Page 273 - EJMO-9-3

Eurasian Journal of
Medicine and Oncology WGCNA and LASSO for osteoporosis biomarkers

and the MeSH term “osteoporosis.” Statistical significance 3. Results
was assessed by a one-tailed Fisher’s exact test and corrected
for multiple comparisons using the Benjamini–Hochberg 3.1. Identification of DEGs
method; an adjusted p<0.05 was required for retention. In the study, we first performed batch effect correction and
For CV, the Latin binomials of the prioritized herbs were normalization on the raw data extracted from the GEO
subsequently queried in the HERB database (http://herb. dataset GSE35958. The results showed that the distribution
ac.cn) to confirm their documented therapeutic relevance of expression profiles of all the samples generally converged
to OP. Only records that were manually curated from peer- after batch correction and log-normalization (Figure 2A).
reviewed publications within the last decade were retained, Subsequently, differential expression analysis was
ensuring the highest level of evidence. conducted between the control and OP groups, resulting

A B

C

Figure 2. Identification of DEGs. (A) Normalized gene expression data from the GSE35958 dataset. (B) Volcano plot of DEGs in GSE35958. Red dots
indicate upregulated genes, blue dots indicate downregulated genes, and gray dots indicate genes without significant differential expression. (C) Heatmap
of DEGs in the GSE30528 dataset.
Abbreviations: DEGs: Differentially expressed genes. Group description: N, normal group; T, test group comprising osteoporosis (OP) patients.

Volume 9 Issue 3 (2025) 265 doi: 10.36922/EJMO025240252