• Combined analysis of gene expression for prognosis prediction of human colorectal cancer
  • Fahimeh Fattahi,1 Somayeh Vafaei,2 Jafar Kiani,3 Zahra Madjd,4 Mohammad Najafi ,5,*
    1. Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
    2. Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
    3. Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
    4. Oncopathology Research Center, Iran University of Medical Sciences, Tehran, Iran.
    5. Department of Biochemistry, Iran University of Medical Sciences, Tehran, Iran


  • Introduction: Colorectal cancer (CRC) is the third leading cause of cancer death in human. CRC can often be cured if found early. CRC has various genetic and epigenetic alterations. Today, treatment of the patients is defined based on the cancer staging and genetic biomarkers. Evidence shows that Long non-coding RNAs (LncRNAs) can serve as potential biomarkers for cancer prognosis. lncRNAs are differentially expressed in various tissues and have important functions in cellular processes. A systems biology approach is used to detect novel lncRNAs in CRC.
  • Methods: Gene Expression Omnibus (GEO) microarray data repositories GSE41011, GSE62392, GSE63624, GSE77953 and GSE48248 for patients with CRC (n=231) and controls (n=20) were used in this study. Furthermore, protein information related to the colorectal cancer tissues were extracted from ProteomeXchange databases (PXD000851, PXD005735, PXD002137, PXD002082). A protein-protein interaction (PPI) gene network was obtained and enriched using Gene Ontology (GO). Several plugins were used to identify the gene characteristics and their roles in signaling pathways. Finally, high-evidence hubs were selected and annotated with microRNAs prediction. The mRNA-miRNA bipartite networks were also performed through the lncRNA databases.
  • Results: In this study, we show candidate genes are expressed at significantly elevated levels in CRC tissues compared to the normal tissues. Analysis of hub mRNA-miRNA genes can help to predict several novel lncRNAs which have not been previously reported in CRC. We thus could have a functional prediction of them in the cell because the lncRNAs are selected from the interaction with mRNA-miRNA bipartite networks.
  • Conclusion: Our study found key dysregulated genes involved in CRC. There are many important target genes in the results that may be critical in CRC progression.
  • Keywords: CRC, Gene co-expression network, systems biology,lncRNAs