2023/11/12 by Radwa Adel, Adel, Radwa, Erçan E. Kuruoğlu +1
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Data Analysis #FOS: Biological sciences #FOS: Physical sciences #Gene Regulatory Network Analysis #Gene expression and cancer classification #Molecular Networks (q-bio.MN) #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2311.06747
openalex publication_date 2023/11/12 · openalex created_date 2023/11/15 · openalex updated_date 2026/07/28
Complex gene interactions play a significant role in cancer progression, driving cellular behaviors that contribute to tumor growth, invasion, and metastasis. Gene co-expression networks model the functional connectivity between genes under various biological conditions. Understanding the system-level evolution of these networks in cancer is critical for elucidating disease mechanisms and informing the development of targeted therapies. While previous studies have primarily focused on structural differences between cancer and normal cell co-expression networks, this study applies graph frequency analysis to cancer transcriptomic signals defined on gene co-expression networks, highlighting the graph spectral characteristics of cancer systems. Using a range of graph frequency filters, we showed that cancer cells display distinctive patterns in the graph frequency content of their gene transcriptomic signals, effectively distinguishing between cancer types and stages. The transformation of the original gene feature space into the graph spectral space captured more intricate cancer properties, as validated by significantly higher F-statistic scores for graph frequency-filtered gene features compared to those in the original space.