TY - JOUR
T1 - Meta-analysis of cancer transcriptomes
T2 - A new approach to uncover molecular pathological events in different cancer tissues
AU - Iqbal, Sundus
AU - Ejaz, Hira
AU - Nawaz, Muhammad Sulaman
AU - Loor, Juan J.
AU - Naeem, Aisha
PY - 2014/3/1
Y1 - 2014/3/1
N2 - To explore secrets of metastatic cancers, individual expression of true sets of respective genes must spread across the tissue. In this study, meta-analysis for transcriptional profiles of oncogenes was carried out to hunt critical genes or networks helping in metastasizing cancers. For this, transcriptomic analysis of different cancerous tissues causing leukemia, lung, liver, spleen, colorectal, colon, breast, bladder, and kidney cancers was performed by extracting microarray expression data from online resource; Gene Expression Omnibus. A newly developed bioinformatics technique; Dynamic Impact Approach (DIA) was applied for enrichment analysis of transcriptional profiles using Database for Annotation Visualization and Integrated Discovery (DAVID). Furthermore, oPOSSUM (v. 2.0) and Cytoscape (v. 2.8.2) were used for in-depth analysis of transcription factors and regulatory gene networks respectively. DAVID analysis uncovered the most significantly enriched pathways in molecular functions that were 'Ubiquitin thiolesterase activity' up regulated in blood, breast, bladder, colorectal, lung, spleen, prostrate cancer. 'Transforming growth factor beta receptor activity' was inhibited in all cancers except leukemia, colon and liver cancer. oPOSSUM further revealed highly over-represented Transcription Factors (TFs); Broad-complex_3, Broad-complex_4, and Foxd3 except for leukemia and bladder cancer. From these findings, it is possible to target genes and networks, play a crucial role in the development of cancer. In the future, these transcription factors can serve as potential candidates for the therapeutic drug targets which can impede the deadly spread.
AB - To explore secrets of metastatic cancers, individual expression of true sets of respective genes must spread across the tissue. In this study, meta-analysis for transcriptional profiles of oncogenes was carried out to hunt critical genes or networks helping in metastasizing cancers. For this, transcriptomic analysis of different cancerous tissues causing leukemia, lung, liver, spleen, colorectal, colon, breast, bladder, and kidney cancers was performed by extracting microarray expression data from online resource; Gene Expression Omnibus. A newly developed bioinformatics technique; Dynamic Impact Approach (DIA) was applied for enrichment analysis of transcriptional profiles using Database for Annotation Visualization and Integrated Discovery (DAVID). Furthermore, oPOSSUM (v. 2.0) and Cytoscape (v. 2.8.2) were used for in-depth analysis of transcription factors and regulatory gene networks respectively. DAVID analysis uncovered the most significantly enriched pathways in molecular functions that were 'Ubiquitin thiolesterase activity' up regulated in blood, breast, bladder, colorectal, lung, spleen, prostrate cancer. 'Transforming growth factor beta receptor activity' was inhibited in all cancers except leukemia, colon and liver cancer. oPOSSUM further revealed highly over-represented Transcription Factors (TFs); Broad-complex_3, Broad-complex_4, and Foxd3 except for leukemia and bladder cancer. From these findings, it is possible to target genes and networks, play a crucial role in the development of cancer. In the future, these transcription factors can serve as potential candidates for the therapeutic drug targets which can impede the deadly spread.
KW - DAVID
KW - Metastatic cancer
KW - Transcription factor
KW - Transcriptional analysis
KW - oPOSSUM
UR - http://www.scopus.com/inward/record.url?scp=85028683169&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028683169&partnerID=8YFLogxK
U2 - 10.0000/issn-2220-8879-networkbiology-2014-v4-0001
DO - 10.0000/issn-2220-8879-networkbiology-2014-v4-0001
M3 - Article
AN - SCOPUS:85028683169
SN - 2220-8879
VL - 4
SP - 1
EP - 20
JO - Network Biology
JF - Network Biology
IS - 1
ER -