Identification and Characterization of Cancer-Related Risk Metabolic Subpathways Reveal Their Functional Significance in Cancer

Cancer progression is accompanied by significant metabolic alterations. We developed a novel computational approach to identify cancer-related risk metabolic subpathways (CMSubpathway). By leveraging the topology of large-scale metabolic pathway gene networks, we initially identified metabolic subpathways and then refined them by taking into account pathway activity dysregulation, prognostic efficacy, and classification performance. We employed the CMSubpathway to extensively identify cancer-related metabolic subpathways across 21 cancer types. Ultimately, 12 risk metabolic subpathways were identified in six cancer types. Subsequently, the 12 overlapping genes of risk metabolic subpathways were identified as the core metabolic module genes. Utilizing the public CRISPR knockout screening datasets sourced from DepMap, we further supported our hypothesis that the essential roles of ADH5, ALDH1B1, and ALDH7A1 in breast cancer cell growth and development. The core metabolic module and its associated genes exhibited significant down-regulation at both the transcriptome and proteome levels based on data from tissues, blood, and single cells. The activity of this core metabolic module was associated with the immune infiltration levels of multiple immune cells, especially T cells. Notably, an abnormal core metabolic module was observed in CD8 T cell subtypes, with the stem-like CD8 T cell subtype showing high metabolic activity and exhaustion markers. Thus, we established a method for identifying risk metabolic subpathways in cancers, which helps to identify more precise biomarkers for cancer patients.