ArticleOncoTargets and therapy2015
Gene expression profile analysis of colorectal cancer to investigate potential mechanisms using bioinformatics.
Article in OncoTargets and therapy, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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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Who cites it
11 citing papers in PubMed, 24 citations in OpenAlex.
- High expression of PPP1CC promotes NHEJ-mediated DNA repair leading to radioresistance and poor prognosis in nasopharyngeal carcinoma.Cell death and differentiation · 2024Article
- Oral vitamin D supplementation induces transcriptomic changes in rectal mucosa that are linked to anti-tumour effects.BMC medicine · 2021Article
- The Association between Periodontitis and Human Colorectal Cancer: Genetic and Pathogenic Linkage.Life (Basel, Switzerland) · 2020Article
- Analysis of potential genes and pathways associated with the colorectal normal mucosa-adenoma-carcinoma sequence.Cancer medicine · 2018Article
- Identification of new progestogen-associated networks in mammalian ovulation using bioinformatics.BMC systems biology · 2018Article
- Gene expression profile analysis of the progression of carotid atherosclerotic plaques.Molecular medicine reports · 2018Article
- Gene expression profile analysis of the bone microenvironment in patients with spinal metastases.Oncology letters · 2018Article
- Identification of miRNA-mRNA Network Associated with Acute Myeloid Leukemia Survival.Medical science monitor : international medical journal of experimental and clinical research · 2017Article
- Identification of Key Candidate Genes and Pathways in Colorectal Cancer by Integrated Bioinformatical Analysis.International journal of molecular sciences · 2017Article
- Analysis of the interplay between methylation and expression reveals its potential role in cancer aetiology.Functional & integrative genomics · 2017Article
- Diametrical diseases reflect evolutionary-genetic tradeoffs: Evidence from psychiatry, neurology, rheumatology, oncology and immunology.Evolution, medicine, and public health · 2015Review
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to explore the underlying molecular mechanisms of colorectal cancer (CRC) using bioinformatics analysis. Using GSE4107 datasets downloaded from the Gene Expression Omnibus, the differentially expressed genes (DEGs) were screened by comparing the RNA expression from the colonic mucosa between 12 CRC patients and ten healthy controls using a paired t-test. The Gene Ontology (GO) functional and pathway enrichment analyses of DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) software followed by the construction of a protein-protein interaction (PPI) network. In addition, hub gene identification and GO functional and pathway enrichment analyses of the modules were performed. A total of 612 up- and 639 downregulated genes were identified. The upregulated DEGs were mainly involved in the regulation of cell growth, migration, and the MAPK signaling pathway. The downregulated DEGs were significantly associated with oxidative phosphorylation, Alzheimer's disease, and Parkinson's disease. Moreover, FOS, FN1, PPP1CC, and CYP2B6 were selected as hub genes in the PPI networks. Two modules (up-A and up-B) in the upregulated PPI network and three modules (d-A, d-B, and d-C) in the downregulated PPI were identified with the threshold of Molecular Complex Detection (MCODE) Molecular Complex Detection (MCODE) score ≥4 and nodes ≥6. The genes in module up-A were significantly enriched in neuroactive ligand-receptor interactions and the calcium signaling pathway. The genes in module d-A were enriched in four pathways, including oxidative phosphorylation and Parkinson's disease. DEGs, such as FOS, FN1, PPP1CC, and CYP2B6, may be used as potential targets for CRC diagnosis and treatment.
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