Fahimeh Fattahi 1 , Jafar Kiani 1 , Mohsen Khosravi 2 , Somayeh Vafaei 1 , Asghar Mohammadi 3 , Zahra Madjd 1,4 , Mohammad Najafi 5 . Show Affiliations »
Abstract
AIM AND OBJECTIVE: It is interesting to find the gene signatures of cancer stages based on the omics data. The aim of study was to evaluate and to enrich the array data using gene ontology and ncRNA databases in colorectal cancer. METHODS: The human colorectal cancer data were obtained from the GEO databank. The downregulated and up-regulated genes were identified after scoring, weighing and merging of the gene data. The clusters with high-score edges were determined from gene networks. The miRNAs related to the gene clusters were identified and enriched. Furthermore, the long non-coding RNA (lncRNA) networks were predicted with a central core for miRNAs. RESULTS: Based on cluster enrichment, genes related to peptide receptor activity (1.26E-08), LBD domain binding (3.71E-07), rRNA processing (2.61E-34), chemokine (4.58E-19), peptide receptor (1.16E-19) and ECM organization (3.82E-16) were found. Furthermore, the clusters related to the non-coding RNAs, including hsa-miR-27b-5p, hsa-miR-155-5p, hsa-miR-125b-5p, hsa-miR-21-5p, hsa-miR-30e-5p, hsa-miR-588, hsa-miR-29-3p, LINC01234, LINC01029, LINC00917, LINC00668 and CASC11 were found. CONCLUSION: The comprehensive bioinformatics analyses provided the gene networks related to some non-coding RNAs that might help in understanding the molecular mechanisms in CRC. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
AIM AND OBJECTIVE: It is interesting to find the gene signatures of cancer stages based on the omics data. The aim of study was to evaluate and to enrich the array data using gene ontology and ncRNA databases in colorectal cancer . METHODS: The human colorectal cancer data were obtained from the GEO databank. The downregulated and up -regulated genes were identified after scoring, weighing and merging of the gene data. The clusters with high-score edges were determined from gene networks. The miRNAs related to the gene clusters were identified and enriched. Furthermore, the long non-coding RNA (lncRNA) networks were predicted with a central core for miRNAs. RESULTS: Based on cluster enrichment, genes related to peptide receptor activity (1.26E-08), LBD domain binding (3.71E-07), rRNA processing (2.61E-34), chemokine (4.58E-19), peptide receptor (1.16E-19) and ECM organization (3.82E-16) were found. Furthermore, the clusters related to the non-coding RNAs, including hsa-miR-27b -5p, hsa-miR-155 -5p, hsa-miR-125b-5p, hsa-miR-21 -5p, hsa-miR-30e-5p, hsa-miR-588 , hsa-miR-29-3p, LINC01234 , LINC01029 , LINC00917 , LINC00668 and CASC11 were found. CONCLUSION: The comprehensive bioinformatics analyses provided the gene networks related to some non-coding RNAs that might help in understanding the molecular mechanisms in CRC. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
Entities: Disease
Gene
Species
Keywords:
Network; gene ontology; lncRNA; miRNA; omics data; online gene expression omnibus.
Year: 2019
PMID: 31654507 DOI: 10.2174/1386207321666191010114149
Source DB: PubMed Journal: Comb Chem High Throughput Screen ISSN: 1386-2073 Impact factor: 1.339