Discovery and Validation of Immune Infiltration-related Genes for the Prognosis of Osteoporosis

Hualiang  Xu; Furong  Xu; Lihong  Chen; Renchun  Wu; Hongqing  Ge; Aiguo  Li

doi:10.18502/ijaai.v24i2.18149

Hualiang Xu Department of Orthopedics, Guangzhou Red Cross Hospital of Jinan University, Guangzhou, Guangdong, China
Furong Xu Department of Nursing, Guangzhou Red Cross Hospital of Jinan University, Guangzhou, Guangdong, China
Lihong Chen Department of Orthopedics, Guangzhou Red Cross Hospital of Jinan University, Guangzhou, Guangdong, China
Renchun Wu Department of Orthopedics, The Third People's Hospital of Bijie City, Bijie, Guizhou, China
Hongqing Ge Department of Orthopedics, Guangzhou Red Cross Hospital of Jinan University, Guangzhou, Guangdong, China
Aiguo Li Department of Orthopedics, Guangzhou Red Cross Hospital of Jinan University, Guangzhou, Guangdong, China

DOI: https://doi.org/10.18502/ijaai.v24i2.18149

Keywords: Bioinformatics; Differentially expressed gene; Immune infiltration; Osteoporosis; Prognosis

Abstract

Osteoporosis (OP), a widespread musculoskeletal disorder characterized by fragile bone fractures, has seen increasing attention regarding immune infiltration-related genes. These genes show significant predictive value in solid tumor prognosis and are now being explored for their roles in musculoskeletal diseases. This study identified osteoporosis-associated differentially expressed immune genes (OP-DEGs) by analyzing the overlap between OP-differentially expressed genes and immune genes.

To elucidate the functional implications of these genes, pathway enrichment analysis was conducted using Gene Ontology and KEGG databases. Additionally, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were employed to explore underlying mechanisms. A competitive endogenous RNA (ceRNA) network was constructed for critical OP-related immune genes, and immune infiltration analysis investigated micro-environmental characteristics. The diagnostic effectiveness of OP was evaluated using ROC curves. Finally, RT-PCR determined the expression levels of 15 key OP-related immune genes in OP and control groups.

The study identified 29 OP-DEGs. Extensive bioinformatics analysis pinpointed 15 key genes that could serve as potential biomarkers for OP diagnosis. RT-PCR results revealed significantly increased expression of VEGFA, HMOX1, RARA, CXCL10, hsa-miR-129-2-3p, OIP5-AS1, and HCG18 in the OP group compared to controls.

Our findings suggest that these immune-related genes may predict OP prognosis and offer new perspectives for early prevention and intervention strategies. The identification of specific immune genes involved in OP development highlights their potential as therapeutic targets for further investigation.