Computational methods for comparison of large genomic and proteomic datasets reveal protein markers of metastatic cancer.
Large-scale genomic and proteomic analysis has provided a wealth of information on biologically relevant systems, and the ability to analyze this information is crucial to uncovering important biological relationships. However, it has proven difficult to compare large datasets from different sources due to different gene and protein identifiers assigned by individual laboratories and database systems. Here, we describe the design of a fully automated blast program (BlastPro) that facilitates rapid comparison of large protein-protein, nucleotide--nucleotide, or nucleotide--protein datasets from numerous, independent studies. Using this system, we compared several published genomic and proteomic databases for proteins that are upregulated in highly motile, metastatic tumor cells. Analysis of five independent studies comprised of greater than 1 x 10(6) genomic sequences and greater than 1,000 proteins revealed that the cytoskeletal-associated protein alpha-actinin is increased at both the mRNA and protein level in metastatic breast, prostate, and skin cancer cells. Interestingly, spatial analysis of alpha-actinin expression revealed that it is amplified 8-fold in the leading pseudopodium compared to the cell body compartment of migrating cells. These findings indicate that amplification of alpha-actinin and its localization to the leading pseudopodium are potential biomarkers of cancer progression to a more metastatic phenotype. Together, our results demonstrate that the BlastPro system can be used to compare large genomic and proteomic datasets to reveal important biological relationships including those associated with cancer progression.
Wang
Yingchun
Y
Department of Pathology and Moores Cancer Center, University of California, San Diego, Basic Science Building 1039, 9500 Gilman Drive, MC 0612, La Jolla, 92093, USA.
Hanley
Rachel
R
Klemke
Richard L
RL
D000185:Actinin / Q000235:genetics / Q000378:metabolism; D000818:Animals; D014408:Biomarkers, Tumor / Q000032:analysis*; D015153:Blotting, Western; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D019556:COS Cells; D002522:Chlorocebus aethiops; D019295:Computational Biology / Q000379:methods*; D018450:Disease Progression; D005260:Female; D023281:Genomics; D006801:Humans; D008297:Male; D008954:Models, Biological; D009362:Neoplasm Metastasis / Q000235:genetics*; D011471:Prostatic Neoplasms / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D040901:Proteomics; D011554:Pseudopodia / Q000378:metabolism; D012333:RNA, Messenger / Q000378:metabolism; D012878:Skin Neoplasms / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D012984:Software; D015854:Up-Regulation
D014408:Biomarkers, Tumor; D012333:RNA, Messenger; D000185:Actinin