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.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.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.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.