Abstract
We previously reported the CINSARC signature as a prognostic marker for metastatic events in soft tissue sarcomas, breast carcinomas and lymphomas through genomic instability, acting as a major factor for tumor aggressiveness. In this study, we used a published resource to investigate CINSARC enrichment in poor outcome-associated genes at pan-cancer level and in 39 cancer types. CINSARC outperformed more than 15,000 defined signatures (including cancer-related), being enriched in top-ranked poor outcome-associated genes of 21 cancer types, widest coverage reached among all tested signatures. Independently, this signature demonstrated significant survival differences between risk-groups in 33 published studies, representing 17 tumor types. As a consequence, we propose the CINSARC prognostication as a general marker for tumor aggressiveness to optimize the clinical managements of patients.
Highlights
From the first report of gene expression quantification method by Schena et al in 19951, to RNA sequencing (RNA-seq), extensively used nowadays by international consortia to decipher transcriptomic abnormalities[2, 3], gene expression has become an essential tool in cancer research
In 2010, we defined a set of 67 genes as a predictor for metastatic events in sarcomas with complex genetics[24], with a better prognosis compared to the standard FNCLCC (Fédération Nationale des Centres de Lutte Contre le Cancer) grading system based on tumor differentiation, mitotic index and necrosis[25]
Using the Gene Set Enrichment Analysis (GSEA) algorithm, we demonstrated that CINSARC, among 15,499 signatures established by various methods, is strongly enriched in prognostic genes at pan-cancer level, with high sensitivity (>95%) and low false-negative rate (
Summary
From the first report of gene expression quantification method by Schena et al in 19951, to RNA sequencing (RNA-seq), extensively used nowadays by international consortia to decipher transcriptomic abnormalities[2, 3], gene expression has become an essential tool in cancer research. Among the many available gene expression databases, the two most used are Gene Expression Omnibus from the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/geo)[13] and ArrayExpress from the European Bioinformatics Institute (http://www.ebi.ac.uk/arrayexpress)[14] These resources, by gathering results from microarrays and RNA-seq experiments, provide an easy access to millions of cancer-related transcriptomic profiles (cell lines, primary tumors and metastases/relapses). In 2010, we defined a set of 67 genes as a predictor for metastatic events in sarcomas with complex genetics[24], with a better prognosis compared to the standard FNCLCC (Fédération Nationale des Centres de Lutte Contre le Cancer) grading system based on tumor differentiation, mitotic index and necrosis[25] These genes were identified based on differential expression analyses with three different classifiers: FNCLCC grade, genomic alteration number and chromosomal instability signature[19]. We reported that no randomly generated signature was a better predictor than CINSARC29
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