Analyzing immune cell infiltrates in skeletal muscle of infantile-onset Pompe disease using bioinformatics and machine learning.

Journal: Scientific reports
Published Date:

Abstract

Pompe disease, a severe lysosomal storage disorder, is marked by heart problems, muscle weakness, and respiratory difficulties. This study aimed to identify novel markers for infantile-onset Pompe disease by analyzing key genes and immune cells infiltrating affected skeletal muscles, building on existing research linking its progression to the immune cell infiltration. The datasets GSE38680 and GSE159062 were downloaded and differential expression genes (DEGs) were identified. Potential markers were screened using support vector machine recursive feature elimination (SVM-RFE) analysis and least absolute shrinkage and selection operator (LASSO) regression models. To analyze DEGs and immune processes, 22 immune cell types in affected tissues were examined using CIBERSORT. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed significant enrichment in the calcium signaling pathway, phosphatidylinositol signaling system, ubiquitin-mediated proteolysis and JAK-STAT signaling pathway. Machine learning identified GPNMB, CALML6 and TRIM7 as key genes. Immune cell infiltration analysis showed that the expression levels of the above three genes were closely related to immune cells in our target population under study, including T cells regulatory (Tregs), monocytes, macrophages M0, resting and activated dendritic cells, naive and memory B cells. Overall, our study results may provide new clues for exploring markers related to Pompe disease by highlighting key genes and their relationship with immune infiltration, offering insights into the disease's development. However, previous studies have shown limited evidence of significant immune cell infiltration in muscle biopsies from Pompe patients, and this lack of direct evidence necessitates further investigation and experimental validation in laboratory settings to confirm these associations.

Authors

  • Jingfei Zhang
    Department of Medical Genetics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Wuhou District, Chengdu, China.
  • Xinyu Lin
    Department of Medical Genetics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Wuhou District, Chengdu, China.
  • Lan Yin
    Department of Medical Genetics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Wuhou District, Chengdu, China.
  • Yue Song
    School of Science, Shenyang University of Technology, Shenyang 110870, China.
  • Xinglu Chen
    Department of Medical Genetics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Wuhou District, Chengdu, China.
  • Yingchuan Zhu
    Department of Medical Genetics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Wuhou District, Chengdu, China.
  • Wenhao Jiang
    YIWEI Medical Technology Co., Ltd, Room 1001, MAI KE LONG Building, Nanshan, ShenZhen, 518000, China.
  • Yilu Lu
    Department of Medical Genetics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Wuhou District, Chengdu, China.
  • Yongxin Ma