Leveraging insights into cancer metabolism-a symposium report

Effects of N6-methyladenosine modification on metabolic reprogramming in digestive tract tumors

N6-methyladenosine (m6A), the most abundant RNA modification within cells, participates in various biological and pathological processes, including self-renewal, invasion and proliferation, drug resistance, and stem cell characteristics. The m6A methylation plays a crucial role in tumors by regulating multiple RNA processes such as transcription, processing, and translation. Three protein types are primarily involved in m6A methylation: methyltransferases (such as METTL3, METTL14, ZC3H13, and KIAA1429), demethylases (such as FTO, ALKBH5), and RNA-binding proteins (such as the family of YTHDF, YTHDC1, YTHDC2, and IGF2BPs). Various metabolic pathways are reprogrammed in digestive tumors to meet the heightened growth demands and sustain cellular functionality. Recent studies have highlighted the extensive impact of m6A on the regulation of digestive tract tumor metabolism, further modulating tumor initiation and progression. Our review aims to provide a comprehensive understanding of the expression patterns, functional roles, and regulatory mechanisms of m6A in digestive tract tumor metabolism-related molecules and pathways. The characterization of expression profiles of m6A regulatory factors and in-depth studies on m6A methylation in digestive system tumors may provide new directions for clinical prediction and innovative therapeutic interventions.

Mitochondrial transporter expression patterns distinguish tumor from normal tissue and identify cancer subtypes with different survival and metabolism

Transporters of the inner mitochondrial membrane are essential to metabolism. We demonstrate that metabolism as represented by expression of genes encoding SLC25 transporters differentiates human cancers. Tumor to normal tissue expression ratios for clear cell renal cell carcinoma, colon adenocarcinoma, lung adenocarcinoma and breast invasive carcinoma were found to be highly significant. Affinity propagation trained on SLC25 gene expression patterns from 19 human cancer types (6825 TCGA samples) and normal tissues (2322 GTEx samples) was used to generate clusters. They differentiate cancers from normal tissues. They also indicate cancer subtypes with survivals distinct from the total patient population of the cancer type. Probing the kidney, colon, lung, and breast cancer clusters, subtype pairs of cancers were identified with distinct prognoses and differing in expression of protein coding genes from among 2080 metabolic enzymes assayed. We demonstrate that SLC25 expression clusters facilitate the identification of the tissue-of-origin, essential to efficacy of most cancer therapies, of CUPs (cancer-unknown-primary) known to have poor prognoses. Different cancer types within a single cluster have similar metabolic patterns and this raises the possibility that such cancers may respond similarly to existing and new anti-cancer therapies.