-related genes for PMF diagnosis or risk stratification.
Purpose of the Study:
- To identify specific inflammation-related genes (IRGs) with diagnostic and prognostic value for primary myelofibrosis (PMF).
- To develop and validate a predictive model for PMF using machine learning approaches.
Main Methods:
- Transcriptomic data from the Gene Expression Omnibus (GEO) database were analyzed to identify differentially expressed genes (DEGs).
- Machine learning algorithms, including LASSO and random forest, were employed to select key hub genes.
- A nomogram model was constructed using selected genes and validated using independent datasets and local sequencing data.
Main Results:
- Three genes (HBEGF, TIMP1, PSEN1) were identified with significant expression differences between PMF patients and healthy individuals.
- A nomogram based on these three genes demonstrated high diagnostic accuracy (AUC=0.994) in the initial cohort and was validated externally (AUC=0.807, AUC=0.982).
- Enrichment analyses revealed associations with cancer and immune pathways, and model genes correlated with immune cell infiltration and function.
Conclusions:
- A robust three-gene diagnostic model for PMF based on inflammation-related genes has been developed and validated.
- This model offers a promising noninvasive molecular tool for the auxiliary diagnosis and risk assessment of primary myelofibrosis.
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