ArticleBioMed research international2019
Hub Genes and Key Pathway Identification in Colorectal Cancer Based on Bioinformatic Analysis.
Article in BioMed research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 32 citations in OpenAlex.
- Phytochemical study ofPharmaceutical biology · 2026Article
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- Decoding Colorectal Cancer: Key Genes and Pathways in the Chinese Population Revealed.Current medicinal chemistry · 2026Article
- Decoding Molecular Interactions: Unraveling the Crosstalk between the Wnt Pathway and Key Signaling Networks by miRNA in Colorectal Cancer Progression.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- Effect of acute exercise on gene expression in peripheral blood mononuclear cells of puberty children.Scientific reports · 2024Article
- Advances in PGD2/PTGDR2 signaling pathway in tumors: A review.Biomolecules & biomedicine · 2024Review
- Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents.BMC medical genomics · 2023Article
- Identification and verification of microRNA signature and key genes in the development of osteosarcoma with lung metastasis.Medicine · 2022Article
- Identification of Key Gene Targets for Sensitizing Colorectal Cancer to Chemoradiation: an Integrative Network Analysis on Multiple Transcriptomics Data.Journal of gastrointestinal cancer · 2022Article
- Prostanoid Signaling in Cancers: Expression and Regulation Patterns of Enzymes and Receptors.Biology · 2022Article
- Low Expression of AGPAT5 Is Associated With Clinical Stage and Poor Prognosis in Colorectal Cancer and Contributes to Tumour Progression.Clinical Medicine Insights. Oncology · 2022Article
- A network-based pharmacological investigation to identify the mechanistic regulatory pathway of andrographolide against colorectal cancer.Frontiers in pharmacology · 2022Article
- Clinical Data and Biocalculation Methods of GABRD Determine the Clinical Characteristics and Immune Relevance of Colorectal Cancer.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Whole-exome sequencing of rectal cancer identifies locally recurrent mutations in the Wnt pathway.Aging · 2021Article
- Comprehensive analysis of the prognosis and immune infiltration for CXC chemokines in colorectal cancer.Aging · 2021Article
- Integration of gene expression data identifies key genes and pathways in colorectal cancer.Medical oncology (Northwood, London, England) · 2021Article
- Article
- Weighted Gene Co-expression Network Analysis Identified a Novel Thirteen-Gene Signature Associated With Progression, Prognosis, and Immune Microenvironment of Colon Adenocarcinoma Patients.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) is one of the most common malignant tumors. The aim of the present study was to identify key genes and pathways to improve the understanding of the mechanism of CRC. GSE87211, including 203 CRC samples and 160 control samples, was screened to identify differentially expressed genes (DEGs). In total, 853 DEGs were obtained, including 363 upregulated genes and 490 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs were performed to obtain enrichment datasets. GO analysis showed that DEGs were significantly enriched in the extracellular region, cell-cell signaling, hormone activity, and cytokine activity. KEGG pathway analysis revealed that the DEGs were mainly enriched in the cytokine-cytokine receptor interaction, drug metabolism, androgen and estrogen metabolism, and neuroactive ligand-receptor interaction. The Protein-Protein Interaction (PPI) network of DEGs was constructed by using Search Tool for the Retrieval of Interacting Genes (STRING). The app MCODE plugged in Cytoscape was used to explore the key modules involved in disease development. 43 key genes involved in the top two modules were identified. Six hub genes (CXCL
Indexed as
Identifiers
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