Dataset Information

A core matrisome gene signature predicts cancer outcome.

ABSTRACT:

Background

Accumulating evidence implicates the tumour stroma as an important determinant of cancer progression but the protein constituents relevant for this effect are unknown. Here we utilised a bioinformatics approach to identify an extracellular matrix (ECM) gene signature overexpressed in multiple cancer types and strongly predictive of adverse outcome.

Methods

Gene expression levels in cancers were determined using Oncomine. Geneset enrichment analysis was performed using the Broad Institute desktop application. Survival analysis was performed using KM plotter. Survival data were generated from publically available genesets.

Results

We analysed ECM genes significantly upregulated across a large cohort of patients with ovarian, lung, gastric and colon cancers and defined a signature of nine commonly upregulated genes. Each of these nine genes was considerably overexpressed in all the cancers studied, and cumulatively, their expression was associated with poor prognosis across all data sets. Further, the gene signature expression was associated with enrichment of genes governing processes linked to poor prognosis, such as EMT, angiogenesis, hypoxia, and inflammation.

Conclusions

Here we identify a nine-gene ECM signature, which strongly predicts outcome across multiple cancer types and can be used for prognostication after validation in prospective cancer cohorts.

SUBMITTER: Yuzhalin AE

PROVIDER: S-EPMC5808042 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Publications

A core matrisome gene signature predicts cancer outcome.

Yuzhalin Arseniy E AE Urbonas Tomas T Silva Michael A MA Muschel Ruth J RJ Gordon-Weeks Alex N AN

British journal of cancer 20180123 3

<h4>Background</h4>Accumulating evidence implicates the tumour stroma as an important determinant of cancer progression but the protein constituents relevant for this effect are unknown. Here we utilised a bioinformatics approach to identify an extracellular matrix (ECM) gene signature overexpressed in multiple cancer types and strongly predictive of adverse outcome.<h4>Methods</h4>Gene expression levels in cancers were determined using Oncomine. Geneset enrichment analysis was performed using t ...[more]

PMID: 29360819

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Project description:Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the US. Despite multiple large-scale genetic sequencing studies, identification of predictors of patient survival remains challenging. We performed a comprehensive assessment and integrative analysis of large-scale gene expression datasets, across multiple platforms, to enable discovery of a prognostic gene signature for patient survival in pancreatic cancer. PDAC RNA-Sequencing data from The Cancer Genome Atlas was stratified into Survival+ (>2-year survival) and Survival-(<1-year survival) cohorts (n = 47). Comparisons of RNA expression profiles between survival groups and normal pancreatic tissue expression data from the Gene Expression Omnibus generated an initial PDAC specific prognostic differential expression gene list. The candidate prognostic gene list was then trained on the Australian pancreatic cancer dataset from the ICGC database (n = 103), using iterative sampling based algorithms, to derive a gene signature predictive of patient survival. The gene signature was validated in 2 independent patient cohorts and against existing PDAC subtype classifications. We identified 707 candidate prognostic genes exhibiting differential expression in tumor versus normal tissue. A substantial fraction of these genes was also found to be differentially methylated between survival groups. From the candidate gene list, a 5-gene signature (ADM, ASPM, DCBLD2, E2F7, and KRT6A) was identified. Our signature demonstrated significant power to predict patient survival in two distinct patient cohorts and was independent of AJCC TNM staging. Cross-validation of our gene signature reported a better ROC AUC (? 0.8) when compared to existing PDAC survival signatures. Furthermore, validation of our signature through immunohistochemical analysis of patient tumor tissue and existing gene expression subtyping data in PDAC, demonstrated a correlation to the presence of vascular invasion and the aggressive squamous tumor subtype. Assessment of these genes in patient biopsies could help further inform risk-stratification and treatment decisions in pancreatic cancer.

Project description:BackgroundDuring pregnancy, the mouse mammary ductal epithelium branches and grows into the surrounding stroma, requiring extensive extracellular matrix (ECM) and tissue remodelling. It therefore shows parallels to cancer invasion. We hypothesised that similar molecular mechanisms may be utilised in both processes, and that assessment of the stromal changes during pregnancy-associated branching may depict the stromal involvement during human breast cancer progression.MethodsImmunohistochemistry (IHC) was employed to assess the alterations within the mouse mammary gland extracellular matrix during early pregnancy when lateral branching of the primary ductal epithelium is initiated. Primary mouse mammary fibroblasts from three-day pregnant and age-matched non-pregnant control mice, respectively, were 3D co-cultured with mammary epithelial cells to assess differences in their abilities to induce branching morphogenesis in vitro. Transcriptome analysis was performed to identify the underlying molecular changes. A signature of the human orthologues of the differentially expressed matrisome RNAs was analysed by Kaplan-Meier and multi-variate analysis in two large breast cancer RNA datasets (Gene expression-based Outcome for Breast cancer Online (GOBO) und Kaplan-Meier Plotter), respectively, to test for similarities in expression between early-pregnancy mouse mammary gland development and breast cancer progression.ResultsThe ECM surrounding the primary ductal network showed significant differences in collagen and basement membrane protein distribution early during pregnancy. Pregnancy-associated fibroblasts (PAFs) significantly enhanced branching initiation compared to age-matched control fibroblast. A combined signature of 64 differentially expressed RNAs, encoding matrisome proteins, was a strong prognostic indicator of distant metastasis-free survival (DMFS) independent of other clinical parameters. The prognostic power could be significantly strengthened by using only a subset of 18 RNAs (LogRank P ≤ 1.00e-13; Hazard ratio (HR) = 2.42 (1.8-3.26); p = 5.61e-09). The prognostic power was confirmed in a second breast cancer dataset, as well as in datasets from ovarian and lung cancer patients.ConclusionsOur results describe for the first time the early stromal changes that accompany pregnancy-associated branching morphogenesis in mice, specify the early pregnancy-associated molecular alterations in mouse mammary fibroblasts, and identify a matrisome signature as a strong prognostic indicator of human breast cancer progression, with particular strength in oestrogen receptor (ER)-negative breast cancers.

Dataset Information

A core matrisome gene signature predicts cancer outcome.

Background

Methods

Results

Conclusions

Publications

A core matrisome gene signature predicts cancer outcome.

Similar Datasets

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