Comput. Biol. Med. - Impact of TGF-b on breast cancer from a quantitative proteomic analysis.

Tópicos

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Resumo

There has been much active research in bioinformatics to support our understanding of oncogenesis and tumor progression. Most research relies on mRNA gene expression data to identify marker genes or cancer specific gene networks. However, considering that proteins are functional molecules that carry out the biological tasks of genes, they can be direct markers of biological functions. Protein abundance data on a genome scale have not been investigated in depth due to the limited availability of high throughput protein assays. This hindrance is chiefly caused by a lack of robust techniques such as RT-PCR (real-time polymerase chain reaction). In this study, we quantified phospho-proteomes of breast cancer cell lines treated with TGF-beta (transforming growth factor beta). To discover biomarkers and observe changes in the signaling pathways related to breast cancer, we applied a protein network-based approach to generate a classifier of subnet markers. The accuracy of that classifier outperformed other network-based classification algorithms, and current feature selection and classification algorithms. Moreover, many cancer-related proteins were identified in those sub-networks. Each sub-network provides functional insights and can serve as a potential marker for TGF-beta treatments. After interpreting the roles of proteins in sub-networks with various signaling pathways, we found strong candidate proteins and various related interactions that are expected to affect breast cancer outcomes. These results demonstrate the high quality of the quantified phospho-proteomes data and show that our network construction and classification method is appropriate for an analysis of this type of data.

Resumo Limpo

much activ research bioinformat support understand oncogenesi tumor progress research reli mrna gene express data identifi marker gene cancer specif gene network howev consid protein function molecul carri biolog task gene can direct marker biolog function protein abund data genom scale investig depth due limit avail high throughput protein assay hindranc chiefli caus lack robust techniqu rtpcr realtim polymeras chain reaction studi quantifi phosphoproteom breast cancer cell line treat tgfbeta transform growth factor beta discov biomark observ chang signal pathway relat breast cancer appli protein networkbas approach generat classifi subnet marker accuraci classifi outperform networkbas classif algorithm current featur select classif algorithm moreov mani cancerrel protein identifi subnetwork subnetwork provid function insight can serv potenti marker tgfbeta treatment interpret role protein subnetwork various signal pathway found strong candid protein various relat interact expect affect breast cancer outcom result demonstr high qualiti quantifi phosphoproteom data show network construct classif method appropri analysi type data

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