English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Abnormal metabolism is an emerging hallmark of cancer. Cancer cells utilizeboth aerobic glycolysis and oxidative phosphorylation (OXPHOS) for energyproduction and biomass synthesis. Understanding the metabolic reprogramming incancer can help design therapies to target metabolism and thereby to improveprognosis. We have previously argued that more malignant tumors are usuallycharacterized by a more modular expression pattern of cancer-associated genes.In this work, we analyzed the expression patterns of metabolism genes in termsof modularity for 371 hepatocellular carcinoma (HCC) samples from the CancerGenome Atlas (TCGA). We found that higher modularity significantly correlatedwith glycolytic phenotype, later tumor stages, higher metastatic potential, andcancer recurrence, all of which contributed to poorer overall prognosis. Amongpatients that recurred, we found the correlation of greater modularity withworse prognosis during early to mid-progression. Furthermore, we developedmetrics to calculate individual modularity, which was shown to be predictive ofcancer recurrence and patients' survival and therefore may serve as aprognostic biomarker. Our overall conclusion is that more aggressive HCCtumors, as judged by decreased host survival probability, had more modularexpression patterns of metabolic genes. These results may be used to identifycancer driver genes and for drug design.

Modularity of the metabolic gene network as a prognostic biomarker for hepatocellular carcinoma