Identification of highly connected and differentially expressed gene subnetworks in metastasizing endometrial cancer.
Kanthida KusonmanoMari K HalleElisabeth WikErling A HoivikCamilla KrakstadKaren K MaulandIngvild L TangenAnna BergHenrica M J WernerJone TrovikAnne M ØyanKarl-Henning KallandInge JonassenHelga B SalvesenKjell PetersenPublished in: PloS one (2018)
We have identified nine highly connected and differentially expressed gene subnetworks between aggressive primary tumors and metastatic lesions in endometrial carcinomas. We implemented a novel pipeline combining gene set and network approaches, which here allows integration of protein-protein interactions and gene expression data. The resulting subnetworks are significantly associated with disease progression across tumor stages from complex atypical hyperplasia, primary tumors to metastatic lesions. The nine subnetworks include genes related to metastasizing features such as epithelial-mesenchymal transition (EMT), hypoxia and cell proliferation. TCF4 and TWIST2 were found as central genes in the subnetwork related to EMT. Two of the identified subnetworks display statistically significant association to patient survival, which were further supported by an independent validation in the data from The Cancer Genome Atlas data collection. The first subnetwork contains genes related to cell proliferation and cell cycle, while the second contains genes involved in hypoxia such as HIF1A and EGLN3. Our findings provide a promising context to elucidate the biological mechanisms of metastasis, suggest potential prognostic markers and further identify therapeutic targets. The pipeline R source code is freely available, including permutation tests to assess statistical significance of the identified subnetworks.
Keyphrases
- cell cycle
- epithelial mesenchymal transition
- genome wide
- cell proliferation
- genome wide identification
- endometrial cancer
- gene expression
- dna methylation
- bioinformatics analysis
- genome wide analysis
- copy number
- small cell lung cancer
- squamous cell carcinoma
- electronic health record
- endothelial cells
- big data
- transforming growth factor
- signaling pathway
- transcription factor
- pi k akt
- case report
- high grade
- deep learning
- risk assessment
- papillary thyroid
- free survival
- lymph node metastasis