Predicting microRNA modulation in human prostate cancer using a simple String IDentifier (SID1.0)

J Biomed Inform. 2011 Aug;44(4):615-20. doi: 10.1016/j.jbi.2011.02.006. Epub 2011 Feb 18.

Abstract

To make faster and efficient the identification of mRNA targets common to more than one miRNA, and to identify new miRNAs modulated in specific pathways, a computer program identified as SID1.0 (simple String IDentifier) was developed and successfully applied in the identification of deregulated miRNAs in prostate cancer cells. This computationally inexpensive Fortran program is based on the strategy of exhaustive search and specifically designed to screen shared data (target genes, miRNAs and pathways) available from PicTar and DIANA-MicroT 3.0 databases. As far as we know this is the first software designed to filter data retrieved from available miRNA databases. SID1.0 takes advantage of the standard Fortran intrinsic functions for manipulating text strings and requires ASCII input files. In order to demonstrate SID1.0 applicability, some miRNAs expected from the literature to associate with cancerogenesis (miR-125b, miR-148a and miR-141), were randomly identified as main entries for SID1.0 to explore matching sequences of mRNA targets and also to explore KEGG pathways for the presence of ID codes of targeted genes. Besides genes and pathways already described in the literature, SID1.0 has proven to useful for predicting other genes involved in prostate carcinoma. These latter were used to identify new deregulated miRNAs: miR-141, miR-148a, miR-19a and miR-19b. Prediction data were preliminary confirmed by expression analysis of the identified miRNAs in androgen-dependent (LNCaP) and independent (PC3) prostate carcinoma cell lines and in normal prostatic epithelial cells (PrEC).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cell Line, Tumor
  • Computational Biology / methods*
  • Databases, Genetic
  • Humans
  • Male
  • MicroRNAs / genetics*
  • MicroRNAs / metabolism*
  • Prostatic Neoplasms / genetics*
  • Prostatic Neoplasms / metabolism*
  • Reproducibility of Results
  • Software*

Substances

  • MicroRNAs