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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.

SUBMITTER: Chen D

PROVIDER: S-EPMC8425875 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and aimsTumor microenvironment plays an essential role in cancer development and progression. Cancer immunotherapy has become a promising approach for the treatment of hepatocellular carcinoma (HCC). We aimed to analyze the HCC immune microenvironment characteristics to identify immune-related genetic changes.MethodsKey immune-relevant genes (KIRGs) were obtained through integrating the differentially expressed genes of The Cancer Genome Atlas, immune genes from the Immunology Database and Analysis Portal, and immune differentially expressed genes determined by single-sample gene set enrichment analysis scores. Cox regression analysis was performed to mine therapeutic target genes. A regulatory network based on KIRGs, transcription factors, and immune-related long non-coding RNAs (IRLncRNAs) was also generated. The outcomes of risk score model were validated in a testing cohort and in clinical samples using tissue immunohistochemistry staining. Correlation analysis between risk score and immune checkpoint genes and immune cell infiltration were investigated.ResultsIn total, we identified 21 KIRGs, including programmed cell death-1 (PD-1) and cytotoxic T-lymphocyte associated protein 4 (CTLA4), and found IKBKE, IL2RG, EDNRA, and IGHA1 may be equally important to PD-1 or CTLA4. Meanwhile, KIRGs, various transcription factors, and IRLncRNAs were integrated to reveal that the NRF1-AC127024.5-IKBKE axis might be involved in tumor immunity regulation. Furthermore, the immune-related risk score model was established according to KIRGs and key IRLncRNAs, and verified more obvious discriminating power in the testing cohort. Correlation analysis indicated TNFSF4 , LGALS9 , KIAA1429 , IDO2, and CD276 were closely related to the risk score, and CD4 T cells, macrophages, and neutrophils were the primary immune infiltration cell types.ConclusionsOur results highlight the importance of immune genes in the HCC microenvironment and further unravel the underlying molecular mechanisms in the development of HCC.

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

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