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Aydin et al. J Transl Genet Genom. 2025;9:406-26 https://dx.doi.org/10.20517/jtgg.2025.108 Page 416

Figure 3. Principal Component Analysis (PCA) plots showing the discrimination of test and control groups for each dataset:
(A) GSE38417, (B) GSE70955, and (C) GSE109178. Each panel presents two complementary PCA visualizations: (left) the
distribution of individual samples and (right) the contribution of variables (network biomarkers) to the principal components.
PCA was performed using limma-normalized gene expression matrices for each dataset, ensuring variance-stabilized and directly
comparable values within each cohort. Left plots (PCA of individuals): Samples are projected onto the first two principal
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components (Dim 1 and Dim 2), with colors representing the cos values, which quantify the quality of representation of each
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sample in the PCA space. Higher cos values indicate that a sample is well explained by the selected components, whereas
lower values reflect weaker representation. Separation patterns between healthy and DMD samples demonstrate the
discriminatory capacity of the network signatures. Right plots (variable factor maps): Arrows correspond to individual biomarker
genes, and their directions and lengths indicate the strength and orientation of each gene’s contribution to the PCA axes. The color
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gradient encodes the cos values, highlighting the variables that most strongly drive the variance structure. Longer vectors with high
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cos values represent biomarkers with greater influence on sample separation. The correlation circle delineates the
multidimensional structure of gene-gene relationships within each dataset. DMD: Duchenne muscular dystrophy.
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mutations . Therefore, there is an urgent unmet need for the development of new therapeutics that can
overcome these limitations, potentially through the integration of multi-omics data to identify all molecular
players involved in disease progression.

The costs and development time of new treatments are major limitations for researchers and patients
struggling with rare diseases. This challenge can be significantly alleviated by state-of-the-art systems,
advances in biomedicine, and drug-repositioning approaches using drugs already on the market. Drug