Predicting the risk of primary Sjogren's syndrome with key N7-methylguanosine-related genes: A novel XGBoost model.

ABSTRACT:

Objectives

N7-methylguanosine (m7G) plays a crucial role in mRNA metabolism and other biological processes. However, its regulators' function in Primary Sjögren's Syndrome (PSS) remains enigmatic.

Methods

We screened five key m7G-related genes across multiple datasets, leveraging statistical and machine learning computations. Based on these genes, we developed a prediction model employing the extreme gradient boosting decision tree (XGBoost) method to assess PSS risk. Immune infiltration in PSS samples was analyzed using the ssGSEA method, revealing the immune landscape of PSS patients.

Results

The XGBoost model exhibited high accuracy, AUC, sensitivity, and specificity in both training, test sets and extra-test set. The decision curve confirmed its clinical utility.

SUBMITTER: Xie H 

PROVIDER: S-EPMC11128997 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature