Single-Sample Networks Reveal Intra-Cytoband Co-Expression Hotspots in Breast Cancer Subtypes

Abstract

Breast cancer is a heterogeneous disease comprising various subtypes with distinct molecular characteristics, clinical outcomes, and therapeutic responses. This heterogeneity evidences significant challenges for diagnosis, prognosis, and treatment. Traditional genomic co-expression network analyses often overlook individual-specific interactions critical for personalized medicine. In this study, we employed single-sample gene co-expression network analysis to investigate the structural and functional genomic alterations across breast cancer subtypes (Luminal A, Luminal B, Her2-enriched, and Basal-like) and compared them with normal breast tissue. We utilized RNA-Seq gene expression data to infer gene co-expression networks. The LIONESS algorithm allowed us to construct individual networks for each patient, capturing unique co-expression patterns. We focused on the top 10,000 gene interactions to ensure consistency and robustness in our analysis. Network metrics were calculated to characterize the topological properties of both aggregated and single-sample networks. Our findings reveal significant fragmentation in the co-expression networks of breast cancer subtypes, marked by a change from interchromosomal (TRANS) to intrachromosomal (CIS) interactions. This transition indicates disrupted long-range genomic communication, leading to localized genomic regulation and increased genomic instability. Single-sample analyses confirmed that these patterns are consistent at the individual level, highlighting the molecular heterogeneity of breast cancer. Despite these pronounced alterations, the proportion of CIS interactions did not significantly correlate with patient survival outcomes across subtypes, suggesting limited prognostic value. Furthermore, we identified high-degree genes and critical cytobands specific to each subtype, providing insights into subtype-specific regulatory networks and potential therapeutic targets. These genes play pivotal roles in oncogenic processes and may represent important keys for targeted interventions. The application of single-sample co-expression network analysis proves to be a powerful tool for uncovering individual-specific genomic interactions.

Keywords: breast cancer networks; co-expression networks; intra-chromosomal hotspots; intra-cytoband co-expression; single-sample networks.

Figure 1

Visualization of gene co-expression in healthy breast tissue and breast cancer subtypes (Top—10,000 higher edges). Genes are color-coded by chromosome, highlighting the distribution of interchromosomal and intrachromosomal interactions. Upper right, evaluation of CIS/TRANS and Inter/Intracytoband interactions in healthy breast tissue and breast cancer subtypes.

Figure 2

Boxplots of CIS/TRANS (A) and Inter/Intra-cytoband (B) interactions in healthy breast tissue and breast cancer subtypes across all samples (single-samples). Each point corresponds to the number of edges in a single-sample network. Notice that the distribution of control samples is much narrower than cancer subtypes.

Figure 3

Network metrics in the largest component (clustering coefficient, modularity, closeness, degree, global efficiency, density) for healthy (normal) and breast cancer subtypes across all samples. *** p-value < 0.001. NS = Non-Significant.

Figure 4

Comparison between the top 10,000 interactions from aggregated networks with the top 10,000 interactions from each single sample network using the Jaccard index.

Figure 5

Kaplan–Meier survival curves for breast cancer patients with high (blue) and low (yellow) of CIS interactions across different molecular subtypes (Luminal A, Luminal B, Her2 and Basal).

Figure 6

Distribution of High-Degree genes in Single-Sample co-expression networks across breast cancer subtypes.

Figure 7

Top 10 High-Degree genes in breast cancer subtypes: Cytoband localization across Single-Sample networks.