Dataset Information

NanoString nCounter with the Oxford Classifier of Carcinoma of the Ovary

ABSTRACT: Using RNA-seq, we recently developed the 52-gene-based Oxford Classifier of Carcinoma of the Ovary (Oxford Classic, OxC) for molecular stratification of serous ovarian cancers (SOCs) based on the molecular profiles of their cell-of-origin in the fallopian tube epithelium. Here, we developed a 52-gene NanoString panel for the OxC to test the robustness of the classifier. We measured the expression of the 52 genes in an independent cohort of prospectively collected SOC samples (n = 150) from a homogenous cohort who were treated maximal debulking surgery and chemotherapy. We performed data mining of published expression profiles of SOCs and validated the classifier results on tissue arrays comprising 137 SOCs. We found evidence of profound non-genetic heterogeneity in SOC. ~20% of SOCs were classified as epithelial-mesenchymal-transition-high (EMT-high) tumors, that were associated with poor survival. This was independent of established prognostic factors such as tumor stage, tumor grade and residual disease after surgery (HR = 3.3, p = 0.02). Mining expression data of 593 patients revealed a significant association between the EMT scores of tumors and the estimated fraction of alternatively activated macrophages (M2) (p < 0.0001) suggesting a mechanistic link between immunosuppression and poor prognosis in EMT-high tumors. The OxC-defined EMT-high serous ovarian cancers carry particularly poor prognosis independent of established clinical parameters. These tumors are associated with high frequency of immunosuppressive macrophages suggesting a potential therapeutic target to improve clinical outcome.

ORGANISM(S): Homo sapiens

PROVIDER: GSE151335 | GEO | 2020/12/14

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Objective: Monocytes are crucially involved in inflammatory processes. In mice, different subsets of monocytes can be distinguished by the expression of Ly6C, a surface marker that is highly expressed on inflammatory monocytes (Ly6C high) and to a lesser extent on patrolling monocytes (Ly6C low). Our previous study revealed an aggravated accumulation of inflammatory Ly6C high monocytes in atherosclerotic-prone mice bearing a deficiency in suppressor of cytokine signaling (Socs)-1. In the present study, we performed a genome-wide analysis of Socs-1-dependent gene regulation in Ly6C high and Ly6C low monocytes. Methods and Results: Monocyte subsets were isolated from socs-1+/+ rag2‒/‒ ldlr–/‒ and socs-1‒/‒ rag2‒/‒ ldlr-‒/‒ mice with subsequent identification of differential regulated genes using illumina MouseWG-6 v2.0 Expression BeadChip microarrays (45,200 transcripts). Principal component analysis illustrates a distinct separation of gene expression profiles in Ly6C high and especially Ly6C low monocytes from Socs-1 deficient mice. Additionally, microarray data revealed 46 genes to be differentially regulated in a Socs-1 dependent manner in both monocytic subsets by 2-fold or more, some of which were validated by qPCR (p<0.05, n=4-6). Among them, two principal regulators of monocyte differentiation, C-X3-C chemokine receptor 1 (Cx3cr1) and colony stimulating factor 1 receptor (Csf1r), were identified to be Socs-1 dependently regulated. Especially for Csf1r a co-expression network for correlating Socs-1 dependent gene expression and protein interaction could be compiled. Furthermore, in silico analysis of a transcription factor (TF) network correlating with Socs-1 dependent mRNA expression data revealed a cluster of TF that includes Nr4a1 as well as several ets-domain proteins that may interact and thus regulate gene expression, inter alia regression of Csf1r. Conclusion: By genome-wide analysis, we could identify Cx3cr1 and Csf1r as two essential receptors mediating monocyte differentiation that are subjected to Socs-1 dependent transcription factor regulation.

Dataset Information

NanoString nCounter with the Oxford Classifier of Carcinoma of the Ovary

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets