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Colorectal cancer (CRC) is highly heterogeneous leading to variable prognosis and treatment responses. Therefore, it is necessary to explore novel personalized and reproducible prognostic signatures to aid clinical decision-making. The present study combined large-scale gene expression profiles and clinical data of 1828 patients with CRC from multi-centre studies and identified a personalized gene prognostic signature consisting of 46 unique genes (called function-derived personalized gene signature [FunPGS]) from an integrated statistics and function-derived perspective. In the meta-training and multiple independent validation cohorts, the FunPGS effectively discriminated patients with CRC with significantly different prognosis at the individual level and remained as an independent factor upon adjusting for clinical covariates in multivariate analysis. Furthermore, the FunPGS demonstrated superior performance for risk stratification with respect to other recently reported signatures and clinical factors. The complementary value of the molecular signature and clinical factors was further explored, and it was observed that the composite signature called IMCPS greatly improved the predictive performance of survival estimation relative to molecular signatures or clinical factors alone. With further prospective validation in clinical trials, the FunPGS may become a promising and powerful personalized prognostic tool for stratifying patients with CRC in order to achieve an optimal systemic therapy.
Pubmed ID: 31140730
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Database for genomes that have been completely sequenced, have active research community to contribute gene-specific information, or that are scheduled for intense sequence analysis. Includes nomenclature, map location, gene products and their attributes, markers, phenotypes, and links to citations, sequences, variation details, maps, expression, homologs, protein domains and external databases. All entries follow NCBI's format for data collections. Content of Entrez Gene represents result of curation and automated integration of data from NCBI's Reference Sequence project (RefSeq), from collaborating model organism databases, and from many other databases available from NCBI. Records are assigned unique, stable and tracked integers as identifiers. Content is updated as new information becomes available.
View all literature mentionsSoftware repository for R packages related to analysis and comprehension of high throughput genomic data. Uses separate set of commands for installation of packages. Software project based on R programming language that provides tools for analysis and comprehension of high throughput genomic data.
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