Application of graph models to the identification of tran-scriptomic oncometabolic pathways in human hepatocellular carcinoma

Whole tissue transcriptomic analyses have been helpful to characterize molecular subtypes of hepatocellular carcinoma (HCC). Metabolic subtypes of human HCC have been defined, yet whether these different metabolic classes are clinically relevant or derive in actionable cancer vulnerabilities is still an unanswered question. Publicly available gene sets or gene signatures have been used to infer functional changes through gene set enrichment methods. However, me-tabolism-related gene signatures are poorly coexpressed when applied to a biological context. Here, we apply a simple method to infer highly consistent signatures using graph models. Using The Cancer Genome Atlas Liver Hepatocellular cohort (LIHC), we describe the main metabolic clusters and their relationship with commonly used molecular classes, and with the presence of TP53 or CTNNB1 driver mutations. We find similar results in our validation cohort, the LIRI-JP cohort. We describe how previously described metabolic subtypes could not have therapeutic rel-evance due to their overall downregulation when compared to non-tumoral liver, and identify N-Glycan, Mevalonate and Sphingolipid biosynthetic pathways as the hallmark of the oncogenic shift of the use of Acetyl-coenzyme A in HCC metabolism. Finally, using DepMap data, we demonstrate metabolic vulnerabilities in HCC cell lines. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was funded by the Agencia Estatal de Salud (AES, PI20 01663) (to J.A.), Ministerio de Ciencia, Innovacion y Universidades MICINN-Agencia Estatal de Investigacion integrado en el Plan Estatal de Investigacion Cientifica y Tecnica y Innovacion, cofinanciado con Fondos FEDER PID2019-104878RB-100/AEI/10.13039/501100011033 (to M.A.A.), and the Fundacion Echebano (Pamplona, Spain) (to J.A.). S.B. is supported by a PhD award from the Fundacion para la Investigacion Medica Aplicada (FIMA). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Raw data used in this study was openly available at Xenabrowser portal: https://xenabrowser.net/datapages/?cohort=TCGA%20Liver%20Cancer%20(LIHC)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443 And at EGA portal: https://dcc.icgc.org/projects/LIRI-JP I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The scripts to adapt public signatures to an expression matrix using graph models are available in GitHub (https://github.com/unav-hcclab/gsadapt/blob/6b7e7ed083882c22b64c01b9318cb57087d45c40/gsadapt_pipeline)