A novel 20-gene prognostic score in pancreatic adenocarcinoma.
Seçil Demirkol CanlıEge DedeoğluMuhammad Waqas AkbarBarış KüçükkaradumanMurat İşbilenÖzge Şükrüoğlu ErdoğanSeda Kılıç ErciyasHülya YazıcıBurçak VuralAli Osmay GürePublished in: PloS one (2020)
Pancreatic ductal adenocarcinoma (PDAC) is among the most lethal cancers. Known risk factors for this disease are currently insufficient in predicting mortality. In order to better prognosticate patients with PDAC, we identified 20 genes by utilizing publically available high-throughput transcriptomic data from GEO, TCGA and ICGC which are associated with overall survival and event-free survival. A score generated based on the expression matrix of these genes was validated in two independent cohorts. We find that this "Pancreatic cancer prognostic score 20 -PPS20" is independent of the confounding factors in multivariate analyses, is dramatically elevated in metastatic tissue compared to primary tumor, and is higher in primary tumors compared to normal pancreatic tissue. Transcriptomic analyses show that tumors with low PPS20 have overall more immune cell infiltration and a higher CD8 T cell/Treg ratio when compared to those with high PPS20. Analyses of proteomic data from TCGA PAAD indicated higher levels of Cyclin B1, RAD51, EGFR and a lower E-cadherin/Fibronectin ratio in tumors with high PPS20. The PPS20 score defines not only prognostic and biological sub-groups but can predict response to targeted therapy as well. Overall, PPS20 is a stronger and more robust transcriptomic signature when compared to similar, previously published gene lists.
Keyphrases
- free survival
- genome wide
- single cell
- genome wide identification
- high throughput
- small cell lung cancer
- rna seq
- electronic health record
- poor prognosis
- copy number
- dna damage
- genome wide analysis
- big data
- dna methylation
- data analysis
- type diabetes
- transcription factor
- cardiovascular events
- randomized controlled trial
- tyrosine kinase
- cell death
- gene expression
- coronary artery disease
- young adults
- cell proliferation
- bioinformatics analysis
- artificial intelligence
- cell cycle arrest
- type iii
- childhood cancer