Gene expression maps explain why diseases often occur together

 


Genomic and transcriptional data has greatly improved the understanding of multiple aspects of human physiology. A new paper in PNAS reports on molecular-level associations of co-occurring diseases identified by their RNA expression.

The investigators went a step further by categorizing participants by their gene expression patterns. This revealed more disease groupings, both known and potential, offering possibilities for the systematic discovery of relationships between diseases at the molecular level. This could enhance treatment approaches to such comorbidities.

Comorbidity refers to the occurrence of two or more disease conditions in the same patient or set of patients. Specific illnesses confer a higher risk for certain other conditions. These patterns of co-occurrence help predict the course and prognosis of the diseases, as well as the odds of developing specific secondary illnesses as a result of the index condition.

Shared disease-related genes may explain these co-occurrences and can be identified using network analysis. The authors of the present paper previously showed how gene expression profiles predicted disease similarity networks, uncovering known comorbidities.

However, earlier network studies failed to identify many known comorbidities. The current study used publicly available RNA-sequencing data, which offer greater sensitivity and reproducibility than earlier methods.

The investigators built a disease similarity network, which replicated and added to associations between a much larger proportion of known comorbidities. Next, they exploited differential gene expression data to build a stratified similarity network, grouping patients by their gene expression profile.

Event Name : International Molecular Biologist Awards

Website Link: molecularbiologist.org

Contact Mail ID : support@molecularbiologist.org

Nomination Link  : https://molecularbiologist.org/award-nomination/?ecategory=Awards&rcategory=Awardee

#GeneExpression #DiseaseMapping #ComorbidityResearch #MolecularBiology #Genomics #Transcriptomics #PrecisionMedicine #SystemsBiology #Bioinformatics #MedicalGenetics #DiseaseNetworks #Biomarkers

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