The results of our study demonstrate the potential of using bioinformatics analysis and machine learning algorithms in identifying novel biomarkers for the diagnosis of preeclampsia. By combining different computational techniques, we were able to identify two genes (F13A1 and SCCPDH, AUC: 0.90 and 0.88, respectively) that have high diagnostic value for PE patients. The nomogram constructed using these two hub genes could be used as a diagnostic tool for PE.
The identification of F13A1 and SCCPDH as potential biomarkers for PE is an important finding. Coagulation Factor XIII A Chain (F13A1) is a serine protease inhibitor that has been shown to be involved in various physiological processes, including the regulation of blood coagulation and fibrinolysis 16. Previously, Yu Shangguan 17 and Epiney et al. 18 also found that F13A1 is differentially expressed (decreased) in placental tissues of individuals with preeclampsia compared to controls. Our findings align with their results and show that the expression of F13A1 is significantly reduced in the placental tissues of PE patients, indicating that F13A1 may play a role in the underlying physiology of PE.
Saccharine dehydrogenase (SCCPDH) is an enzyme involved in the metabolism of lysine, an essential amino acid. SCCPDH and F13A1 are both involved in the response to elevated platelet cytosolic Ca2 + 19. The function of SCCPDH and its role in PE are not well understood. Some studies have demonstrated in animal models that SCCPDH is involved in the regulation of chronic stress, which contributes to anxiety depression 20. There is evidence to suggest that anxiety and depression are prevalent in women with preeclampsia and that these mental health conditions can have an impact on the onset and course of the disease 21,22. Research in this area is ongoing, and it would be interesting to further explore the possible complex mechanisms behind anxiety depression and coagulation regulation in PE and whether SCCPHD is directly involved.
In addition to individual genes, our study also identified pathways affected in PE. The relationship between the TGF-β signaling pathway and preeclampsia is not fully understood, but TGF-β signaling is believed to play a role in the development of the condition23. Preeclampsia is thought to result from abnormalities in the maternal blood vessels that supply the uterus and placenta. TGF-β is a signaling molecule that regulates various physiological processes, including inflammation and the immune response, and it has been shown to be involved in the development of hypertension 24,25 and renal dysfunction 26. Some studies have suggested that TGF-β signaling may contribute to the pathogenesis of preeclampsia by disrupting the normal functioning of maternal blood vessels, leading to increased blood pressure and decreased blood flow to the uterus and placenta 27,28. Therefore, more research is needed to fully understand the role of TGF-β signaling in the development of preeclampsia. In the context of preeclampsia, the role of gap junctions in the development of this condition is not well understood, and more research is needed to determine their exact contribution. However, there is some evidence to suggest that gap junctions may play a role in the regulation of blood flow and blood pressure, which are key features of preeclampsia 29,30.
Our study also highlights the importance of dysregulated immune cells in the pathogenesis of PE. The results showed that various immune cells were dysregulated in PE patients, which is consistent with previous studies that have suggested that an abnormal immune response is a key contributor to the development of PE 31,32. This highlights the need for further studies to investigate the underlying mechanisms of immune cell dysregulation in PE, which could lead to the development of new therapies for this disease. In future studies, it would be important to perform additional bioinformatics analysis and machine learning on larger and more diverse datasets to further refine and validate our results. In addition, functional studies should be performed to gain a deeper understanding of the biological mechanisms underlying the candidate biomarkers. These studies have the potential to significantly improve our understanding of preeclampsia and to lead to new and more effective strategies for its prevention and treatment.
There are some limitations to our study that need to be considered. First, the sample size of the dataset used in this study was relatively small, which may limit the generalizability of the results. Second, the dataset only included gene expression data from the placenta, and it is possible that the results would be different if other tissues, such as peripheral blood samples, were analyzed. Finally, the results of this study need to be validated in independent patient populations before they can be used for clinical purposes. In addition, it is important to note that these findings are preliminary and need to be validated in further studies. Further research is needed to confirm the accuracy and reliability of these biomarkers and to explore their potential clinical applications.