Expression profiles of PRKG1, SDF2L1 and PPP1R12A are predictive and prognostic factors for therapy response and survival in high-grade serous ovarian cancer.
Giuseppe BenvenutoPaola TodeschiniLara ParacchiniEnrica CaluraRobert FruscioChiara RomaniLuca BeltramePaolo MartiniAntonella RavaggiLorenzo CeppiGabriele SalesFederica DonatiPatrizia PeregoLaura ZanottiSara BallabioTommaso GrassiMartina Delle MarchetteGermana TognonEnrico SartoriMarco AdorniFranco OdicinoMaurizio D'IncalciEliana BignottiChiara RomualdiSergio MarchiniPublished in: International journal of cancer (2020)
High-grade serous ovarian cancer (HGS-EOCs) is generally sensitive to front-line platinum (Pt)-based chemotherapy although most patients at an advanced stage relapse with progressive resistant disease. Clinical or molecular data to identify primary resistant cases at diagnosis are not yet available. HGS-EOC biopsies from 105 Pt-sensitive (Pt-s) and 89 Pt-resistant (Pt-r) patients were retrospectively selected from two independent tumor tissue collections. Pathway analysis was done integrating miRNA and mRNA expression profiles. Signatures were further validated in silico on a cohort of 838 HGS-EOC cases from a published dataset. In all, 131 mRNAs and 5 miRNAs belonging to different functionally related molecular pathways distinguish Pt-s from Pt-r cases. Then, 17 out of 23 selected elements were validated by orthogonal approaches (SI signature). As resistance to Pt is associated with a short progression-free survival (PFS) and overall survival (OS), the prognostic role of the SI signature was assessed, and 14 genes associated with PFS and OS, in multivariate analyses (SII signature). The prognostic value of the SII signature was validated in a third extensive cohort. The expression profiles of SDF2L1, PPP1R12A and PRKG1 genes (SIII signature) served as independent prognostic biomarkers of Pt-response and survival. The study identified a prognostic molecular signature based on the combined expression profile of three genes which had never been associated with the clinical outcome of HGS-EOC. This may lead to early identification, at the time of diagnosis, of patients who would not greatly benefit from standard chemotherapy and are thus eligible for novel investigational approaches.
Keyphrases
- high grade
- free survival
- prognostic factors
- low grade
- genome wide
- end stage renal disease
- chronic kidney disease
- squamous cell carcinoma
- randomized controlled trial
- stem cells
- ejection fraction
- dna methylation
- machine learning
- big data
- data analysis
- room temperature
- radiation therapy
- mesenchymal stem cells
- transcription factor
- deep learning
- rectal cancer
- genome wide analysis
- ionic liquid
- smoking cessation