Comprehensive Summary
This study focuses on identifying ferroptosis-related genes in small intestinal neuroendocrine neoplasms (SI-NENs) that are a subgroup of neuroendocrine tumors found in the small intestine. Two public data sets (GSE65286 and GSE98894) were used to obtain RNA sequencing data of SI-NENs patients and used towards machine learning via LASSO regression and random forest. These machine learning devices determined the core ferroptosis-related genes in SI-NENs, found to be CDCA3, CDC25A, CYP4F8, and MYB. Differential expression analysis was demonstrated in the significant downregulation of these genes compared to the control group. The study’s findings are important as ferroptosis is involved in multiple neuroendocrine tumors; the researchers believe that the machine learning is useful towards application to other cancers.
Outcomes and Implications
This study contributes to the medical field as it focuses on ferroptosis, which is not only present in SI-NENs, but also other neuroendocrine tumors. By being able to train machine learning to detect ferroptosis genes in tumors, it can be applied to other tumors as well, allowing patients to identify and relieve their tumor much more quickly. Furthermore, the machine learning technology can be applied outside of simply neuroendocrine tumors. It can be trained to target other identifiers of genes in other tumors, furthering the medical field.