Identification of the transcriptome signatures and immune-inflammatory responses in postmenopausal osteoporosis

Abstract

Postmenopausal osteoporosis is the most common type of osteoporosis in women. To date, little is known about their transcriptome signatures, although biomarkers from peripheral blood mononuclear cells are attractive for postmenopausal osteoporosis diagnoses. Here, we performed bulk RNA sequencing of 206 samples (124 postmenopausal osteoporosis and 82 normal samples) and described the clinical phenotypic characteristics of postmenopausal women. We then highlighted the gene set enrichment analyses between the extreme T-score group and the heathy control group, revealing that some immune-inflammatory responses were enhanced in postmenopausal osteoporosis, with representative pathways including the mitogen-activated protein kinase (NES = 1.6, FDR <0.11) pathway and B_CELL_RECEPTOR (NES = 1.69, FDR <0.15) pathway. Finally, we developed a combined risk prediction model based on lasso-logistic regression to predict postmenopausal osteoporosis, which combined eleven genes (PTGS2, CXCL16, NECAP1, RPS23, SSR3, CD74, IL4R, BTBD2, PIGS, LILRA2, MAP3K11) and three pieces of clinical information (age, procollagen I N-terminal propeptide, β isomer of C-terminal telopeptide of type I) and provided the best prediction ability (AUC = 0.97). Taken together, this study filled a gap in the large-scale transcriptome signature profiles and revealed the close relationship between immune-inflammatory responses and postmenopausal osteoporosis, providing a unique perspective for understanding the occurrence and development of postmenopausal osteoporosis.

Keywords

• Postmenopausal osteoporosis
• Transcriptome signatures
• RNA sequencing
• Immune-inflammatory responses
• Risk prediction model