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

It is well known that tumors originating from the same tissue have differentprognosis and sensitivity to treatments. Over the last decade, cancer genomicsconsortia like the Cancer Genome Atlas (TCGA) have been generating thousands ofcross-sectional data, for thousands of human primary tumors originated fromvarious tissues. Thanks to that public database, it is today possible toanalyze a broad range of relevant information such as gene sequences,expression profiles or metabolite footprints, to capture tumor molecularheterogeneity and improve patient stratification and clinical management. Tothis aim, it is common practice to analyze datasets grouped into clusters basedon clinical observations and/or molecular features. However, the identificationof specific properties of each cluster that may be effectively targeted bytherapeutic drugs still represents a challenging task. We define a method togenerate an activity score for the metabolic reactions of different clusters ofpatients based on their transcriptional profile. This approach reduces thenumber of variables from many genes to few reactions, by aggregatingtranscriptional information associated to the same enzymatic reaction accordingto gene-enzyme and enzyme-reaction rules. We also applied the methodology to adataset of 244 RNAseq transcriptional profiles taken from patients withcolorectal cancer (CRC). CRC samples are typically divided into two sub-types:(i) tumors with microsatellite instability (MSI), associated withhyper-mutation and with CpG island methylation phenotype, and (ii)microsatellite stable (MSS) tumors, typically endowed with chromosomalinstability. We report some key differences in the central carbon metabolism ofthe two clusters. We also show how the method can be used to describe themetabolism of individual patients and cluster them exclusively based onmetabolic features.

Metabolic enrichment through functional gene rules