Machine Learning-Based Integration Identifies Plasma Cells-Related Gene Signature ST6GAL1 in Idiopathic Pulmonary Fibrosis and Its Pan-Cancer Analysis

Abstract

Background: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and fibrotic disease that occurs primarily in older adults, and it is associated with a poor prognosis. Plasma cells are an essential effector cell in IPF development and progression. The aim of this study is to identify biomarkers associated with plasma cells in IPF and initially explore their potential role in the tumor immune microenvironment. Methods: The plasma cells marker genes were extracted via single cell RNA sequencing (scRNA-seq) analysis. Then, gene co-expression networks were generated to determine the hub genes most relevant to the IPF state and plasma cells infiltration level. Moreover, the differential expressed genes (DEGs) screening were conducted based on the bulk RNA-seq and microarray data. In addition, a machine learning-based integrative procedure for constructing a concordance plasma cells-related gene signature (PCRGS) were developed. Furthermore, a core gene in the PCRGS was identified and validated through experiments. Finally, the molecular docking procedure and pan-cancer analysis for the core gene were produced. Results: The established PCRGS based on the seven most potent genes was found to be an independent prognostic factor for overall survival and exhibited superior and robust performance when compared with conventional clinical features and 22 published signatures. Additionally, the PCRGS effectively distinguish IPF patients and normal subjects. Eventually, ST6GAL1 was selected as the core gene and its localization in the plasma cells as well as over-expression in the lungs of bleomycin-injured mice were validated. Three drugs related to ST6GAL1 were predicted, and the results showed that quercetin and ST6GAL1 might have a more stable binding conformation based on the molecular docking procedure. Furthermore, the results of a pan-cancer analysis confirmed a tight association between the ST6GAL1 expression and the prognosis of various tumors. Conclusions: PCRGS is an effective prognostic and predictive biomarker in IPF associated with immune responses, among which ST6GAL1 is a potential therapeutic target in diverse cancers and may act as a pivotal gene connecting IPF and cancer in terms of plasma cells immune effects.