Computational Identification of Key Regulators in Two Different Colorectal Cancer Cell Lines.
Transcription factors (TFs) are gene regulatory proteins that are essential for an effective regulation of the transcriptional machinery. Today, it is known that their expression plays an important role in several types of cancer. Computational identification of key players in specific cancer cell lines is still an open challenge in cancer research. In this study, we present a systematic approach which combines colorectal cancer (CRC) cell lines, namely 1638N-T1 and CMT-93, and well-established computational methods in order to compare these cell lines on the level of transcriptional regulation as well as on a pathway level, i.e., the cancer cell-intrinsic pathway repertoire. For this purpose, we firstly applied the Trinity platform to detect signature genes, and then applied analyses of the geneXplain platform to these for detection of upstream transcriptional regulators and their regulatory networks. We created a CRC-specific position weight matrix (PWM) library based on the TRANSFAC database (release 2014.1) to minimize the rate of false predictions in the promoter analyses. Using our proposed workflow, we specifically focused on revealing the similarities and differences in transcriptional regulation between the two CRC cell lines, and report a number of well-known, cancer-associated TFs with significantly enriched binding sites in the promoter regions of the signature genes. We show that, although the signature genes of both cell lines show no overlap, they may still be regulated by common TFs in CRC. Based on our findings, we suggest that canonical Wnt signaling is activated in 1638N-T1, but inhibited in CMT-93 through cross-talks of Wnt signaling with the VDR signaling pathway and/or LXR-related pathways. Furthermore, our findings provide indication of several master regulators being present such as MLK3 and Mapk1 (ERK2) which might be important in cell proliferation, migration, and invasion of 1638N-T1 and CMT-93, respectively. Taken together, we provide new insights into the invasive potential of these cell lines, which can be used for development of effective cancer therapy.
Institute of Bioinformatics, University Medical Center Göttingen Göttingen, Germany.
Darius
D
Wlochowitz
Institute of Bioinformatics, University Medical Center Göttingen Göttingen, Germany.
Martin
M
Haubrock
Department of Hematology/Medical Oncology, University Medical Center Göttingen Göttingen, Germany.
Jetcy
J
Arackal
Department of Hematology/Medical Oncology, University Medical Center Göttingen Göttingen, Germany.
Annalen
A
Bleckmann
Department of Medical Statistics, University Medical Center Göttingen Göttingen, Germany.
Alexander
A
Wolff
Department of Medical Statistics, University Medical Center Göttingen Göttingen, Germany.
Tim
T
Beißbarth
Institute of Bioinformatics, University Medical Center Göttingen Göttingen, Germany.
Edgar
E
Wingender
Institute of Bioinformatics, University Medical Center Göttingen Göttingen, Germany.
Mehmet
M
Gültas
Wnt pathway; colorectal cancer; master regulator analysis; pathway analysis; promoter analysis