Gene Expression-Based Inference of Metabolic Signatures Reveals Distinct Molecular Profiles in Right- and Left-Sided Colon Cancer

<b>Background/Objective</b>: Colon cancer, the third most diagnosed cancer worldwide, is anatomically classified into right- and left-sided colon cancers based on embryonic origin and vascular supply. The aim of this study was to investigate molecular differences between patients with right- and left-sided colon cancer. <b>Methods</b>: In this pilot study, Blood samples from right-sided (<i>n</i> = 6) and left-sided (<i>n</i> = 6) colon cancer patients, as well as healthy controls (<i>n</i> = 6), were analyzed for 92 cancer-related genes via RT-qPCR. KEGG pathway analysis was performed with ShinyGO 0.82, and gene-metabolite interactions were assessed using EnrichR and MetaboAnalyst 6.0. Additionally, patients' sociodemographic and clinical data were analyzed. <b>Results</b>: KEGG analysis revealed that p53, HIF-1, TNF, PI3K/Akt, MAPK, and Rap1 signaling pathways were enriched in right-sided colon cancer, whereas VEGF, HIF-1, MAPK, PI3K/Akt, Rap1, and Ras signaling pathways were implicated in left-sided colon cancer. In the gene-metabolite analysis, key metabolites identified in right-sided colon cancer included palmitic acid, adenosine triphosphate (ATP), glycerol, and adenosine diphosphate (ADP), associated with genes such as <i>ACSL4</i>, <i>TP53</i>, <i>MAPK14</i>, <i>FLT1</i>, <i>AURKA</i>, <i>KDR</i>, <i>ERCC3</i>, and <i>PFKL</i>. For left-sided colon cancer, glucose-6-phosphate (G6P), ATP, ADP, glycerol, and palmitoyl-CoA were key metabolites forming the basis of the gene-metabolite network, along with genes including <i>G6PD</i>, <i>PFKL</i>, <i>MAPK14</i>, <i>FLT1</i>, <i>CDK4</i>, <i>AURKA</i>, <i>MAP2K1</i>, <i>ERCC3</i>, <i>TP53</i>, <i>WEE1</i>, and <i>GPD2</i>. <b>Conclusions</b>: These findings highlight distinct molecular profiles between right- and left-sided colon cancers, particularly in pathways related to angiogenesis, apoptosis, ferroptosis, and fatty acid metabolism, which may inform therapeutic strategies.