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  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Development Of An Immune-related Diagnostic Predictive Model For Oral Lichen Planus.

Development of an immune-related diagnostic predictive model for oral lichen planus.

Jiamin Bian1, Jiayu Yan1,2,3, Chu Chen2

  • 1School of Stomatology, North Sichuan Medical College, Nanchong, Sichuan, China.

Medicine
|March 15, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

Researchers developed a new diagnostic model for oral lichen planus (OLP) using immune system data. This model, based on 5 key immune genes, aids in early OLP detection and potentially prevents cancer progression.

Area of Science:

  • Immunology
  • Genomics
  • Computational Biology

Background:

  • Oral lichen planus (OLP) is a chronic inflammatory condition with a known risk of malignant transformation.
  • Immune system dysregulation is implicated in the pathogenesis of OLP.
  • Early diagnosis and intervention are crucial for managing OLP and preventing cancer.

Purpose of the Study:

  • To develop a diagnostic prediction model for OLP utilizing immunopathogenesis.
  • To identify key immune-related biomarkers for early OLP detection.
  • To provide insights for preventing OLP-associated malignancy.

Main Methods:

  • Analysis of publicly available OLP gene expression datasets (GSE52130, GSE38616).
  • Assessment of immune cell infiltration using MCPcounter and ssGSEA algorithms.

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  • Differential gene expression analysis, gene set enrichment analysis, and intersection with immunology databases (ImmPort).
  • Construction of a diagnostic model using protein-protein interaction networks and LASSO regression.

    • Main Results:

    • Significant immune cell infiltration observed in OLP compared to controls.
    • Gene set enrichment analysis highlighted pathways related to keratinization, antibacterial, and immune responses.
    • Identification of 774 differentially expressed genes, including 65 differentially expressed immunologically relevant genes (DEIRGs).
    • A 5-hub gene (BST2, RNASEL, PI3, DEFB4A, CX3CL1) diagnostic model for OLP was established with good diagnostic accuracy.

    Conclusions:

    • The developed immune-related diagnostic model shows promise for early OLP detection.
    • The 5 identified hub genes are potential biomarkers correlating with immune infiltration.
    • This research offers a novel approach to understanding and diagnosing OLP, potentially aiding in cancer prevention.