Identication and validation of cell senescence biomarkers in idiopathic pulmonary hypertension via integrated
Wenzhang Lu1, Jiayi Xu1, Yanrong Chen2
1Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China; Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China.
Background:
Idiopathic pulmonary hypertension (IPAH) is a rare and severe disease that affects the pulmonary vasculature. As the diagnosis of IPAH requires invasive right heart catheterization surgery, early detection of this condition is notoriously challenging. Therefore, it is of utmost importance to investigate biomarkers present in peripheral blood that could aid physicians in the early identification and management of IPAH.
Method:
We speculate that cellular senescence may be involved in the occurrence and development of IPAH through various pathways. In this study, we utilized integrated transcriptome analyses and machine learning-based approach to develop a diagnostic model for IPAH cell senescence. To select genetic features, we employed two machine learning algorithms: the Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF). Additionally, we validated our findings through both external data sets and qRT-PCR experiments.
Results:
The resulting diagnostic nomogram was able to identify five important biomarkers that can aid in the diagnosis of IPAH, including TNFRSF1B, CCL16, GCLM, IL15, and SOD1. These genes are primarily associated with the immune system, as well as with cell senescence and apoptosis.
Conclusion:
Our study demonstrates the utility of machine learning algorithms in making accurate diagnoses of IPAH, providing clinicians with a more directed approach to the diagnosis and treatment of this disease.
