ArticleMolecular medicine reports2015
Functional and protein‑protein interaction network analysis of colorectal cancer induced by ulcerative colitis.
Article in Molecular medicine reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- Expression of microRNA-99a-3p in Prostate Cancer Based on Bioinformatics Data and Meta-Analysis of a Literature Review of 965 Cases.Medical science monitor : international medical journal of experimental and clinical research · 2018Pooled it
- A network approach to elucidate and prioritize microbial dark matter in microbial communities.The ISME journal · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) is a well‑recognized complication of ulcerative colitis (UC), and patients with UC have a higher incidence of CRC, compared with the general population. However, the properties of CRC induced by UC have not been clarified using an interaction network to analyze and compare gene sets. In the present study, six microarray datasets of CRC and UC were extracted from the Array Express database, and gene signatures were identified using the genome‑wide relative significance (GWRS) method. Functional analysis was performed based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Prediction of the genes and microRNA were performed using a hypergeometric method. A protein‑protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/proteins, and clusters were obtained through the Molecular Complex Detection algorithm. Topological centrality and a novel analyzing method, based on the rank value of GWGS, were used to characterize the biological importance of the clusters. A total of 217 differentially expressed (DE) genes of CRC were identified, 341 DE genes were identified in UC, and 62 common genes existed in the two. Several KEGG pathways were the same in CRC and UC. Collagenase, progesterone, heparin, urokinase, nadh and adenosine drugs demonstrated potential for use in treatment of CRC and UC. In the PPI network of CRC, 210 nodes and 752 edges were observed, wheras 314 nodes and 882 edges were identified in UC. Cluster 3 in UC had the highest GWGS, while the topological centrality of Cluster 3 in UC had the lowest degree and betweenness. PPI network analysis provided an effective way to estimate and understand the likelihood of the potential connections between proteins/genes. The results obtained following the use of GWGS to analyze differences between clusters did not agree with the topological degree and betweenness centrality, which indicated that gene fold change based GWGS was controversial with degree here in CRC and UC.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.