Systemic Dyslipidemia Drives Pan-Cancer Prognosis via Epigenetic Remodeling: A Hybrid Pi-Score Analysis

Figures

• 

  Figure 1. Global landscape of epigenetic links across clinical comparisons. Faceted tile plots summarizing the mean HyPiPi scores across cancer types. (Left) The male cohort shows broad connectivity between lipid variables (TGY, TCH, HDL) and metabolic cancers, particularly Liver Hepatocellular Carcinoma (LIHC) and Kidney Renal Clear Cell Carcinoma (KIRC). (Right) The female cohort exhibits distinct patterns, with stronger associations observed in hormone-dependent cancer types such as Breast Cancer (BRCA). The color intensity represents the strength of the shared epigenetic link derived from the Hybrid Pi-score integration.

• 

  Figure 2. Multidimensional epigenetic landscape of lipid variables across pan-cancer. A grid of radar plots visualizing the Hybrid Pi-scores of lipid-associated CpGs across 32 cancer types arranged radially. (Top Row) Male cohort results separated by three conditions: Normal vs. Tumor (Left), Alive vs. Dead (Middle), and Stage I/II vs. III/IV (Right). (Bottom Row) Female cohort results for the same three conditions. The radial distance represents the score magnitude, highlighting condition-specific epigenetic clusters associated with dyslipidemia. Note the prominent expansion of the radar area in the ‘Dead’ group for Triglycerides (TGY).

• 

  Figure 3. Directional consistency check between population and cancer cohorts. Scatter plots comparing the KoGES Hybrid Pi-score (x-axis) and TCGA Pi-score (y-axis). (Left) Male cohort. (Right) Female cohort. Points in the first (top-right) and third (bottom-left) quadrants indicate directional consistency. This confirms that epigenetic alterations associated with dyslipidemic profiles (e.g., high TGY, low HDL) in the general population align with the methylation patterns driving poor cancer prognosis.

• 

  Figure 4. Ranking of the top 20 Master CpG sites. (Top) Lollipop charts ranking the top 20 CpG sites appearing most frequently across datasets for Males (Left) and Females (Right). The dot size/color represents the mean HyPiPi score. (Bottom) A Venn diagram illustrating the overlap (or lack thereof) of these top 20 Master CpGs between males and females.

• 

  Figure 5. Cluster analysis of cancer types and lipid variables. (Left) Male cohort. (Right) Female cohort. Bubble plots visualize the connectivity between cancer types (x-axis) and lipid variables (y-axis) via the top 20 CpGs. Bubble size indicates the count of shared markers, and color intensity represents the mean score. Heatmaps (bottom of each panel) show the specific methylation levels of these Master CpGs across cancer types, revealing distinct patterns for ‘Good’ (HDL) vs. ‘Bad’ (TGY) lipids.

• 

  Figure 6. Network topology of lipid variables and cancer types. Network graphs illustrating the connectivity between lipid variables (orange nodes) and cancer types (sky-blue nodes). (Left) The male network shows TGY as a central super-hub connecting to gastrointestinal and renal cancers. (Right) The female network displays connectivity driven by TCH and HDL, linking to reproductive cancers. Edges represent shared epigenetic markers from the top 20 list.