Data-driven Bayesian network learning analysis on the regulatory mechanism between carcinogenic genes and immune cells

Abstract

Background

In recent years, it has become a research focus to accurately extract key genes influencing the occurrence and development of diseases from massive genomic data and study their regulatory mechanisms. Further exploration of these large databases is beneficial for us to identify key regulatory mechanisms in the occurrence and development of diseases, providing direction and theoretical basis for subsequent experimental design and research.

Methods

By using data from The Cancer Genome Atlas (TCGA) for lung adenocarcinoma (LUAD), the immune cell content of patients was obtained through deconvolution calculations in CIBERSORTx. Combined with Bayesian network inference methods, the impact of key gene expression on the heterocellular network influencing lung cancer was analyzed.

Results

We found CD36 and ADRA1A genes were identified as two key mRNA genes influencing lung adenocarcinoma. The sensitivity analysis shows that the model performs well on both the testing set (error rate = 4%, AUC = 0.9804), the training set (error rate = 5.637%, AUC = 0.9746) and the verification set (error rate = 28.85%, AUC = 0.8689). The model has excellent predictive capabilities.

Conclusion

We found that CD36 and ADRA1A genes may influence the development of lung cancer by affecting regulatory T cell and follicular helper T cell populations. Additionally, plasma cells may affect the expression of the CD36 gene. And the mutual information calculated by the model for these two genes is also the highest, indicating their potential as tumour biomarkers. Combining the network model, it can be inferred that CD36 and ADRA1A are likely to influence the occurrence and development of the disease through follicular helper T cells and regulatory T cells.