Multiomics Integration Reveals Coexpression Regulatory Networks in Osteoporosis and Osteosarcoma

Abstract

Background

Osteoporosis and osteosarcoma impose a substantial and rising global health burden, yet their transcriptomic landscapes have not been systematically compared. We performed a multiomics characterization integrating bulk transcriptomics, coexpression network analysis, immune deconvolution, single-cell RNA sequencing, and diagnostic classifier construction.

Methods

Bulk transcriptomic profiling was performed using GSE56815 (circulating blood monocytes, n = 80; 40 low-BMD/40 high-BMD) and the TARGET-OS database (n = 88 osteosarcoma tumor samples; DNA methylation n = 86; copy number n = 81). Differential expression was assessed by Welch′s t-test with Benjamini–Hochberg correction. Coexpression modules were identified by hierarchical clustering of the Top 2000 most variable probes with dynamic tree cutting. A logistic regression classifier was constructed from the Top 20 differentially expressed probes using fivefold-stratified cross-validation. Single-cell RNA sequencing of an osteosarcoma tumor specimen (GEO accession GSM9030790) was processed with SVD-based dimensionality reduction and K-means clustering (k = 8; n = 3658 cells passing QC). In vitro validation was conducted by qRT-PCR in MC3T3-E1 osteoblast precursor cells and RAW264.7 osteoclast precursor cells.

Results

Differential expression analysis identified 4347 probe sets at nominal p < 0.05, of which 604 remained significant after Benjamini–Hochberg correction. Eight coexpression modules were identified; module M0 showed a weak positive association with low-BMD status (r = 0.184), which did not reach statistical significance. Six hub genes—SP7/Osterix, RUNX2, BMP2, WNT5A, DKK1, and SOST—were examined by qRT-PCR: SP7, RUNX2, BMP2, and WNT5A showed significant downregulation (p < 0.01), whereas DKK1 showed significant upregulation (p < 0.01) and SOST showed a nonsignificant trend toward upregulation under osteoporosis-simulating conditions. The diagnostic classifier achieved a moderate AUC of 0.693 (fivefold CV range: 0.48–0.91), with the lower bound falling below the random classifier baseline (AUC = 0.5), indicating limited discriminative capacity in some data partitions. scRNA-seq resolved eight transcriptionally distinct cell clusters in the osteosarcoma specimen (3658 cells passing QC).

Conclusions

This integrative multiomics study provides a comprehensive transcriptomic comparison of osteoporosis and osteosarcoma, identifies BMD-associated coexpression modules with experimentally validated hub genes, and establishes a reusable analytical framework for biomarker discovery in metabolic and malignant bone disease. However, the weak module-trait correlations, subthreshold classifier performance in some cross-validation folds, and the use of circulating blood monocytes to study bone-specific gene expression warrant cautious interpretation of the findings.