Ensemble learning model for identifying the hallmark genes of NF kappa B/TNF signaling pathway in cancers

Background The nuclear factor kappa B (NF kappa B) regulatory pathways downstream of tumor necrosis factor (TNF) play a critical role in carcinogenesis. However, the widespread influence of NF kappa B in cells can result in off-target effects, making it a challenging therapeutic target. Ensemble learning is a machine learning technique where multiple models are combined to improve the performance and robustness of the prediction. Accordingly, an ensemble learning model could uncover more precise targets within the NF kappa B/TNF signaling pathway for cancer therapy. Methods In this study, we trained an ensemble learning model on the transcriptome profiles from 16 cancer types in the TCGA database to identify a robust set of genes that are consistently associated with the NF kappa B/TNF pathway in cancer. Our model uses cancer patients as features to predict the genes involved in the NF kappa B/TNF signaling pathway and can be adapted to predict the genes for different cancer types by switching the cancer type of patients. We also performed functional analysis, survival analysis, and a case study of triple-negative breast cancer to demonstrate our model's potential in translational cancer medicine. Results Our model accurately identified genes regulated by NF kappa B in response to TNF in cancer patients. The downstream analysis showed that the identified genes are typically involved in the canonical NF kappa B-regulated pathways, particularly in adaptive immunity, anti-apoptosis, and cellular response to cytokine stimuli. These genes were found to have oncogenic properties and detrimental effects on patient survival. Our model also could distinguish patients with a specific cancer subtype, triple-negative breast cancer (TNBC), which is known to be influenced by NF kappa B-regulated pathways downstream of TNF. Furthermore, a functional module known as mononuclear cell differentiation was identified that accurately predicts TNBC patients and poor short-term survival in non-TNBC patients, providing a potential avenue for developing precision medicine for cancer subtypes. Conclusions In conclusion, our approach enables the discovery of genes in NF kappa B-regulated pathways in response to TNF and their relevance to carcinogenesis. We successfully categorized these genes into functional groups, providing valuable insights for discovering more precise and targeted cancer therapeutics.

Automated summary

This study used an ensemble learning model to identify genes associated with the NF kappa B/TNF signaling pathway in cancer using transcriptome data from the TCGA database. The identified genes were found to be involved in adaptive immunity, anti-apoptosis, and cellular response to cytokine stimuli, and had detrimental effects on patient survival. This research provides valuable insights for the discovery of precise and targeted cancer therapeutics.