TITLE:
Identification of Common Gene Characteristics and Pathways between SARA-CoV-2 and Femoral Head Necrosis by Bioinformatics and Systems Biology Methods
AUTHORS:
Huanning Yang, Yimin Zhu, Linzeng Qi, Hongliang Wang
KEYWORDS:
SARS-CoV-2, Femoral Head Necrosis, Differentially Expressed Genes, Protein-Protein Interaction (PPI), Drug Molecule
JOURNAL NAME:
Journal of Biosciences and Medicines,
Vol.13 No.7,
July
16,
2025
ABSTRACT: The global pandemic of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has had a huge impact on public health in countries around the world since late 2019, seriously threatening the health of people in all countries. Necrosis of the femoral head is one of the major sequelae of coronavirus infection with SARS-CoV-2, but studies reporting the association between necrosis of the femoral head and SARS-CoV-2 are rare. Therefore, we performed transcriptome analyses to investigate the common pathways and shared differentially expressed genes (DEGs) between osteonecrosis of the femoral head and SARS-CoV-2, aiming to shed light on the interaction between the two conditions. In this paper, we used three RNA-seq datasets (GSE74089, GSE157103, GSE152418) from the Gene Expression Omnibus (GEO) to obtain reciprocally differentially expressed genes (DEGs) between patients with osteonecrosis of the femoral head and SARS-CoV-2 infection, and identified a total of 36 common DEGs in these three datasets. We utilized various combinatorial statistical methods and bioinformatics tools to build a protein-protein interaction network (PPI), and then identified hub genes and important modules from this PPI network. In addition, we performed functional analysis based on ontology terminology and pathway analysis, and found some close associations between femoral head necrosis patients and SARS-CoV-2 infections. Transcription factor-gene interactions and DEGs-miRNAs coregulatory networks were also identified in the dataset. In addition, we conducted experiments to verify the accuracy of the pivotal gene diagnosis by introducing a new dataset (GSE171110).