Identification and Characterization of Robust Hepatocellular Carcinoma Prognostic Subtypes Based on an Integrative Metabolite‐Protein Interaction Network

Abstract

Metabolite-protein interactions (MPIs) play key roles in cancer metabolism. However, our current knowledge about MPIs in cancers remains limited due to the complexity of cancer cells. Herein, the authors construct an integrative MPI network and propose a MPI network based hepatocellular carcinoma (HCC) subtyping and mechanism exploration workflow. Based on the expressions of hub proteins on the MPI network, two prognosis-distinctive HCC subtypes are identified. Meanwhile, multiple interdependent features of the poor prognostic subtype are observed, including hypoxia, DNA hypermethylation of metabolic pathways, fatty acid accumulation, immune pathway up-regulation, and exhausted T-cell infiltration. Notably, the immune pathway up-regulation is probably induced by accumulated unsaturated fatty acids which are predicted to interact with multiple immune regulators like SRC and TGFB1. Moreover, based on tumor microenvironment compositions, the poor prognostic subtype is further divided into two sub-populations showing remarkable differences in metabolism. The subtyping shows a strong consistency across multiple HCC cohorts including early-stage HCC. Overall, the authors redefine robust HCC prognosis subtypes and identify potential MPIs linking metabolism to immune regulations, thus promoting understanding and clinical applications about HCC metabolism heterogeneity.

Metabolomic and Transcriptomic Profiling Identified Significant Genes in Thymic Epithelial Tumor

“…To identify differential metabolism-related genes (DMRGs) that are significantly associated with the prognosis of TETs, we first screened out genes relevant to metabolic differences between tumor and paraneoplastic tissues from a metabolite-protein interaction network (MPI) [11]. This network is composed of 1870 metabolites and 4132 proteins (represented by the encoding genes) from data sources, including the Kyoto Encyclopedia of Genes and Genomes (KEGG) [12], Reactome [13], Human-GEM [14], and BRENDA [15].…”

Gene interaction perturbation network deciphers a high-resolution taxonomy in colorectal cancer

“…Development of molecular classification takes the plunge toward more effective interventions and provides critical insights into CRC heterogeneity( Guinney et al, 2015 ; Isella et al, 2017 ; De Sousa et al, 2013 ; Sadanandam et al, 2013 ; Marisa et al, 2013 ). However, molecular subtypes with distinctive peculiarities and outcomes are mainly identified based on the snapshot transcriptional profiles, largely ignoring the dynamic changes of gene expressions in a biological system( Guinney et al, 2015 ; Isella et al, 2017 ; De Sousa et al, 2013 ; Sadanandam et al, 2013 ; Marisa et al, 2013 ; Chen et al, 2021 ) 9 . Indeed, gene expressions are commonly variable at distinct time points or conditions, so that the subtypes based on expression data are unstable and difficult to reproduce( Chen et al, 2021 ).…”

“…However, molecular subtypes with distinctive peculiarities and outcomes are mainly identified based on the snapshot transcriptional profiles, largely ignoring the dynamic changes of gene expressions in a biological system( Guinney et al, 2015 ; Isella et al, 2017 ; De Sousa et al, 2013 ; Sadanandam et al, 2013 ; Marisa et al, 2013 ; Chen et al, 2021 ) 9 . Indeed, gene expressions are commonly variable at distinct time points or conditions, so that the subtypes based on expression data are unstable and difficult to reproduce( Chen et al, 2021 ). Conversely, biological networks remain relatively stable irrespective of time and condition, and could more reliably characterize the biological state of bulk tissues( Chen et al, 2021 ; Sahni et al, 2015 ; Li et al, 2019 ).…”

“…Indeed, gene expressions are commonly variable at distinct time points or conditions, so that the subtypes based on expression data are unstable and difficult to reproduce( Chen et al, 2021 ). Conversely, biological networks remain relatively stable irrespective of time and condition, and could more reliably characterize the biological state of bulk tissues( Chen et al, 2021 ; Sahni et al, 2015 ; Li et al, 2019 ). Previous studies have demonstrated that network analysis is well documented and applied in high-dimensional data, performing more robustly and effectively than single-gene approach( Chen et al, 2021 ; Sahni et al, 2015 ).…”

“…To tackle this issue, we introduced a rank-based individual-specific gene interaction perturbation approach( Chen et al, 2021 ), which not only leveraged gene node information but also included vital interaction information in the biological network. Gene interactions are highly conservative in normal samples but broadly perturbed in diseased tissues( Sahni et al, 2015 ).…”