Differential correlation network analysis of rectal transcriptomes reveals cystic fibrosis-related disturbance.
Jan Krzysztof NowakCyntia J SzymańskaJaroslaw WalkowiakPublished in: Pharmacogenomics (2022)
Background: Intestinal pathology in cystic fibrosis (CF) remains mechanistically underexplored. Aim: We hypothesized that differential correlation network analysis of expression data would reveal hub genes of CF-related disturbance in the large bowel. Materials & methods: Transcriptomes of 29 rectal tissue samples were accessed at ArrayExpress (E-GEOD-15568 by Stanke et al. ). Results: We identified 279 transcript pairs differentially correlating in CF and controls, including: ESRRA and RPL18 (r CF = 0.55; r controls = -0.68; p adj = 1.60 × 10 -100 ), SRP14 and FAU (r CF = -0.69; r controls = 0.48; p adj = 2.99 × 10 -90 ), SRP14 and GDI2 (r CF = -0.34; r controls = 0.60; p adj = 1.05 × 10 -78 ). The genes related to membrane protein targeting (q = 8.34 × 10 -14 ) and one cluster was clearly linked to male infertility. Conclusion: FAU , SRP14 and GDI2 may be involved in a compensatory protein trafficking mechanism in CF rectum, highlighting their potential therapeutic value.
Keyphrases
- cystic fibrosis
- pseudomonas aeruginosa
- network analysis
- lung function
- genome wide
- single cell
- poor prognosis
- type diabetes
- bioinformatics analysis
- metabolic syndrome
- big data
- dna methylation
- electronic health record
- chronic obstructive pulmonary disease
- adipose tissue
- small molecule
- insulin resistance
- deep learning
- cancer therapy
- protein protein
- drug induced
- data analysis