ArticleOncology letters2019
Identification of key candidate genes for colorectal cancer by bioinformatics analysis.
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents.BMC medical genomics · 2023Article
- dRNASb: a systems biology approach to decipher dynamics of host-pathogen interactions using temporal dual RNA-seq data.Microbial genomics · 2022Article
- ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network.Hereditas · 2022Article
- Smell Detection Agent Optimisation Framework and Systems Biology Approach to Detect Dys-Regulated Subnetwork in Cancer Data.Biomolecules · 2021Article
- Identification of Hub Genes in Different Stages of Colorectal Cancer through an Integrated Bioinformatics Approach.International journal of environmental research and public health · 2021Article
- PAICS, a Purine Nucleotide Metabolic Enzyme, is Involved in Tumor Growth and the Metastasis of Colorectal Cancer.Cancers · 2020Article
- Colon Cancer Progression Is Reflected to Monotonic Differentiation in Gene Expression and Pathway Deregulation Facilitating Stage-specific Drug Repurposing.Cancer genomics & proteomicsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) is one of the most common cancers of the digestive tract. Although numerous studies have been conducted to elucidate the cause of CRC, the exact mechanism of CRC development remains to be determined. To identify candidate genes that may be involved in CRC development and progression, the microarray datasets GSE41657, GSE77953 and GSE113513 were downloaded from the Gene Expression Omnibus database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used for functional enrichment analysis of differentially expressed genes (DEGs). A protein-protein interaction network was constructed, and the hub genes were subjected to module analysis and identification using Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape. A total of 142 DEGs were identified, with enriched functions and pathways in the 'cell cycle', 'cell proliferation', 'the mitotic cell cycle' and 'one-carbon metabolic process'. In addition, 10 hub genes were identified, and functional analysis indicated that these genes are mainly enriched in 'cell division', 'cell cycle' and functions associated with nucleotide binding processes. Survival analysis demonstrated that DNA topoisomerase II α, cyclin-dependent kinase 1 and CDC28 protein kinase regulatory subunit 2 may be involved in cancer invasion or recurrence. The DEGs identified in the present study may help explain the molecular mechanisms of CRC development and progression.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.